Download A PSYCHOGRAPHIC SEGMENTATION ANALYSIS OF

A PSYCHOGRAPHIC SEGMENTATION ANALYSIS OF
PRESCRIPTION DRUG USERS
A Thesis
Presented to
The Faculty of Graduate Studies
of
The University of Guelph
by
JULIE C . HORNE
In partial fulfilment of requirements
for the degree of
Master of Science
September, 1997
O Julie C. Home, 1997
National Library
Bibliothéque nationale
du Canada
Acquisitions and
Bibliographie Services
Acquisitions et
services bibliographiques
395 Wellington Street
OttawaON K I A O N 4
Canada
395, rue Wellington
OttawaON K1A ON4
Canada
The author has granted a nonexclusive licence allowing the
National Librxy of Canada to
reproduce, Loan, distribute or selI
copies of this thesis in microform,
paper or electronic formats.
L'auteur a accordé une licence non
exclusive permettant à la
Bibliothèque nationale du Canada de
reproduire, prêter, distribuer ou
vendre des copies de cette thèse sous
la fome de microfiche/film, de
reproduction sur papier ou sur format
électronique.
The author retains ownership of the
copyright in this thesis. Neither the
thesis nor substantial extracts fiom it
may be printed or otherwise
reproduced without the author's
permission.
L'auteur conserve la propriété du
droit d'auteur qui protège cette thèse.
Ni la thèse ni des extraits substantiels
de celle-ci ne doivent être imprimés
ou autrement reproduits sans son
autorisation.
ABSTRACT
A PSYCHOGRAPHIC SEGMENTATION ANALYSIS OF
PRESCRIPTlON DRUG USERS
Julie C. Home
University of Guelph, 1997
Advisor:
Dr. J. Liefeld
This thesis is an investigation of the information search activities of adult consumers of
prescription dmgs. It identified distinct groups of consumers based on different patterns of
attitudes regarding information search and prescription dnigs. Data were collected using a
self-administered mail questionnaire in a s w e y of 456 prescription h g users, who
indicated they had taken a prescription medication over the last 12 months. Eight
underlying attitudinal dimensions were derived through Principal Axis Factoring of 32
attitudinal variables and were narned: information involvement, information avoidance.
self-assured knowledge, self-care orientation, manufacturer-oriented information. doctororiented infonnation, pharmacist-onented information, and patient decision making. Using
cluster analysis, the prescription dnig users were classified into different types according to
the similarity of their scores on each of these eight attitudinal dimensions. The resulting
typology formed three groups, each having a diffeferentonentation to infonnation search and
prescription drugs. These were labelled the System Skeptics, the Confident Decision
Makers, and the Uninformed Followers. Significant differences were found across the
cluster groupings on a nurnber of behavioural. demographic and situational descriptors.
Many thanks go out to my advisor, Dr. John Liefeld, my cornmittee members. Dr.
KaRn Finlay and Dr. Dick Vosburgh. and my chairperson, Dr. Vinay Kanetkar for their
guidance, patience and persistence with me - even after leaving graduate school to pursue
full-tirne work in the field of market research.
I would also Like to thank Market Facts of Canada Ltd. and Hoffinann-La Roche
Ltd. for providing me with generous hnding for my research.
My heartfelt appreciation goes out to my friends and farnily who never ceased to
provide encouragement when it was need most. 1 would like to thank my father. Dr. John
Home. in pa-rticular. for always finding time in his busy schedule io listen. to send a flurry
of articles. e-mails or faxes,and for dways believing in me.
Finally, 1would like to single out a very special person in my life. Pien Steel. who
not only provided his love and encouragement, but laughter. a critical eye, statistical sawy.
and even. exceptional writing skills. I know this has b e n a difficulijoumey. but 1do
believe we have finally reached the end. Al! of my love goes out to you.
TABLE OF CONTENTS
I. INTRODUCTION
5
1.1 Problem Statement
1.2 Raîionaie
6
-
-
7
II. LITERATURE REVTEW
8
2.1 Theoretical Foundations
8
2.2 Review of Previous Information Search Literature
2.2.1 Factors Influencing Extemal Information Search
2.2.2 Sumrnary
2.2.3 Typologies of Extemal Information Search
2.2.4 Summary
2.2.5 Review of Studies Related to Information Search for Drugs
2.2.6 Summary
10
II
16
16
20
20
26
2 3 Measures of External Search Behaviour
26
2.4 Methodologies Used in the Information Search Literature
28
3.1 Research Purpose and Objectives
31
3.2 Research Design
31
3 3 Questionnaire Development
33
3.4 Statisticai Anaiysis
34
3.4.1 Factor Analysis
3.4.2 Cluster Analysis
3.4.3 Cross-Tabulations
3.4.4 Discriminant Analysis
3.4.5 Multinominal Logistic Regession
34
36
38
39
40
3 5 Reseanih Assumptions
41
3.6 Research Limitations
41
IV.RESULTS
43
4.1 Introduction
43
4 2 Sample
43
4.2.1 Response Rate
4.2.2 Sample Characteristics
43
44
4 3 Factor Analysis
45
4.4 Cluster Anaiysis
56
4.4.1 System S keptics
4.4.2 Confident Decision Makers
4.4.3 Uninfomed Followers
4.5 Cross Tabulations
62
62
63
64
...
111
4.6 Discriminant Analysis
4.6.1 Behavioural Measures as Predictors
4.6.2 Demographic Measures as Predictors
4.6.3 Situational Measures as Predictors
65
66
72
76
4.7 Logistic Regression
81
4.8 Muftinominal Logit
84
E DISCUSSION
5.1 Introduction
88
88
5.2 Attitudinal Dimensions
89
5.3 Psychographic Segments
5.3.1 Cluster # 1 - System Skeptics
91
-
5.3.2 Cluster #2 Confident Decision Makers
5.3-3 Cluster #3 - Uninformed Followers
5.4 Implications and Recommendations
5.5 Recommendations For Future Research
91
92
93
94
100
RFFERENCES
103
APPENDICES
108
APPENDIX 1: RESULTS FROM PRINCIPAL COMPONENTS ANALYSIS
108
APPENDIX II: RESULTS FROM PRINCIPAL AXIS FACTORING
114
APPENDIX III: RESULTS FROM HIERARCHICAL CLUSTER ANALYSIS
131
APPENDIX IV: RESULTS FROM NON-HIERARCHICAL CLUSTER ANALYSIS 142
APPENDIX V: RESULTS FROM DISCRIMINANT ANALYSIS
146
APPENDIX VI: RESULTS FROM LOGISTIC REGRESSION
168
APPENDIX VII: QUESTIONNAIRE
181
LIST OF TABLES
Table 1: Principal Component., Analysis Factor Solution ..............................................
48
Table 2: Suggested Names For Principal Components Analysis Factoring
Method Resulb ........................................................................................................... 49
Table 3: Principal Axis Factor Solution .8 Factor Solution............................................. 53
Table 4: Suggested Factor Names For Principal Axis Factoring Method Results ............54
Table 5: Reliability Coefficients for Composite Variables .............................................. 56
Table 6: Analysis of Agglomeration Coefficient for Hierarchical Cluster Analysis........ 57
Table 7: Mean Scores On Composite Attitudinal Scales For AI1 Prescription
Dmg Users .................................................................................................................. 57
Table 8: Results of Non-hierarchical Cluster Analysis with Initial Seed Points frorn
Hierarchical Results .................................................................................................. 59
Table 9: Mean Scores on Attitudinal Variables Used in Cluster Analysis....................... 60
Table 10: Ever Asked Doctor to Prescribe Specific Medication...................................... 64
Table 11: Ever Asked Phamacist For Genenc Substitution............................................ 65
Table 12: Canonical Discriminant Functions For Behavioural Variables........................ 66
Table 13: Canonical Discriminant Functions Evaluated At Group Means ...................... 67
Table 14: Results of Discriminant Function Analysis of Information Search Behaviour 68
Table 15: Group Means by Redictor Variables............................................................... 69
Table 16: Classification Results for Behavioural Predictors............................................ 70
Table 17: Classification Results for Cases Selected for Use in the Analysis ................... 71
Table 18: Classification Results for Cases Not Selected for Use in the Analysis ............ 71
Table 19: Classification Results for Demographic Predictors ......................................... 72
Table 20: Canonical Discriminant Fumions ................................................................... 73
Table 2 1: Canonical Discriminant Functions Evaluated At Group Means ...................... 73
Table 22: Results of Discriminant Function Analysis of Demographic Variables .......... 75
Table 23: Group Means by Demographic Predictor Variables ........................................ 76
Table 24: Canonical Discriminant Functions For Situational Variables .......................... 77
Table 25: Canonical Discriminant Functions Evaluated At Group Means ...................... 77
Table 26: Results of Discriminant Function Analysis of Health Characteristics ............. 79
Table 27: Group Means by Predictor Vanables ............................................................... 80
Table 28: Classification Results for Situational Predictors ..............................................80
Table 29: Classification Results for Cases Selected for Use in the Analysis ...................81
Table 30: Classification Results for Cases Not Selected for Use in the Analysis ............ 81
Table 3 1: Classification Matrix Using Demographic Indicators to Predict Group
Mem bership ............................................................................................................... 83
Table 32: Classification Mauix Using Demographic Indicators to Predict Group
Mem bership ............................................................................................................... 83
Table 33: Classification Matrix Using Dernographic Indicators to Predict Group
Membership...............................................................................................................84
Table 34: Predicted Percentages Based on Coefficients from LIMDEP Output..............87
I. INTRODUCTION
As part of the process of consumer decision-making, consumers generally
recognize a problern or unmet need and may undertake an information search. prior to
purchase, to resolve these problems (Stemthal & Craig, 1982). During the information
searching stage, both intemal and extemal search can take place. Interna1 searching
occun when information is accessed from an individual's long-term memory, whereas
externai seanih occurs when information is actively puaued from extemal sources
(Bettman, 1978). While prior studies have lacked a consistent definition of what
behaviours constitute extemal information search. such definitions as the nurnber of
informational sources from which information was sought the arnount and types of
information sought, the time allotted to searching for information, as well as the number
of brands for which information was sought, have been used to charactenze consumers'
extemal information seeking behaviour (Kiel & Layton. 1981). For the most part. the
literature on consumer information search has suggested that there are rather distinct
patterns of searching behaviour, but has largely focused on the information searching
behaviours of purchasers of durable goods (Newman & Staelin, 1972; Claxton. Fry &
Portis, 1974; Westbrook & Fomell, 1979; Kiel & Layton, 198 1 ; Furse, Punj & Stewart.
1984). This study, however, will extend the existing literature into the information search
behaviours for prescription dmgs by adult consumes.
Although the literature in the area of information search patterns for prescription
dmgs is limited, the few studies that have been conducted to date have tended to focus on
consumers' experiences with various health professionals and the information provided by
these professionals. Little emphasis has been given to the information sought out by
consumers themselves, and the underlying reasons why these consumers may, or may not,
search for drug information. Morris, Grossman. Barkdoll, & Gordon (1987), however.
examined the information search behaviours of recent purchasers of prescription drugs.
Four distinct groups of consumers were categorized according to their search behaviours:
physician reliant, pharmacist reliant, questioners and the uninfonned. Another study
examined the information search motives of elderly users of hypertension medication
(Moms, Tabak & Olins, 1992). Four segments of information seekers were identified as:
ambivalent learners, uncertain patients, ris k avoiders, and the assertively self-reliant.
1.1
Problem Statement
Understanding the search activities of prescription dnig consumers requires
examining how these consumers, if at dl, go about gathering information about their
prescription medications. Of particular interest is from where the information is obtained
(Le. physicians, pharmacists, interpersonal contacts with frienddrelatives. or extemal
resources such as reference books, magazines, TV etc.), and consumers' motives for
seeking out dmg information (e.g. self-care. desire to communicate better with physician,
inability to obtain enough information irom health professionals, etc.).
In addition to the absence of descriptions about consumen' information search
behaviours for dmg information, no Canadian published nor publicly-held studies have
been conducted on consumers' search activities for drug purchases. The few studies that
have been reported were done in the United States.
1.2
Rationale
At present, the Ministry of Health in Ontario, as well as other provincial
ministries, are undertaking reform of drug programs with the airn of "enabling consumers
to get al1 the information they need about their drugs and to undentand how to get that
information" (Ministry of Health, 1994. p. 20). Therefore, a study related to consumers'
search patterns for drug information appears to be needed in order to most effectively
target informational materials and educational prograrns to these consumers. In addition.
this study will potentially offer guidance to marketing practitioners in their efforts to se11
dnig products. Dmg information may be targeted and disseminated to consumen who are
receptive and who are actively involved in their prescription medication decisions. Also,
the identification of such information search patterns could potentially assist retailers (e.g.
chain drug stores, independent pharmacies) in providing better customer service. By
offenng drug information to receptive consumers, they may give themselves an advantage
over cornpetitors who lack such a service.
Finally, this study will contribute to the existing information search literature by
expanding beyond durable goods into consumers' search for information about
prescription dnigs. The information search activities of prescription drug users will be
analyzed to identify a typology of information search behaviours. This typology will be
profiled using behavioural, demographic. and situational descriptors in order to aid in the
identification and understanding of the groups.
II. LK'ERATURE REVIEW
2.1
Theoretical Foundations
As part of the conceptual models of consumer decision-making. information
search is conceptualized as a sub-cornponent of the decision process. Most decision
models postdate a sequence of steps that involve identifying a problem to be solved;
gathenng information about alternative courses of action; evaluating and weighing the
opaons; choosing an alternative; and taking action to solve the problem. The notion that
consumers may undertake an extemal search for information prior to a purchase decision
has been widely popularized by conceptual models such as those of Engel, Kollat and
Blackwell (1973) and Bettman (1979). A discussion of these two conceptual models will
be provided in order to contextualize the information search component within the overall
consumer decision-making process.
One of the most widely-used models for understanding consumer behaviour was
developed by Engel, Kollat and Blackwell(1973). This mode1 provides a general
representation of consumer behaviour, which encompasses the information search
component. In particular. this mode1 hypothesizes a five-stage decision process which
includes: problem recognition, search, alternative evaluation, choice and outcorne. In the
context of information acquisition. the EKB mode1 defines search as a "motivated
exposure to information with regard to a given alternative resulting when existing
information, beliefs and attitudes are found to be inadequate" (Engel. Kollat & Blackwell,
1982. p. 321). As stated by Engel, Kollat and Blackwell (1973), the factors that most
often motivate a consumer to undertake an external search for information occur when the
quantity and quality of existing information is insufficient. when information is not
readily recalled frorn rnemory, when perceived risk is high (price, social or physiological
risk). or when the consumer experiences uncertainty about their own ability to make a
correct choice.
While this mode1 is useful in identifying variables that inff uence the consumption
process and in providing a framework for examining anas of consumer behaviour, the
EKB model has not been ernpirically tested. Therefore. the EKB model is only useful for
suggesting relationships among variables, rather than for providing conclusive evidence
about these relationships (Horton, 1984).
In addition to the EKB (1973) model, one of the most comprehensive theones of
information search, based on an information processing perspective. is provided by
Bettman (1979). In Bettman's model, intemal and extemal search processes are outlined
and are further distinguished by the direction and degree of information search taken by
consumers. Direction of external search refers to the type and source of information
sought by consumers. while degree of extemal search refers to the amount of information
sought (Bettrnan, 1978). The factors influencing the direction of extemal information
search are noted by Bettman as: the current goals of the consumer (Le. what information
is useful for the choice at hand). prior experience/stored knowledge (i.e. what is already
known) and the structure of the environment (i.e. what is available in the extemal
environment). On the other hand, the influences upon the degree of search are outlined
as: the costhenefit of searching for information, choice environment factors (Le.
information availability, difficulty of the choice, time pressure). individuals differences.
and the level of conflict or uncertainty about the decision (Bettman. 1978).
The first section of the literature review covers the factors influencing the degree
of extemal infomation search undertaken by consumers. while the second section
reviews the studies that have developed typologies for understanding infomation search
patterns. The third section coven research related io the area of information search
behavioun for prrscnption drug information. The fourth. and fifth sections address the
measures that have been used in previous studies to represent extemal information search.
and the various methodologies that have been used in the examination of information
search.
2.2
Review of Previous Information Search Literature
For the most part, existing studies on search behaviour have tended to focus on
developing typologies of search strategies, or on the explanatory factors that influence
search behaviour. A problem in the literature, however, is in the diversity of the
determinants exarnined and in the analytical methods used throughout the research, which
makes it difficult to draw cornparisons among the studies. Some researchers have
examined predictoa of various kinds of search behaviour, while other approaches have
used cluster analysis to determine distinct pattems of search behaviour (Claxton. Fry &
Portis, 1974; Newman & Staelin, 1972; Westbrook & Fornell, 1979; Kiel & Layton,
1981; Furse, Punj & Stewart, 1984).
2.2.1
Factors influencing Extemal information Search
A number of determinants of external search have been examined in the
information search literature, which centre primarily on the search activities undertaken
for durable goods. In their review of the literature, Beatty & Smith (1987) outlined seven
categones of variables that affect search: market environment; situational variables,
potential payoff, knowledge and experience: individual differences; conflict and conflict
resolution; and cost of search. Approxirnately 60 variables have been studied empirically
as determinants of search (Beatty & Smith, 1987; S ~ i v a s a n& Ratchford. 1991).
Newman (1977). in his review of the literature, outlined a number of determinants of
search that have been examined. Previous purchase history, demographic background,
buyer personality. perceived risk, and perceived costs of search are the major categones
of deterrninants that have been shown to be related to external information search. Other
studies have examined a host of variables that are related to the tendency for consumers
to engage in search.
Using an involvement-based perspective, Beatty and Smith (1987) studied the
relationship between external search effort and a number of motivating antecedent
variables, such as ego involvement, product class knowledge, time availability. purchase
involvement and attitudes toward shopping. Extemal search effort, on the other hand.
was represented by a total search index based on four major dimensions of search: media
search, retailer search, interpersonal seamh and neutral sources. Using self-report
measures from recent purchasers of televisions, VCRs. and home cornputers, the results
indicated that greater retailer and media search were both associated with higher purchase
irivoivementlshopping attitudes, greater time availability and lower product class
knowledge. For the interpersonal search component (Le. seeking the opinions of others),
product class knowledge appeared to be the primary motivator. Purchase
involvement/shopping attitudes also had a significant influence on interpersonal search.
For neutral sources, time availability and ego involvement were the only two variables
associated with this fonn of extemal information search.
Newman and Staelin (1972), on the other hand, examined the amount of
information search undertaken by buyers of new cars and major household appliances.
Using a combined score of in-store and out-of-store activities into an overall search
index, Newman and Staelin (1972) found that the number of brands initially considered.
whether the same brand was bought as before. the identity of the household member
exercising the most influence on the decision. the consumer's ability to judge the product.
education. and stage in the life cycle to be significant predictors of search at the .O5 level.
Three others variables were significant at the .IO level, location of the household by city
size, the occupation of the head of household. and the number of times the product was
purchased in the last ten years.
Using self-reported rneasures. Duncan and Olshavsky ( 1982) studied the
relationship between consumer beliefs about the marketplace (e.g. "1 need to look at al1
the available choices if I am to tell which is the best one") and the pre-purchase extemal
search behaviours of recent purchasers of colour televisions. Similar to the measures
used by Newman & Staelin (1972). an index of in-store activities such as the number of
stores visited and number of brands examined, and out-store activities such as the number
of times infomation was sought from personal sources. unsponsored sources and
marketing dominated sources were used to charactenze extemal search. The results of
the study indicated that beliefs about the marketplace and an individual's view about their
capabilities as consumers accounted for 50 percent of the variance in extent of extemal
search. Duncan and Olshavsky (1982) concluded that the large percentage of variance
explained by the beliefs suggest that they are an important determinant of external prepurchase information seeking.
Using a behavioural process methodology. Moore and Lehmann (1980) conducted
an information acquisition experiment on the purchase of bread, a low-involvement good.
Moore and Lehmann ( 1980) classified the deterrninants of external search into six
categories: market environment, situational variables. potential payoff/product
importance, knowledge and experience. individual differences and conflict and conflictresolution strategies. Extemal search was charactenzed in terms of total number of
acquisitions, number of brands searched, and the number of amibutes searched.
Regression analysis showed that only time pressure (situational), knowledge/expenence
and ability (individual) were significantly related to total search.
Swan (1969) studied the relationship between consumer learning and the extent of
information search using a senes of choice situations involving hypothetical brands of
shirts. He proposed that infomation seeking would decline with repeated choice of the
same brand, and with the choice of a satisfactory rather than an optimal choice. The
results frorn Swan's study indicate that expenence with the brand and a satisfactory
choice as the decision objective leads to lower information seeking behaviour.
Schaninger and Sciglimpaglia ( 198 1) examined the relationship between cognitive
peaonality traits and demographics on depth of search (the number of cues drawn and
number of dimensions exarnined) using a behavioural process methodology. Six traits
were used to represent individual differences in information processing: tolerance for
ambiguity. rigidity, cognitive style, need for cognitive clarity, self-esteem and trait
anxiety. Age, family life cycle, and socio-economic factors were used as demographic
variables. The findings suggest that individuals who were younger. earlier in the farnily
life cycle. educated, and of higher social class examined more cues and alternatives under
the hypothetical buying decisions. Furthemore. those individuals who were tolerant of
ambiguity, had clarifier cognitive styles (i.e. seek additional information to clarify
understanding), and had higher self-esteem exarnined more cues and alternatives in the
hypothetical buying decisions.
Bnicks (1985) studied the effects of prior knowledge about a product class on pre-
purchase information search. Brucks (1985) concluded that knowledgeability
encornpassed both objective and subjective dimensions. Objective knowledge referred to
what an individuai actually knows. while subjective knowledge referred to an individual's
degree of confidence in hisher knowledge. A number of studies, however, have found a
negative relationship between amount of product experience and arnount of extemal
search (Moore and Lehmann, 1980; Newman and Staelin, 1972; Swan. 1969). A
common explanation for the negative relationship between knowledge and search is that
expenenced consumers have existing knowledge about the various attributes of various
alternatives, and consequently do not need to acquire such information from external
sources. However, another cornmonly held explanation asserts that expenenced
consumes perform more efficient searches for information because pnor knowledge may
aliow the individual to distinguish, formulate and evduate information more efficiently.
A number of studies have postulated that prior knowledge encourages information search
by making it easier to process information (Punj & Staelin, 1983). Although the results
are ofien contradictory. the findings from Brucks' study indicate that pnor knowledge
facilitates the acquisition of new information.
A study by Urbany, Dickson and Wilkie (1989) examined the relationship
between consumer uncertainty and the extent of infonnation search behaviour using
purchasers of major household appliances. Extemal search was charactenzed according
to measures of the amount of time spent shopping, the number of different stores shopped
at, the number of brands considered, and the use and reactions to various sources of
information. Urbany et al. classified uncertainty into two dimensions: knowledge
uncertainty (i.e. what is known about the alternatives) and choice uncertainty (Le. which
alternative to choose). From the results, it was found that choice uncertainty tended to
increase search, whereas knowledge uncertainty had a negative effect on search. It was
hypothesized that greater knowledge uncertainty was associated with a reduced ability to
eficiently use new information making infonnation search a more difficult process. For
choice uncertainty, it was argued that this type of uncertainty produced conflict and.
subsequentiy, the motivation to resolve that conflict. Therefore, more extensive search
was undertaken by those experiencing choice uncertainty than those experiencing
knowledge uncertain ty.
While it is usehl to examine the explanatory variables that are related to
consumers' information search activities, these results are limiting as they tend to
examine a few determinants of search behaviour against a diversity of measures used to
characterize extemal search. Measures such as the counts of retail shopping activity,
sources and types of information used, alternatives considered, time spent in the purchase
decision process. and a total search index have al1 been used to rcpresent external search
(Newman, 1977). Therefore, a lack of consistency in the definitions of search makes it
difficult to draw conclusions about which variables affect the external search undertaken
by consumers. Furthemore, the variables found to be significantly related to search in
bivanate tables frequently fade into insignificance when multivariate techniques are used,
and the amount of variance explained by any one variable has typically been small. In his
review of the literature, Newman (1977) stated,
"The relationship between arnount of search and any one determinant seldom has
been drarnatic. Differences in group means typically are small and the diversity of
behaviour within groupings of buyers is the rule, reflecting the complexity of
influences on behaviour". (p.32)
2.2.3 Typologies of Extemal Information Search
While researchers have typically sou& to detemine the amount of search
conducted by consumen, as well as to identify the major influences on search. other
studies in the area of information search have sought to develop distinct segments or
clusters of consumers based on their information search patterns. Early efforts to identify
distinct groups of consumers characterized by differing information search patterns used
unidimensional measures of aggregate search activity. These aggregate measures.
however, have been cnticized for failing to adequately define differences in patterns of
information gathering. Recent studies have distinguished information search patterns
according to the type of sources used, such as retailer, media and interpenonal search.
while others have distinguished search patterns according to information. brand and time
dimensions.
Using canonical correlation analysis, Westbrook and Fomell ( 1972) studied the
degree of information seeking by consumers of new cars and major household appliances.
Measures of retail, neutral and personal sources were used as criterion vanables and six
explanatory measures as predictor variables (age. education. satisfaction. working order
of previously purchased product, joint decision making and number of brand
alternatives). The first canonical variate was interpreted as the extent of physical
shopping search, and was associated with the number of brand alternatives considered.
higher education. and the absence of an urgent need to replace the product. On the other
hand, the second canonical variate was interpreted as the tendency to seek out objective
(neutral) versus subjective (personal) pre-purchase information, and was negatively
associated with buyer age and education. Following this, four segments of consumers
were denved according to the types and intensity of usage of different information
sources during their recent purchase expenence: "objective shoppers" (reliance on neutral
sources to the exclusion of persona1 sources), "store intense shoppers" (high degree of
retail search and greater consultation with penonal sources. " personal advice seekers"
(visit few retail outlets and rely on friends, neighbours and relatives) and "moderate
shoppers" (visit the fewest retail outlets and do not rely on neutral or personal sources).
Kiel and Layton (198 1). on the other hand, conducted a study of the behaviours
and correlates of information search activities for new automobile buyers. Measures such
as the number of dealers visited. the number of ads recalled, the amount of time spent
searching for information, and the number of phone calls made were used as measures of
search behaviour. Using principal factor analysis. four factors emerged as the dimensions
representing various search behaviours: retail search. media search, interpersonal search
and a time dimension. To develop a taxonomy, cluster analysis produced three major
search groups: high information seekers, low information seekers, and selective
information seckers. Using rank order correlation and t-tests, the examination of the
correlates of search behaviour siiggested that only certain predictors of search behaviour
were related to the search dimensions. Relationships emerging from this research suggest
that continuing exposure to product information via low-involvement learning leads to
greater information search dunng the decision period; consumen with the Ieast selfconfidence in decision-making undertake the greatest search activity; and search activity
tends to decrease with age.
A snidy conducted by Furse, Punj and Stewart (1984) identified distinctive
extemal information search patterns among purchasers of new automobiles. Extemal
information search behaviour was measured according to: the number of dealers visited,
the activities at the dealerships visited. and out-of-store information search behaviour
(talking to friends, reading magazines). Other variables included the number of previous
cars owned, satisfaction with the car purchased, and selected demographic information.
Using principal cornponents analysis, five factors emerged as the dimensions representing
extemal search behaviour: retailer visits, out-of-store search. interpersonal search, instore search, and the involvement of others. Using the factor scores as the basis for the
cluster analysis, six clusters were denved: a low-search group, a purchase-pal assisted
group. a high search group, a high self-search group, a retailer shopper group and a
moderate search group.
Claxton, Fry and Portis (1974) classified consumers of fumiture and major
appliances in ternis of their information gathenng activities. According to Claxton et al.,
three classes of variables were viewed as potential causes of search pattern differences individual, situational and product characteristics. Similar to other studies researching
extemal search, information search was measured by: the type and range of alternatives
considered, information sources used, features considered, stores visited and time spent
considering the purchase (Newman & Staelin, 1972). From the resulü, three mesures
were found to generate distinctive clusten: number of information sources used, total
visits to stores and deliberation time. The three clusters obtained in both the furniture and
appliance samples were labelled thorough (store intense), thorough (balanced) and nonthorough. income, education and concem with selecting the right product were positively
associated with thoroughness, whereas situational characteristics such as immediacy of
need and financial constraints were negatively and positively nlated to search.
2.2.4 Summary
By developing typologies of information seanih patterns, different groups of
consumers can be identified according to the search behaviours exhibited by them. A
shortcoming in this research, however, is that the studies conducted have focused
pnmarily on the information search behaviours in which consumers engage in. without
examining the underlying reasons why these consumers seek out information fiom
external sources. Furthemore, the information search patterns that have emerged have
tended to examine only the search behaviours for purchasers of durable goods.
Information search patterns for other product classes have received considerably less
attention in the research.
2.2.5
Review of Studies Related to Information Search for Dmgs
Although a substantial amount of research has been conducted on the prepurchase search activities employed by consumers of durable goods. little ernpincal
research has been conducted on the information sought by consumers in decisions
involving their health. While engaging in information search is acknowledged to be one
mechanism by which consumers cope cognitively with uncertainiy surrounding healthrelated events. then is evidence to suggest that consumers ofien perceive that they are
unsuccessful in obtaining the necessary information, particularly from health
professionals. despite their desire to acquire health-related information (Lenz, 1984).
Thus, greater attention needs to be given to attitudinal dimensions that may inhibit or
facilitate consumers to engage in a search for information about their drugs.
A study conducted by Morris. Grossman, Barkdoll and Gordon ( 1987) examined
the source and nature of drug information received and sought by consumers in
conjunction with a prescription obtained within the previous four weeks. Experiences at
the pharmacy, such as what was said about the medication (i-e. how much medication to
take or use; how ~ f t e nto take or use the medicine; whether or not the medication could
be refilled; any precautions to take while using the medication; as well as any possible
side effects). whether this information was provided by the phamacist or if the consumer
had to ask about it. as well as the printed or audio-visual materials available were used to
represent the sources and nature of dmg information obtained at the pharmacy. Similar
measures were used to represent the consumer's experiences at the physician's office. with
the inclusion of a measure to indicate the various individuals who may have provided
information to the consumer (i.e. doctor. nurse. secretary or technician). In addition to
the experiences at the pharmacy and at the doctor's office, consumers were asked to
indicate which self-initiated information search activities they engaged in. such as reading
reference books, magazines. newspapen, or taiking to fnends or family. As an attitudinal
component, consumers were also solicited about their attitudes towards various barriers
(e.g. "It is difficult to get information because the doctor uses technical terrns) or
facilitators (e.g. "It would be easier to ask questions if you thought of some questions and
wrote them down before your appointment) in obtaining information from the doctor and
phamacist.
Factor analytic and clustering techniques were used to segment consumers
according to the sources and nature of drug information obtained by thcm. Using
principal components analysis, four factors represented the sources of information, and
were interPretable': 1) non-health professional sources, 2) spontaneous counselling
activities of physicians, 3) spontaneous counselling activities of pharmacists and. 4)
question asking activities directed to both the doctor and pharmacist Factors scores were
used as input into the cluster andysis, and the four clusters retained were: physician
reliant, pharmacist reliant, questioners and the uninfomed2. In order to provide a
descriptive portrayal of who these consumers were, the four groups were compared for
demographic. situational and attitudinal differences. The uninformed segment were the
least likely to actively pursue information. were likely to perceive several informationseeking barriers. and were most apt to see trust in the physician as negating the need to
obtain information. The physician-reliant group, on the other hand. were apt to be
passive recipienü of physician-supplied information. who responded favourably to
counselling, perceived few baniers to obtaining infornation from the physician, and
reported self-initiated search made them feel better. The questioners were most likely to
consult reference books and magazines and perceived multiple barriers to obtaining
information from health professionals; whereas the pharmacist-reliant group were the
least likely to obtain information from references books or magazines. The pharmacist-
I
e"
Arnount of variance explained by each factor was not reported.
structure was viewed to be stable as the sarnple was randomly split and the
four-cluster soiution was similar for each of the haIves.
reliant group, however. were the most responsive to both counselling from the doctor and
the pharmacist, but tended to receive counselling at the pharmacy to a greater extent
While this study provides a description of the information seeking behaviours of
consumen of drugs based on the activities at the pharmacy. physician's office and at a
self-initiated level, Moms et al. (1987) suggested that future studies could potentially
include lifestyle. health beliefs, and psychographic variables in order to provide a more
thorough description of the clusten.
Another study conducted by Moms, Tabak & Olins (1992) studied the
infonnation seeking motivations of elderly users of hypertension medication.
Information conceming health beliefs, motivations and preferences for infomationseeking and active participation in healthcare were addressed in the research, along with
perceptions of knowledge, and demographic information. Using exploratory factor
analysis, six factors best described the rnoûvational bases for information searsh. These
were 1) information involvement. 2) self-care, 3) regimen barriers, 4) information
avoidance, 5) risk aversion, and 6) question-asking. The factor scores served as input into
the cluster analysis. The results indicated four distinct segments of infonnation seekers:
ambivalent leamen, who viewed themselves as vulnerable to negative health infonnation.
not particularly healthy, but receptive to drug information; uncertain patients, who
perceived dificulty adhenng to the drug regimen and reported low levels of information
receipt from health professionals; nsk avoiden, who viewed infmnation-seeking as a
risk-coping strategy; and the assertively self-reliant. who were the least receptive to
information about their medication.
While distinct market segments have been identified by Moms et al. (1987)
according to their information-seeking strategies and their underlying motivations. age
has also been shown to be a significant predictor of the amount. and type. of information
obtained by consumers. A study by Morris, Grossman. Barkdoll. Gordon and Chun
(1987) has shown that the elderly seek less information from health professionals about
their drugs than their younger counterparts. despite the fact that the elderly are most in
need of such information. Morris et ai (1987), however, suggested that this may be due to
the type of condition being treated among the eldedy. For the most part. elderly patients
consume medications for chronic rather than acute illnesses. Thus, the nature of the
information provided rnay Vary depending upon the condition being treated. Moms et al.
(1987) suggested that the elderly may obtain information over a longer period of time
than their younger counterparts due to the nature of their illnesses. However, this
explanation is only speculative as the respondents included in this study were only
eligible to participate if they had obtained one or more new (e.g. non-refill) prescriptions
in the last four weeks. Thus, those consumers taking prescription drugs for chronic
illness were excluded from the analysis.
In terms of the nature of the information provided. younger consumers were more
likely to receive information about the side effects of their drug purchases than the elderly
(Moore, Kalu, Yavaprabbas, 1983; Moms et al., 1987). A statistically significant
difference (p < .0001) also existed between self-initiated searches for information among
elderly and non-elderly subjects. Younger subjects were more likely to consult friends
and relatives about their prescription. while the elderly were more likely to report that
?heyobtained information about their prescription from mass media sources such as
magazines. newspapers. television and radio.
While not specifically related to prescription dmg usen. a study conducted by
Stnitton & Lumpkin (1992) examined the differences in information search behaviour
among eldedy adoptes and non-adopters of healthcare innovations. Of particular
interest in this study was the product category of generic dmgs. Innovativeness was
measured on two dimensions: the innovation's perceived effect on one's daily routines,
and the respondent's usage of the product. Information search, on the other hand. was
characterized by marketer-dominated sources (e.g. point-of-sale information).
interpersonal sources (e.g. spouse, fnends, neighbours), medical expert sources (e-g.
docton, pharmacists), independent expert sources (television programs. magazine and
newspaper articles) and mass media sources (e.g. television. magazine and newspaper
advertisernents). Compared to nonadopters, the results suggested that adopters placed
more importance on interpersonal sources, independent expert sources and medical expert
sources as potential sources of information for genenc dmgs. However, the relative
importance of each information source type for both adopters and nonadopters were:
medical experts, interpersonal, marketing-dominated, independent expert sources, and the
mass media. Strunon & Lumpkin (1992) noted, however, ihat the nurnber of sources
evaluated as "important" among both adopters and non-adopters were relatively small,
which suggested that information search. even for a health-reiated purchase, is a relatively
limited activity.
2.2.6
Summary
While typologies of the information search behaviours, and motives of
prescription drug users have been developed, greater emphasis needs to be given to the
attitudinal or psychographic dimensions inhibiting or facilitating information search for
prescription dmg information. If unique groups of prescription drug users exist. this will
aid in the development of informational materials and educational programs targeted to
these consumers.
2.3
Measures of External Search Behaviour
A variety of rneasures have been used to define extemal search behaviour (Beatty
& Smith, 1987; Kiel & Layton, 198 1; Moore & Lehmann, 1980; Newman & Staelin.
1972). However. a consistent definition of what behaviours constitute extemal
information search is lacking. Past studies from the examination of extemal search for
durable goods have included the number of informational sources frorn which
information was sought, the amount and types of information soughk the time dimension
over which infomztion was sought, the number of brands for which information was
sought, and the manner in which the information was sought, as constituting extemal
information search (Kiel & Layton, 198 1). In order to detemine which behaviours
constitute external search, however. a consistent definition of what is meant by external
search is required. For the purposes of this research. the general definition of extemal
search used by Beatty & Smith (1987) will be employed:
"External search is the degree of attention, perception. and effort directed toward
obtaining environmental data or information related to the specific purchase under
consideration" (Beatty & Smith. 1987. p. 85).
Most studies on consumer information search have tended to focus on prepurchase events, with little. if any, emphasis given to information collected on an ongoing basis. It is proposed that an orientation focusing solely on pre-purchase search is
deficient and unable to account for search activity that occurs without a recognized
consumption need. More specifically, a study conducted by Bloch, Sherrell & Ridgway
(1986) examined search activity outside of a purchase context. These researchers viewed
the emphasis in the existing literature on pre-purchase information search as a conceptual
shortcoming. and concluded that by limiting the study of search to prepurchase settings
allowed only a subset of consumers' total search activity to be assessed. In their
frarnework, search was conceptualized as involving two cornponents: pre-purchase search
and ongoing search. It was hypothesized that consurners engaged in on-going search for
two reasons: to build a useful bank of product information for future use, and for
recreational activity. The results demonstrated that recreational motives were an effective
predictor of the frequency of ongoing search engaged in and helped to explain a high
proportion of the variance in search behaviour when combined with informational
motives. The study also provided a strong relationship between a consumer's
involvement in a product class and the propensity ro engage in ongoing search. This
study is particuiarly useful in understanding situations where product information is
obtained. yet the plan to purchase may be temporally removed, or in some cases. non-
existent (Bloch, Shemll& Ridgway, 1986).
To highlight the lack of attention paid to the activities undertaken after purchase,
Hoyer argued that the traditional decision-making models (Engel, Kollat & Blackwell,
1973; Bettman; 1979) focus on the cognitive processing that occurs immediately prior to
the act of purchase to the exclusion of other types of processing that may occur outside of
the immediate choice context. While this theory (Hoyer, 1984) does not directly address
the information search process per se, it has implications for those products that do not
require a considerable degree of cognitive processing prior to purchase. These products,
however, may require mote cognitive effort over repeated use of the product, rather than
at the initial purchasing stage. This may occur in the case of a prescription dmg purchase
due to side effects from taking the medication. curiosity, or educational efforts directed to
the consumer via advertisements in the media or pamphlets provided in retail outlets.
2.4
Methodologies Used in the Information Search Literature
Previous studies of consumer information search have employed different
methods to obtain measures of extemal information search. The two methodologies most
cornmonly employed in the literature are self-report, survey-based information, and those
using a behavioural process methodology. Conflicting opinions in the literature,
however, do not provide definitive answers as to which method of data collection is most
appropriate.
Studies conducted by Beatty & Smith ( 1987). Kiel & Layton ( 1981 ), Newman &
Staelin (1972) have used self-reported responses to obtain measures of external
information search. However. a problem associated with the retrospective studies of
information seeking has been the low reliability and questionable validity of the measures
used. Recall of the information search activities undertaken by consumers has been
reported to be low (Newman & Lockeman, 1975).
The study conducted by Newman & Lockeman (1975) compared survey-based
and observation-based measures of information search. The results found little or no
correlation between the observation-based and survey-based scores. The findings also
suggest that consumen tend to undemport the arnount of in-store information search they
engage in. The researchers raised concem over the accuracy of survey methods in
accounting for the search undertaken by consumen, and they suggesr that better measures
are needed to examine the extent to which various information sources are used by
consumen. However, to support the use of survey-based methods. Beatty & Smith
(1987) state that,
"until a superior methodology appears. self-report measures seem to be the most
reasonable measures of extemal search acûvity available for "real" purchase
decisions" (p.93).
Due to the inherent problems with self-reported measures of search. behavioural
process methodologies using an information display board have been used to moniior
extemal search (Moore and Lehmann; lacoby, Chestnut. Wei@& Fisher, 1976). This
rnethodology employs a board that displays product information arranged in a brand-byattnbute matrix, and has allowed researchers to obtain more specific measures of search
(lacoby et al.. 1976; Bmcks, 1985) However, the information display board methodology
has some significant limitations. Most importantly, information display boards impose a
well defined. comprehensive structure on what is often perceived to be an il1 structured
problem. As well, information display boards also delirnit the size of the brand choice
problem by defining the number of available alternatives and attributes (Brucks. 1985).
Alternatives to retrospective self-report and behavioural process methodologies in
the research have utilized direct observation of search activities, as well as verbal
protocols from purchasers while they are shopping (Newman & Lockeman, 1975;
Bettman, 1970). Direct observation is problematic, however, because the incidence of
information seeking tends to be higher when consumers are being observed (Newman &
Lockeman. 1975). Verbal protocols, on the other hand, are problematic because there is
only a weak relationship between intended and actual information seeking (Lenz. 1984).
III.
METHODOLOGY
3.1
Research Purpose and Objectives
The primary purposes of this study were:
To identib segments of prescription dmg users by classiQing consumers
according to similarîties on attitudinal dimensions related to health, prescription
dmgs, and the conditions frorn which they suffer,
if' attitude groups exist, to profile each segment in tems of demographic,
situational and behavioural dimensions;
If attitude groups exist, to suggest implications for various stakeholders, such as
govemment agencies, health professionals, phamiaceutical manufacnirers. and
retailers.
Classifying prescription drug users into groups according to their similarity on
attitudinal dimensions is useful for marketing practitioners. The technique is primarily
descriptive in nature. No formalized hypotheses are required. Since the resulting groups
or segments cannot be specified a priori, no hypotheses of the relationship between the
groups (if they exist) and various demographic, situational and behavioural dimensions
can be specified.
3.2
Research Design
A survey research design was used. A self administered questionnaire was mailed
to memben of a consumer mail panel who previously indicated that they had taken a
prescription medication over the last 12 months. A mail questionnaire, using a consumer
panel, was chosen for a variety of reasons:
O
Consumer panels are representative of the general population across many sociodemographic charactcristics;
A sample representative of the population using a different methodology. such as
through telephone interviewing. would be difficult and more expensive to obtain;
Panel response rates are higher. with an average response rate of roughly 75%
(Aaker & Day. 1983). Given that a large sample size was needed for this study, a
mail panel provided greater assurance that a large sampie size would be achieved.
in summary. a consumer mail panel provided a high response rate and a lower
cost. though it is recognized that a trade-off may have been made in terms of the
generalizability of the survey results to the Canadian prescription drug population as a
whole.
The survey was conducted using the Market Facts of Canada Ltd. Consumer Mail
Panel. Questionnaires were sent to either the male or fernale head of household
belonging to the Market Facts of Canada consumer panel. who in the 1995 Sumrner
Flexibus conducted by Market Facts. indicated that they had taken a prescription
medication over the last 12 months. The questionnaires were distributed to panel
members early in the fa11 of 1995.
3.3
Questionnaire Development
The questionnaire was developed using ideas obtained from the literature review,
consultation with stakeholders in the phannaceutical industry, discussions with Market
Facts of Canada Ltd., and the MSc. advisory committee. Questionnaire content was
drawn extensively from past research conducted by Morris et al. (1987, 1992). Specific
question areas drawn from this research related to the patient's experiences with the
doctor and pharmacist in terms of receiving verbal and written information. the patient's
question asking ability, the patient's use of other sources (other than the doctor and
pharmacist) to obtain information, and 19 out of the 32 anjtudinal variables that were
used as the basis of segmentation. The other attitudinal variables were borrowed h m
propnetary studies supplied by representatives from the pharmaceutical indusûy. These
variables were also used as part of the segmentation.
The questionnaire was pretested with a sample of 20 graduate students. in addition
to three relatives of the graduate student and advisory committee . The pre-test sample
read through and completed the question critiquing question wording/comprehension.
questionnaire flow, and survey length. The questionnaire was also reviewed by
representatives of Market Facts of Canada Ltd. to ensure the language used was
consistent with Company practices.
3.4
Statistical Analysis
3.4.1
Factor Analysis
A factor analysis was conducted with a total of 32 different attitudinal items (24a-
24ff ;see Questionnaire in Appendix VTI) to identify psychographic dimensions which
might underlie information search behaviour. The purpose of the analysis was to reduce
the number of attitudinal items to a smaller number of dimensions which would be used
as the basis for clustering.
The data set was factor analyzed first by Rincipal Components Analysis and then
by Principal Axis Factoring. This was done in accordance with Tabachnick and Fidell's
(1989) recommendation of using PCA as the fust step in any factor analysis. which gives
the researcher insight into the number and nature of factors. However, as Tabachnick &
Fidell indicate. Principal Components Analysis is often not a sufficient factor analysis
procedure because it analyzes al1 the variance in the observed variables. including error
variance. Consequently, Principal Axis Factoring was used as the final bais for factor
extraction. This method was deemed to be more suitable because it only analyzes the
shared variance, and ignores variance due to error. This solution yielded the same
number of factors as in the PCA analysis, and was interpretable. This solution was used
to compute factor scores that could be used in the cluster analysis.
Varimax rotation was used in order to increase the interpretability of the factors- It
is an orthogonal method, used when the factors are expected to be uncorrelated. Varimax
rotation was used in the factor procedure because there was no assumption made that the
dimensions would be related to one another (Tabachnick & Fidell, 1989).
The number of factors to be retained were determined using four guidelines:
eigenvalues of the facton. the scrw test. the amount of variance accounted for by each
factor and group of factors. and the interpretability of each factor.
Fint, an examination of the eigenvalues was undertaken. Only factors having an
eigenvaiue of one or more were considered since those with lesser values are not as
important, from a variance perspective. as an observed variable (Tabachnick & Fidell.
1989).
Second. the scree plot of the eigenvalues associated with each factor was
examined empincally to detemine at which point there was a noticeable drop in the
values from one factor to the next Most commonly. those facton with an eigenvalue of
one or more are retained for further consideration.
Third. the 5 factor through to 9 factor solutions were exarnined to determine the
cumulative amount of variance for which each solution accounted. Those soiutions which
had a significant increase in total variance explained from the previous solution were
retained for further consideration.
Fourth, within each solution. the factors were examined for their interpretability.
The nature and meaning of each factor was judged according to the grouping of the
original variables which were most highly associated with that factor.
After a final factor solution was chosen, a decision was made regarding whether
to use factor scores or composite variables as the basis for clustering. There were both
advantages and disadvantages associated with each approach. Factor SCORS are
advantageous because they take into account each of the item's reliability. which means
that items receive different weights depending on their reliability. The drawback,
however, is that the creation of factor scores capitalizes on chance relationships among
variables so that factor-score estimates are biased (Tabachnick and Fidell, 1989).
Tabachnick and Fidell also cite that factor scores suffer from indeterminacy because there
is an infinite number of possible factor scores that can be denved, and that there is no
definitive way to decide among them.
Regarding the use of composite variables, they are best used when the scale is
untested and exploratory, with little or no evidence of reliability or validity. The
drawback to the use of composite variables is that each of the items that compose a factor
may receive equd weight, despite the fact that their variance conûibutions may differ.
Composite variables were used as the basis for ciustering due to their greater
predictive ability, interpretability, and given that the scales were untested and exploratory.
These composite variables were created using the variables which loaded highly on each
of the factors from the Principal Axis Factoring method. The raw score values of al1 of
the variables with loadings of .3 or more for a factor were totalled to produce a single
composite variable or scale. Eight summated scales were created. These scales were
reduced to a comrnon metric by averaging their total values by the number of variables
that they were composed of. These averaged scales were used as input for the cluster
analysis.
3.4.2
Cluster Analysis
Cluster analysis was used to identify naturally occurring psychographic segments
among the sample of prescription drug users. The segmentation was based on the eight
attitudinal dimensions created through Principal Axis Factoring method and scale
summation.
The clustering method used was Ward's method. This clustering method uses a
hierarchical method, and is considered to be one of the better clustering methods
available (Hair, Anderson. Tatham & Black. i987). The advantage of a hierarchical
method over a non-hierarchical method is that it does not require the specification of a
starting seed which has to be specified based on a non-empirical critenon. Hair et al.
(1987) state that the use of non-hierarchical techniques with random seed points is
markedly inferior to hierarchical techniques. Ward's method starts the process of
clustering by treating each case or respondent in the sample as a cluster. In each
successive stage, one cluster is combined with one other cluster to which it is the closest.
This process continues until at the last stage dl the cases have been combined into one
large cluster. The agglomeration schedule output of this analysis summarizes what has
occumd at each stage of the clustering process.
The agglomeration schedule was used to judge the nurnber of clusters chosen.
It
presents which sub-chers are being combined at each particular stage and the distance
between the most dissimilar points of those clusters. The stage at which a relatively large
clustering coefficient appears indicates that two very different clusters are being
combined. Following convention. the number of clusters chosen is the number associated
with the stage immediately preceding the stage at which this large coefficient is noted.
It seemed aso on able to expect that between 3 and 5 clusters would be identified
among the sample of prescription drug users examined. Previous research had identified
an average of about four types. With this in mind. the number of clusten to be accepted
was detennined empirically by examining the agglomeration coefficients, assuciated with
the different cluster solutions.
Having chosen the number of clusters generated from the hierarchical model. the
second step was to use a non-hierarchical procedure to confirm the results. Cluster means
generated from the hierarchical method were input into the Quick Cluster method. a nonhierarchical method in order to confirm the results. In performing this type of cluster
analysis, the initial seed points are the cluster centroids on the eight cluster variables.
According to Hair et al. (1987). the advantages of the hierarchical rnethod are
complemented by the ability of the non-hierarchical method to confinn the results by
allowing the switching of cluster membership. If the non-hierarchical results agree with
the results frorn the hierarchical approach, it provides support that it is a suitable cluster
solution for the data set.
3.4.3 Cross-Tabulations
Cross-tabulations are used to examine the relationship between two or more
categorical variables. A chi-square statistic c m be used to test the nul1 hypothesis of no
relationship between the two variables (Hair et al.. 1987).
In this case. cross-tabulations were perfonned to determine if tespondent
membenhip in a cluster was confmed by two reported behaviours. These variables
were: asking a doctor to prescnbe a specific drug. and asking a pharmacist to dispense a
generic drug over a brand name prescription medication.
3.4.4
Discriminant Analysis
Discriminant analysis was used for each of three separate sets of predictor
variables, demographics. situationai. and behavioural measures of information search.
The goal of discriminant anaiysis was to find the dimension or dimensions dong which
the cluster groupings differed and to find classification functions to predict group
membership.
Each discriminant analysis used the direct method. With the direct method, al1
predictors enter the equations at once and each predictor is assigned only the unique
association it has with the groups (Tabachnick & Fidell, 1989). Other methods. such as
hieranihical and step-wise methods were not used because there was no theoretical reason
to suggest that certain variables should be entered into the equation prior to the entry of
other variables.
In order to determine the number of discriminant functions that best separate the
cluster groupings. the eigenvalues. percents of variance, and canonical correlations were
used to assess the significance of each discriminant function extracted. Following this.
the plots of centroids were assessed to determine how the cluster groups were separated
by the discriminant functions. The meaning of the functions are inferred from the pattern
of correlations between the function and the predictors. Though consensus is lacking
regarding how high correlations in a loading matrix must be in order to be interpreted. by
convention. correlations in excess of .30 (9%of variance) may be considered eligible
(Tabachnick & Fidell, 1989).
Finally,classification functions were used to predict group mem bership and to
check the adequacy of classifications for cases in the same sample through crossvalidation. Cross-validation involves dividing the sample randomly into two parts, and
denving classification functions on one part, and testhg them on the other (Tabachnick &
Fidell, 1989). The stability of the classification procedure was checked by crossvalidation. Approximately 25% of the cases were withheld from calculation of the
classification functions.
3.4.5
Multinominal Logistic Regression
A second approach used for undentandhg and predicting group membership was
logistic regression. Logistic regression was used to identify the demographic variables
that discnminated among cluster groups. This procedure allows for the examination of a
dichotomous dependent variable and both continuous and categorical independent
variables.
According to Hair et al. (1987), logistic regression or logit analysis is preferred
over discriminant analysis in certain circumstances because discriminant analysis relies
on multivariate nonnality and equal variancecovariance matrices across groups. Logistic
regression or logit analysis does not face these strict assumptions, thus making it a more
appropriate technique when these assumptions are violated. Similarly. OGonnan &
Woolson (1991) recommend using logistic regression for estimation when the underlying
distribution is nonnomal since the estimates of the coefficients will be biased if
discriminant analysis is used.
In order to detemine the assessrnent of the fit of the model in logistic regression,
the likelihood value (-2LL). which is sirnilar to R~ in multiple regression. was used. in
interpreting the likelihood value, a weU fitting model should have a small value for -2LL.
The model c m also be assessed by the classification table which compares actual events
versus the predicted values. (Hair et al, 1987).
3.5
Research Assumptions
First, subjects in the study were adults who had obtained at least one prescription
medication for themselves in the past 12 months. It was assumed that respondents
were able to recall relevant information that was received or sought about their
prescription medication(s) or about the condition for which they suffer over the
course of this tirnefiame;
Second, it was assumed that the appropriate household member, who had
responded to the screener questionnaire, completed the follow-up questionnaire:
3.6
Research Limitations
The sample used in this research may not be representative of the Canadian
prescription drug user population overall due to the fact that the sample was
drawn from a defined population of panel members. which are self-selected rather
than randomly selected. However, the fact that the demographic profile of this
sample is sirnilar to the Canadian population of prescription dmgs users overall
(See page 45 for more detail) suggests that the two groups are not widely
divergent.
An order bias, and consequently, an increase in response error may have occurred
because of the consecutive arrangement of the thirty-two attitudinal scales from
which the cluster solution was derived. Participants may have experienced
response fatigue, which may have affected the answea they gave to the attitudinal
items appearing at the end of the attitude battery. To mitigate the effects of orderbias, selected statements were worded in the negative to ensure that participants
were reading and responding to the statements appropriately.
IV.
RESULTS
4.1
Introduction
The research findings are presented in five main sections. The first section
presents the response rate and composition of the sample. The second section presents
the results of the factor analysis, which were used as the basis for conducting the cluster
analysis. The third section presents the results of the cluster analysis, which sought to
identify a typology of prescription drug users. The fourth section presents the findings of
the cross-tabulations which were conducted in order to determine if the derived cluster
membenhip could be confirmed by reported behaviours. Finally. the fifth section presents
the fmdings of the discriminant and logishc regression analyses which sought to profile
the resulting clusters in tems of demographic, situational and behavioural variables.
4.2
Sarnple
4.2.1
Response Rate
A total of 600 questionnaires were sent to either the male or fernale head of
household belonging to the Market Facts of Canada Consumer Mail Panel, who in the
1995 Summer Flexibus conducted by Market Facts, indicated that they had taken a
prescription medication over the last 12 months. Of these, 456 prescription drug usen
retumed completed questionnaires. This represents a response rate of approximately 7 6 2
-- consistent with similar studies of this nature.
According to Aaker et al. (1983).
consumer mai1 panels typically achieve an average response rate of 75% .
4.2.2
Sample Characteristics
Roughly eighty percent of the sample were women (78%)and just under one-half
of the sample were 55 years of age or older (45%). The largest proportion of the sample
was represented by the elderly (28% were 65 years of age or older). Almost one-half of
the sample had some education beyond high school. Nearly one-quarter (24%) of
respondents were university educated. while another 18% had some other type of postsecondary education. The income level of approximately 60% of the sample was less
than $60,000 per annum; 39% reported an annual household income of less than $30,000;
39% reported an income of between $30,000-$59,999 per year. Just under onequarter of
the sarnple (224) eamed $60,000 or more on an annual basis. The sample was
represented primarily by residents of Ontario (representing roughly 40% of the sarnple)
and Quebec (24%). These provincial proportions are roughly equivalent to 1991 Census
data population counts (i.e., Ontario 37%. Quebec 268).
Two-thirds of the sample (67%) reported having filled a prescription medication
within four weeks of receiving the questionnaire. Most of the sarnple received refills of
an existing prescription medication (72%) cornpared to those who received a new
prescription medication (28%). Three-quarters of the sample (758) were still taking the
medication at the time they received the questionnaire; over one-half (53%) had been
taking the medication for more than one year. Seventy percent (70%)of the sample
reported that the prescription medication that they had filled most recently was for the
treatment of a chronic illness, predominantly respiratory disease (i.e. asthma) (13%) and
cardiovascular disease (i.e. hypertension) (19%). At the time of completing the
questionnaire, onequarter (27%) of ihe sample reportcd that they were taking only one
medication, 19% were taking two prescnption medications. 15% were taking three
prescnption medications and 19% were taking more than three.
This sarnpie is deerned to be representative of the population of prescnption drug
users in ternis of age and gender because these characteristics are aligned with statistics
given in the Annual Statistical Report of the Prescription Dnig Services Branch (19951996). This report cites that the highest percent of prescriptions used in 1995-1996 was
in the 65 years or older group. receiving 44.1% of al1 prescriptions. By gender, females
accounted for 60.8% of al1 prescriptions and received more prescriptions across al1 groups
(beyond age 14) than males. Consequently, the sample is represented by the heaviest
users of prescnption medications, and is therefore, appropriate for research into the
information search activities of prescription drug users.
4.3
Factor Analysis
The purpose of the factor analysis was to reduce the number of attitudinal items to
a smaller set of dimensions. and to understand the underlying motivations of prescnption
drug usen that may be related to information search behaviour.
After removing univariate and multivariate outliers. a total of 433 cases were
analyzed for the factor analysis procedure. The criterion given by Tabachnick & Fidell
(1989) of having at least 5 cases per variable was achieved -- with approximateiy 13.5
cases per observed variable. Missing data was handled by rnean substitution in order to
ensure an adequate sarnple size for analysis.
Frequencies weR nin on the 32 variables and examined for skewness in their
distributions. Though a few variables were skewed, factor solutions are useful even when
observed variables are not normally distributed (Tabachnick & Fidell, 1989). To assess
Iinearity. scatterplots were examined. Nonlinearity was not a concern, and therefore
variable transformation was not considered necessary.
Four different indicaton, generated through the factor procedure, were examined
which confirrned that these attitudinal items were suitable for factoring. These included
the correlation matrix, Bartlett's Test of Sphericity, the Kaiser-Meyer-Olkin measure of
sampling adequacy, and the anti-image correlation matrix. Most of the correlations in the
matrix were low (Appendix 1). Large sample sizes. however, tend to produce low
correlations and since there were correlations of 0.3 and over. factor analysis may still be
used (Tabachnick & Fidell. 1989). Bartleit's Test of Sphencity had a value of 345 1.7599
and a significance of less than 0.0001, though its sensitivity and dependence on sarnple
size is likely to be significant given a large size even if correlations are low (Tabachnick
& Fidell, 1989). The Kaiser-Meyer-Olkin measure of sarnpling adequacy had a value of
0.80416, indicating that the matrix is factorable. According to Tabachnick & Fidell
(1989). values of .6 and above are required for good factor analysis. Further, most of the
offdiagonal elements of the anti-image correlation matrix were low. This low proportion
of large anti-image coefficients denotes that there are likely no correlations between the
unique parts of the variables being factored. and therefore factor analysis may be used.
To aid in the interpretation of the factor analysis, Principal Components Analysis
was conducted to provide insight into the number and nature of factors. Using the
accepted mle that factors with eigenvalues greater than one should be extracted
(Tabachnick & Fidell. 1989). a nine factor solution was identified. These factors had
eigenvalues of 5.14, 3.52. 2.15, 1.68. 1-46, 1.25. 1.19, 1-14, 1.O7 respectively.
The underlying attitudes were labelled as Information Involvement. Self-Assured
Knowledge. Doctor-Oriented Information. Information Avoidance, ManufacturerOriented Information, Self Care Orientation. Phamacist-Orientcd Information and
Patient Decision Making. The identity of each the factors was infened from the group of
attitudinal items loading the highest on that factor. A cut-off of -3 for inclusion of a
variable in the interpretation of a factor was utilized.
The item Ioadings for each factor are presented in Table 1. Based on the factor
loadings. the ninth factor was deemed to be uninterpretable as only one item loaded
highly on this factor. Other items loading on this factor just barely met the .3 cut-off for
inclusion.
Table 1: Principal Components Andysis Factor Solution
9.Rotated Factor Mavix
Like-to know as much about my
0.68
medication...
24M Feel better about taking when
knowledgeable...
24A Usually ask Dr. questions at tirne of medical 057
visit..
241 Get information h m books and written ... 0.42
24DD Helpful if Dr.gave written information...
OAS
24F Should be told about medication options... OR7
24FF Think of questions before medical
OAl
appointment...
24N Information is too hard for me to
-0.05
understand...
24CC Dr. uses words 1don't understand...
0.06
24U Don't know enough to make informed
-0.04
medication choices...
24EE Phanpacist is too far away to ask
0.09
questions...
24K Not cnough privacy at phannacy counter... 0.16
24J Don't think Drs. know enough about drugs... 0.13
0.10
24X Know more about my condition than
orheS...
24L Know more about my medication than
0.24
ohers...
24Y Know where to find a11 the information 1
0.14
need ...
24AA Would.rather have Dr. make decisions than -0.09
to be given choice...
24V Aiways better t seek professional help than 030
to mat yourseik..
24H Better to rely less on doctors and more on -0.05
your own common sense...
24BB Should be abIe to choose medication ...
0.15
24P No need to ask questions if trust doctor... -0.26
24B Helpfid to ask friends or family about
0.19
dnig ...
24s h g companies ought û~inform
0.15
consumers...
24D h g Company should tell me about cimg.,. 0.03
24E Doctor is open to questions ...
0.34
0.10
24R Doctor provides information about h g . . .
24C Phmacist provides info about the d m g ... 0.06
030
24G Usually ask pharmacist questions about
dnig...
24T Want to be able to detemine if medication 032
1s working ...
242 Wmt info to decide if dnig should be
0.20
taken.. .
240 Rgmembering to take medication is
-0.07
dtfficult...
24Q More concemed about my health than
-0.04
othem...
Loadings of .3and above are indicated in bord
0.27
0.09 -0.08
0.09 -0.09 0.21 0.20 0.01
0.23
0.46
-0.07 0.07 0.16 0.01 -0.0-
-0.06
0 3 2 4.12 4.15
-0.02 4.26 0.18
-0.08 0.10 0.03
0.24
0.28
0.42
-0.04 0.01 0.14
0.07 4-19 0.03 -0.W
0.05 0.15 037 - 0 3
0.14 -0.21 -0.10 030
4.1 1
0.15
-0.03
0.00 0.75
-0.06 0.07 0.04 0.14 0.05
0.00 -0.23 0.64
43-15 -0.19 OS5
-0.09 0.10 -0.15 0.01 -0.05
0.26 -0.01 -0.06 0.14 0.17
0.14
0.07
0.27
036
-0.19 0 3
0.29 -0.46 0.23
0.16 -030 0.24
0.78 0.03 0.01
0.02
0.09 -052 -0.02 0.19
0.02 4.01 -0.12 -0.07 0.23
0.05 0.41 0.21 0.05 0.28
0.15 0.07 0.06 0.19 -0.E
0.75
4.10 -0.03 -0.01 0.24 0.04 0.06 0.00
0.61
031
-0.28
0.03
4.05 0.34
0.44
0.14 -0.42 0.06 -030 -0.M
-0.02
0.12
0.06
-0.64 0.08 -0.08 O. L 7
0.14 -0.00 0.07
0.01
0.60
-0.05 4.09 0.03
0.48 0.44)
0.03 0.12 0.25
0.02 035 -0.07 0.66 -0.05
-0.03 -0.19 -0.15 -0.16 0.06
0.29 059 0.10 -0.08 -0.OC
0.09
0.01 -0.01
0.79
0.10
0.05
0.15
0.14
0.05
4.13 -0.03 0.79
0.65 -0.16 0.03
0.70 4.12 -0.16
0.17 -0.05 0.02
-0.03 0.04 O.I Z
O.12
-0.02
0.05 0.05 -0.16 -0.02
0.02 0.19 O. 18
0.00 -0.00 0.12 0.02
O.1 O
0.02
-0.11
4.05
0.1 7
0.14
0.10 -0.04
0.14 -0.00 -0.08
0.12 -0.01 0.08
0.77 0.00 0.06
0.71 -0.03 0.10
0.08 -0.08 0.09
039
4.08 O.1 1
0.23 4.00 0.12
0.29
0.19 -0.02 057 0.24
-0.09 -0.03 0.03
-0.01 0.05 0.07 0.08 0.79
0.42
0.12
0.08
0.23
0.46
031
-0.14 0.04 0.47 -0.02
Table 2 presents the attitudinal items that make up each factor with their
suggested factor narnes.
'able 2: S u w t e d N a m s For Principal Components Anaiysis Factoring Method Results
Que. #
Factor 1:
24W+
24M+
24A+
24DD+
24F+
241+
24FF+
24V+
24E+
24G+
24T+
Factor 2:
24X+
24L+
24Y+
24Q+
241+
Factor 3:
24R+
24E+
24P+
24A+
24K24124Y+
24AA+
Intormatlon Involvement
1 like to know as much as 1 can about the medication the doctor prescribes for me.
1 feel better taking drugs when 1 am knowledgeablc about them.
1 usually ask the doctor questions about the cimg prescribed at the time of my medical visit.
It would be helpful if the dcctor provided wntten information about the drug he/she prescribes.
When there is more than one medication to treat rny condition. 1 should be told about each one.
1 often like to get information about my medication from books and other written materials.
t some questions and wrote them down
It would be easier to ask the doctor questions if 1 ~ o u g hof
before my appointment.
It's always bener to seek professional help than to try to m a t yourself
My doctor is generally open to questions about the dmg(s) he or she prescribes
1 usually ask the phmacist questions about the dru@) when 1 am having the prescription filled
1 want infornation about prescription drug(s) so 1 can determine if the medication is working or
not.
Seif-Assured Knowledge
1 feel 1 know more about my condition than do other people experiencing the sarne heaith
condition as me.
1 feel 1 know more about my medication than do other people who take the sarne medication as me
1 feel I know where to find al1 the information 1 need on my medication.
I feel 1 am more concerned about my health than are otfier people my age.
1 often like to get information about my rnedication(s) from books and other written materials.
Doctor-Oriented Information
The doctor always provides me with information about the medication he or she prescribes for me.
My doctor is generally open to questions about the drugs he or she prescribes.
There is no need to ask questions about prescription dmgs if you m s t your doctor.
1 usually ask the doctor questions about the drug prescribed at the time of my medical visit.
There is not enough privacy at the pharmacy counter to ask questions about the medication 1 have
been prescribed.
1 don't think doctors know enough about the drugs they prescribe
1 feel 1 know where [O find al1 the information 1 need on my medication
1 would rather have the doctor make the decision about my treatrnent than for him or her to give
me a whole lot of choices.
Factor 4:
24N+
24CC+
24U+
24AA+
24P+
24EE+
Factor 5:
24!3+
24D+
24T+
24DD-t
Factor 6:
24V24H+
24B+
24AA-
24I+
243+
24BB+
Factor 7:
24C+
24G+
24EE-
Idormation Avoidanœ
Information about rny medication is too hard for me to undentand.
The doctor uses words 1 don't understand when tefling me about the drug hetshe is prescribing.
1 feel 1 don't know enough about my medication to make informed choices about which
medication 1 should take.
1 would raiher have the doctor make ihe decision about my mament than for him or her to give
me a whole lot of choices.
There is no need to ask questions about prescription dmgs if you trust the doctor.
The pharmacist is too far away behind the pharmacy counter for me to ask questions about the
drug(s) prescribed
Manufacturer-OrientedInformation
Drug cornpanies ought to inform consumers about health issues and medications.
The cornpany who makes the drug should tell me what i need to know about my medication.
1 want information about my prescription dmg(s) so 1 can detemine if the medication is workhg
or not
it would be helpful if the doctor provi&d writren information about the drug(s) helshe prescribes.
Seit-CareOrientation
It's dways better to seek professional help than to try to ueat yowself.
It is better to rely less on doctors and more on your own cornmon sense when it comes to carhg
for your body.
It is helpful to ask my fnends or family questions about the drugs 1 am taking.
1 would rather have the doctor make the decision about my treatment than for him or her to give
me a whole lot of choices.
1 often Iike to get information about my rnedication(s) From books and other written matenals.
1 don't think doctors know enough about the drugs they prescribe.
When there is more than one medication to treat my condition. 1should be allowed to choose
which medication I want to cake.
Pharmacist-Oriented Infornation
The phmacist always provides me with infornation about the medication he or she gives me.
1 usually ask the pharmacist questions about the drugs when I am having the prescription fillesi
The pharmacist is too far away behind the counter for me to ask questions about the h g
prescribed.
Factor 8:
Patient Decision Making
24BB+
When there is more than one medication to mat my condition. 1 should be allowed to choose
which medication 1 want to take.
1 want information about my prescription h g so 1 can decide if 1 should take the medication.
1 feel 1am more concemed about my health than are other people my age.
1 want information about rny prescription drug so 1can determine if the medication is working or
not.
When there is more than one medication to treat my condition. I should be iold about e x h one.
1 would rather have the doctor make the decision about my marnent han for him or her to give
me a whole lot of choices
24Z+
24Q+
24T+
24F+
24AA-
Factor 9:
224Tc
Incondosive
24F24FF+
When there is more than one medication to ueat my condition. 1 should be told about each one.
It would be easier to ask the doctor questions if 1 thought o f some questions and wrote hem down
before my appointment
Remernbering to take my medication is often difficult for me.
I want information about prescription dmg(s) so 1 can determine if the medication is working or
not
The PCA solution was used to provide guidance into the number and nature of the
factors. However, it was not chosen because this method analyses al1 the variance in the
observed variables, including error variance. A Principal Axis Factoring was used as the
final b a i s for factor extraction. This method was deemed to be rnost suitable because it
only analyzes the shared variance, and eliminates variance due to error (Tabachnick &
Fidell, 1989). This solution yielded the sarne nurnber of factors as in the PCA analysis,
and was interpretable.
An eight factor solution was most interpretable. and as a result, was selected from
the Principal Axis Factoring results to represent the psychographic dimensions which
might underlie information search behaviour. In addition. compared to the nine factor
solution, which emerged for both PCA and PAF procedures. the eight factor solution
accounted for 40.4% of the variance, compared to 42.28 of the variance in the nine factor
solution.
Four out of the eight factors generated had eigenvalues greater than one. The
comsponding eigenvalues for each of the factors were as follows: 4.62.2.94. 1.56, 1.13.
.86, .67, .64. .52 respectively. Though eigenvalues of less than one represent less
variance than a single observed variable (Tabachnick & Fidell, 1989). the remaining 4 out
of the eight factors were retained because they were consistent with the results obtained
through the Principal Components factoring method, and were interpretable.
The underlying attitudes were labelled as Information Involvement. Self-Assured
Knowledge, SelfCare Orientation. Information Avoidance, Manufacturer-ûriented
Information. Doctor-Oriented Information, Pharmacist-Onented Information, and Patient
Decision Making. The identity of each the factors was inferred from the group of
attitudinal items loading the highest on that factor. A cut-off of .3 for inclusion of a
variable in the inierpretation of a factor was utilized.
The item loadings for each factor are presented in Table 3.
-
able 3: Principal Axis Factor Solution 8 Factor Solution
Rotated Factor Matrix
m l k e to know as much about my rnedicauon...
24M Feel better about taking when knowledgeable...
24A Usually ask Dr. questions at tirne of rnedical visit...
241 Get information from books and written...
!4DDHelpful if Dr.gave wriuen information...
24F Should be told about medication options...
Z4FF Think of questions before medical appointment..
24N Information is too hard for me to understand...
Z4CCDr. uses words 1 don't understand..
24U Doq't know enough to make informed medication
choces...
14EEPhaxmacist is t w far away to ask questions...
24K Not enough privacy at pharmacy counter...
241 Don? think Drs. know enough about dmgs...
24X Know more about my condition than others...
24L Know more about my medication than others...
24Y Know where to find ail the information 1 need...
!4AAWould ra@erhave Dr. make decisions than to be
given choice...
24V Always bener to seek professional help than to treat
yourself. ..
24H Better to rely Iess on doctors and more on your own
common sense...
!4BBShould be able to choose medication...
24P No need to ask questions if trust doctor...
24B Helphl to ask friends or family about drug ...
245 h g companies ought to inform consumers...
24D Drug Company should tell me about drug...
24E Doctor is open to questions...
24R Doctor pmvides information about drug...
24C Pharmacist provides info about the drug ...
24G Usually ask pharmacist questions about drug...
24T Wantto be able to determine if medicarion is
working...
242 Want info to decide if dmg should be taken...
240 Remernbering to take medication is dificult...
244 More concemed about rny health than others...
Factor Factor tactor Factor Factor Factor Factor Facto]
1
2
3
4
5
6
7
8
65 -.[O -26 -.O2
-10
.18
.19
-20
Table 4 presents the attitudinal items that make up each factor with their
suggested factor narnes.
*able 4: Suggested Factor Names For Principal Axis Factoring Method Resdts
Facfor 1:
Inionnation Involvement
24W+
24M+
24A+
24DD+
241+
24F+
24T+
Factor 2:
24N+
24CC+
24U+
1 like to know as much as 1 can about the medication the doctor prescribes for me.
1 feel bener taking drugs when 1 am knowledgeable about them.
1 usually ask the docmr questions about the drug prescribed at the time of my medical visitIt would be helphd if the doctor provided written information about the drug helshe prescribes.
1 often Iike to get information about my medication from books and other written materids.
When there is more than one medication to treat my condition. 1 should be toId about each one.
1want information about my prescription drug so 1can determine if the medication is working or not
24J+
Information Avoiâance
Information about my medication is too hard for me to understand.
The doctor uses words I don't understand when telling me about the drug he/she is prescribing.
1 feel I don't know enough about my medication to make infomed choices about which medication 1
should take.
The phannacist is too far away behind the counter for me to ask questions about the drug(s) helshe
prescribes.
There is not enough pnvacy at the pharrnacy counter for me to ask questions about the medication 1
have been prescribed.
1 don't think doctors know enough about the drugs they prescribe.
Factor 3:
24X+
SeK- Assured Knowledge
1 feel1 know more about my condition than do oiher people experiencing the same health condition as
24L+
24Y+
me.
1 feel 1 know more about my medication than do other people who take the same medication as me.
1 feel 1 know where to find al1 the information 1 need on my medication.
24EE+
24K+
Factor 4:
24A.A24V24H+
24B+
241+
24J+
24BB+
24P-
Self-Care Orientation
1 would rather have the doctor make the decision about my maunent than for him or her to give me a
whole lot of choices.
It's always better to seek professional help than m uy to m a t yourself.
It is better to rely less on doctors and more on your own common sense when it cornes to caring for
your body.
It is helpful to ask my fnends or farnily questions about the dmgs 1 am taking.
1 often like to get information about my medication(s) from books and other wrinen rnaterials.
I don? think doctors know enough about the h g s they prescribe.
When there is more than one medication to mat my condition. 1 should be allowed to choose which
medication 1 want to take.
There is no need to ask questions about prescription drugs if you trust the doctor.
Factor 5:
24S+
24D-t
24DPc
24T+
Manufacturer-OrientedInformatioa
g cumpanies ought to inform consumers about health issues and rnedications.
The company who makes the h g should tell me what I need to know about my medication.
It would be helphl if the doctor provided written information about the dmgs helshe prescribes.
1 want information about my prescription dnig so 1 can determine if the medication is worlcing or not
Factor 6:
24E+
24R+
24A+
Doctor-Oriented I n f o ~ t i o n
My doctor is generally open to questions about the dnigs he or she prescnbes.
The doctor aiways provides me with information about the medication he or she prescribes for me.
[ usuaily ask the doctor questions about the dnig prescribed at the tirne of my medicd visit.
Factor 7:
24C+
24G+
24EE-
Pharmacist-ChienteciInformation
Factor 8:
242+
24T+
24BB+
h
The phmacist dways provides me with information about the medication he or she gives me.
I usually ask the pharmacist questions about the drugs when 1 am having the prescription filIed.
The pharmacist is too far away behind the counter for me to ask questions about the dmg prescribed.
Patient Decision Making
1 want information about my prescription dmg so [ can decide if 1 should take the medication.
1 want information about my prescription h g so 1 can detemine if the medication is working or not
When there is more than one medication to m a t my condition. 1 should be allowed to choose which
medication 1 want to take.
After reviewing which variables loaded highly on each of the factors, composite
variables were created by surnming the items with loadings of .3 or more, and averaging
the score over the total number of variables in the equation. Composite variables were
used as the b a i s for clustering because of their predictive ability and interpretability.
Hair et al. (1987) suggest that when the scde is untested and exploratory, with little or no
evidence of reliability or validity. composite variables should probably be used.
The reliability procedure indicated that almost al1 of the items included in the
scale were reliable measures of each of the underlying anitudinal dimensions (Table 5).
Though Murphy & Davidshofer (1994) cite that there is considerable variability in
reported levels of reliability, estimates of 0.7 and over are deemed to be acceptable for
iating scales. Reiiability estirnates lower than 0.60 are usually thought to indicate low
levels of reiiability (Murphy & Davidshofer. 1994). It is important to note. however. that
reliability estimates are denved as a function of the number of items. and the average
interconelations among those items. Only Scale-7, the scaie related to information from
the pharmacist, fell below the 0.6 reliability coefficient critenon. This item, however,
consisted of only three variables. which may contribute to its comparativeiy lower
reliability level. Al1 eight scdes were used as the basis for the clustering procedure.
Table 5: Reüability Coeflicients for Composite Variables
Standardized
Item Alpha
4.4
Scale-1
Scale-2
Scale-3
Scale-4
Scde-5
Scale-6
Scale-7
Scale-8
-7431
.63U
.7099
.6705
.67 13
.7 172
5487
.fi464
Cluster Analysis
Reliminary cluster analysis, using Ward's method, indicated that there were likely
three distinct groups of prescription drug users. The agglomeration schedule produced
was visually inspected. Appendix III presents the last seven stages of the agglomeration
schedule. Table 6 presents the agglomeration coefficient for the first 1 through 7 cluster
groupings. Following convention, the nurnber of clusters to be chosen is the number
associated with the stage immediately preceding the stage at which a significant drop in
this coefficient is noted. Since the largest drop was seen in going from three to four
clusters, the three-cluster solution was selected.
Tabie 6: Analysis of Aplomeration Coeflicient for Hierarehicai Cluster Analysis
Number of Clusten
Percentage Change in
Agglomeration
Coeffiaent to Next Level
Al1 the prescription d m g users were then classified according to the similarity of
their scores on each of the underlying attitudinal dimensions. The three segments were
narned according to their score on each of the attitudinal dimensions (Table 7). Together.
these scores indicate the nature of the patterns of attitudes about each of the three
consumer segments.
1
Cluster Groups
1
Confident
Decision
Maken
(N=433)
1
Uninformed
(N=131: 30%)
Scale-l=Infomation Involvement; Scale-Z=Information Avoidance; Scde-3=Self-Assured Knowledge: Scale_4=Self
Care Orientation: Scale-S=Manufacturer-Oriented Information; Scale-6=Doctor-Onented Information:
Scale-7=Pharmacist-Oi7ented Infornation; Scale-8=Patient Decision Making.
The next step was to use a non-hierarchical procedure to confirm the results.
Cluster means generated from the hierarchical method were input into the Quick Cluster
method, a non-hierarchical method in order to confinn the ~ s u l t s .In performing this
type of cluster andysis, the initial seed points are the cluster centroids for each of the
eight variables as determined from the hierarchical procedure.
As part of the non-hierarchicd results from the Quick Cluster approach is an
analysis of variance which tests the effects of the attitudinal dimensions upon cluster
membership (Table 8). In this case, the analysis indicated that the effects of each of the
attitudinal dimensions on group membership were significant. This meant that the
differences between the three segments, on each of the amtudinal dimensions, were likely
not due to chance.
Table 8: Results of Non-hierarchicalCluster Analysis with initial Seed Points fmrn merarchical
aesults
Cluster
Scale-1
Scale-2
Scale-3
Scale-4
Scale-5
Scale-6
Scale-7
Scale-8
Cluster
Size
Classification cluster centres
r
1
1
5.6
3.83
3.73
3.90
5.65
5.17
4.14
5.1 O
t 33
2
6.3
3.O4
532
4.44
6.O3
6.14
5.92
5-99
166
3
5.04
2.5 1
3.9 1
3.17
4.1 1
6.09
5.44
3.27
131
Finai cluster centres
Variable
Cluster M.S.
E m r M.S.
F Ratio
Scale- 1
75.4239
.546
I38.0439
Scale-2
74.8729
399
83-2752
Scale-3
134.4076
1 570
85.6034
Scale-4
69.0083
-797
86.4849
Scale-S
154.1052
.969
159.OO36
ScaIe-6
80.0545
1 .O50
76.192 1
Scale-7
1 145909
1 -273
90.0 151
Scale-8
276.7452
1.014
772.8252
The mean scores the clusters had on al1 of the variables input into the cluster
analysis are presented in Table 9.
'able 9: Mean Scores on Attitudinal Variables Used in Clusi
Information InvoIvement
24W+
1 like to know as much as I can about the medication the
doctor prescribes for me.
1 feel better taking h g s when 1 am knowledgeable about
24M+
them.
24A+
1 usually ask the doctor questions about the h g
prescnbed at the tirne of my medical visit.
24DDI It would be helpful if the doctor provided written
information about the dmg hdshe prescribes.
1often like to get information about my medication h m
24I+
books and other M n e n materials.
When there is more than one medication to mat my
24F+
condition, 1 should be told about each one.
24T+
1 want information about my prescription h g so 1 can
determine if the medication is working or not
Information Avoidance
24N+
24CC+
24U+
24EE+
24K+
243+
Information about my medication is too hard for me to
understand.
The doctor uses words 1 don't understand when telling
me about the drug helshe is prescribing.
1 feel 1 don't know enough about my medication to make
informed choices about which medication 1 should taJce,
The pharmacist is too far away behind the counter for me
to ask questions about the dmg(s) helshe prescribes.
There is not enough privacy at the phannacy counter for
me KI ask questions about the medication 1 have been
prescnbed.
1 don't think doctors know enough about the dmgs they
prescri bc.
Self-kured Knowledge
24X+
24L+
24Y+
1 feel 1 know more about my condition than do other
people expexiencing the same heaith condition as me.
1 feel 1 know more about my medication [han do other
people who cake the same medication as me.
1 feel 1 know where to find ail the information 1 need on
my medication.
SeK-Cam OrientaLion
24AA-
24V24H+
248+
24I+
1 wouId rather have the doctor make the decision about
my matment than for him or her to give me a whole lot
of choices.
It's always better to seek professional help than to try to
mat yourself.
It is bener to rely less on doctors and more on your own
rommon sense when it cornes to caring for your body.
It is helpfui to ask my friends or family questions about
the dmgs 1 am taking
1 often like to get infonnation about rny medication(s)
from books and other written materials.
Self-Glue Orientation (cont'd)
24J+
1 don't think doctors know enough about the dmgs they
prescribe.
24BB+
When there is more than one medication to treat my
condition, 1 should be allowed to choose which
medication 1 want to take.
24PThere is no need to ask questions about pmcription h g
if you trust the doctor.
ManufacturerOriented Information
24S+
h g companies ought to infom consumers about hedth
issues and medications.
24ib
The Company who makes the dmg shouId tell me what 1
need to know about my medication.
2 4 D b It would be helpful if the doctor provided written
information about the dmgs hdshe prescribes.
24T+
1 want information about my prescription h g so 1 can
detemine if the medication is working or not.
Doctor-Orienteci information
24E+
My docior is generally open to questions about the dmgs
he or she prescribes.
24R+
The doctor always provides me with infoxmation about
the medication he or she prescribes for me.
24A+
1 usually ask the doctor questions about the dmg
prescribed at the time of my medical visit.
Pbarmacist-Orienteô information
The p h m a c i s t always provides me with information
about the medication he or she gives me
24G-e
1 usually ask the phamacist questions about the dmgs
when I am having the prescription filled.
24EEThe pharmacist is too far away behind the counter for me
IO ask questions about the dmg prescribed.
Patient ûecision Making
24C+
242+
24T+
24BB+
1 want information about rny prescription dnig so 1 can
decide if 1 should take the medication.
1 want information about rny prescription dnig so 1 cm
detemine if the medication is working or not
When there is more than one medication to treat my
condition. I should be allowed IOchoose which
medication 1 want to take.
A description of the three cluster segments is presented below:
4.4.1
System Skeptics
This group was composed of 133 prescription drug users or approxirnately 3 1% of
the sample. Consumers in Cluster #1 (System Skeptics) tended to exhibit a higher level
of information avoidance than those in the other two clusters. They tended to perceive
barriers to receiving information from health professionals. and lacked confidence in their
ability to make informed health care decisions. They were most likely to feeI the
information was too hard for them to understand, to claim that doctors used words they
didn't understand, or that pharmacists were too far away to ask questions about the drugs
they had been prescribed (Table 9). In addition, the System Skeptics tended to be less
positive about the role of doctors and pharmacists in educating patients about medications
compared to the other two clusters. They were the least likely to feel that both docton
and pharmacists provided them with information about their medication. and they were
the least likely to ask questions about their medication of these professionals.
4.4.2
Confident Decision Makers
This group was composed of 166 prescription drug users or approximately 38% of
the sample. Consumers in Cluster #2 (Confident Decision Makers) tended to be more
proactive when it came to decisions about their health. They tended to want to know as
much as they could about their rnedications and felt more cornfortable taking their
rnedications when they were knowledgeable. They also tended to be confident in what
they knew. They were more likely to perceive themselves to be more knowledgeable
about their medication and health compared to the rest of the population. Confident
Decision Makers appeared to be more empowered when it came to decisions about their
health. They tended to want to take the responsibility on thernselves. radier than have
someone else make the decisions for them. They were more likely to find ii helpful to ask
their fiiends and family questions about their medications, and liked to read books and
other written materials to get as much infonnation as they could. They also appeared to
be receptive to information from a variety of infonnation sources -- d i ~ c t l yfrom the
manufacturer, as well as from traditional sources, like doctors and pharmacists. Most
irnportantly, they more often wanted to have the final Say; they wanted to decide whether
they should take the medication, to determine whether ir was working or not, and to
decide which medication they should take among the available alternatives.
4.4.3
Uninformed Followers
This group was composed of 131 prescription drug usea or approximately 30% of
the sample. Consumers in Cluster #3 (Uninformed Followers) did not perceive barriers to
receiving information about medications and their health, nor did they want to bear the
responsibility of making medication decisions themselves. They were the most likely to
feel that it was better to seek professional help than to try to treat themselves. and felt that
there was no need to ask questions if you ûusted your doctor. This group of consumers
was most confident in placing this in the hands of traditional healthcare providers docton, and to a lesser extent. pharmacists. They appeared to be less interested in
receiving any infonnation directly from drug manufacturers.
4.5
Cross Tabulations
Cross-tabulations were performed to determine if respondent membership in a
cluster was supported by each of two reported behaviours. The variables used were
related to the propensity to ask a doctor to prescnbe a specific dmg, a s well as to ask a
pharmacist to dispense a generic dmg over a brand name prescription medication.
The results indicate that the Confident Decision Makers (5 1%) were more likely
to ask a doctor to prescnbe a specific medication over System Skeptics (37%) and
Uninformed Followers (40%), which is consistent with their propensity to be involved in
decisions related to their health. Table I O presents the results from this cross tabulation.
The chi-square was statistically significant, with a value of 7.18. 2 df and a significance
level of .O3
Table 10: Ever Asked Doctor to Prescribe Specific Medication
System Skeptics
Confident
Decision
Makers
Uninfomed
Followers
Row
Toul
1
Yes
No
Count
Row Pct
Col PCC
Total Pct
49
26.3
36.8
11.4
85
45.7
51.2
19.8
52
28 .O
39.7
12.1
Count
Row Pct
Col Pct
Total Pct
84
34.4
19.5
81
33.2
48.8
18.8
79
32.4
603
18.4
133
30.9
166
38.6
30.5
Column
Total
63.2
131
186
13.3
234
56.7
430
100.0
Consistent with their greater likelihood of asking a doctor to prescnbe a specific
medication, the results also indicated that Confident Decision Makers (59%) were more
likely to ask a pharmacist to dispense a genenc medication than System Skeptics (43%)
and Uninformed Followers (43%). Table 1 ï presents the results from this cross
tabulation. The chi-square was statistically significant. with a value of 10.59, 2 df and a
significance level of .01.
Table 11: Ever Asked Pharmacist For Ceneric Substitution
Yes
No
Systern Skeptics
Confident
ûecision
Makers
Uninformed
Followers
Count
Row Pct
Col Pct
Total Pct
56
26.7
42.7
13.1
97
46.2
59.1
22.7
57
27.1
33.2
13.3
Count
Row Pct
Col Pct
Total Pct
75
34.6
573
17.6
67
30.9
40.9
15.7
34.6
56.8
17.6
2 17
50.8
Column
Toial
30.7
131
1 64
38.4
132
30.9
427
100.0
75
Row
Total
2 IO
49.2
The differences among the groups are statistically significant and in the expected
direction (e.g., a Confident Decision Maker is more likely to ask for a generic drug than
an Uninfomed Follower), but these differences are not as large as might be expected.
However, since the self-reported behavioural data supports the attitudinal data (i.e..
Confident Decision Makers are more likely to be deciding on their medication), the
attitudinal data has at least some validity.
4.6
Discriminant Analysis
Overail, the results from the three discriminant analyses indicated that thex were
significant differences across the cluster groups in tems of their reported information
search behaviour. demographics, and characteristics d a t e d to their health.
4.6.1
Behaviourai Measures as Predictors
The 12 behavioural measures used in the discriminant procedure were as follows:
1.
2.
3.
4.
5.
6.
7.
8.
9.
1O.
II.
12.
counselled by pharmacist about drug;
counselled by doctor about drug;
counselled by doctor about condition;
counselled by phamacist about condition;
asked questions of pharmacist about condition;
asked questions of doctor about condition;
number of sources used for information about condition;
searched for information on condition;
number of sources used for information about dmg;
asked questions of pharmacist about drug;
asked questions of doctor about drug;
searched for information on dmg.
For the first discriminant procedure which used behavioural measures to predict
group membership, two discriminant functions were calculated, with a combined
x2(24)=87.28,p c.01. After rcrnoval of the first function, there was still a strong
association between groups and predictors, x2(11)=28.39, p c.01. The two discriminant
function accounted for 68% and 3296, respectively, of the between-group variability
(Table 12).
Table 12: Canonical Discriminant Functions For Behavioural Variables
Chi-Square
Function
Pe-nt of
CurnuIative
Wilks'
Vanance
Percentof
Lambda
Variance
df
Signi ficance
Level
1
68.3 1
68.3 1
0.8 1
87.28
24
.O0
2
3 1.69
100.00
0.93
28.39
1I
-00
Examination of the group centroids shows the first discriminant function
rnaximally separates Cluster 1, the System Skeptics. from the other two cluster groups.
The second discriminant function discriminates cluster 3, the Uninforrned Followers,
fiom cluster group 1 and group 2.
Table 13: Canonical Discriminant Functions Evaluated At Croup Means
Cluster Croups
Function 1
Fwiction 2
I
-56624
.O805 1
The loading ma& of correlations between predictors and discriminant hnctions,
as shown in Table 14, suggests that the best predictors for distinguishing between cluster
1, the System Skeptics and the other two groups (first function) are counselling activities
conducted by the phannacist about the drug (COUNLPH)and about the condition
(CNSLPHCN),and counselling activities by the doctor about the dmg (COUNSLDR) and
condition (CNSLDRCN).Cluster 1, the System Skeptics are less Iikely to report that they
were counselled by their phannacist about the drug they were prescribed (mean=3.12)
than Confident Decision Makers (mean=5.54) or Uninformed Followers (mean=5.06), as
well as their doctor (mean=4.37) versus Confident Decision Makers (mean=5.55) and
Uninformed Followers (mean=5.77). They are also Iess likely to report being counselled
by their phannacist (mean=. 16) or doctor (mean= 1.83) about the condition for which they
are being treated. (Table 15).
Table 14: Results of Discriminant Function Analysis of Information Search Behav
Univariate
Correlations of
predictor variables
F(2.409 )
with discyiminant
functtons
Predictor Variables
1
2
Counselled b Pharmacist
abouibu
(COUNLP~)
91
-.M
26.6530
Counselled bv Doctor
about DiIl
(coUNSL&>
55
-.29
10.9716
1
1
1
Counselled by Doctor
about Condition
(CNSLDRCN)
1
Counselled by Pharmacist
about Condition
(CNSLPHCNI
Asked Pharmacist
about Condition
(ASKPHCN)
-15
-16
1.0633
Asked Doctor about Condition
(ASKDRCN)
-.I7
.O 1
-9507
No. of.Sources Used for
Information about Condition
(SRCHCON#)
.15
.62
63755
Searched for Information
on Condition
(SRCHCOND)
-14
.60
5.9682
No. of Sources Used for
Information about Drug
(ANYINF#)
.19
.45
4.1859
- .O2
36
4.6298
-1 I
38
2.5403
Asked Phmacist about h
(QUESTPH
1
Searched for Information
about Dru
~YINF&
~ s k e dDoctorabout h g
(QUESTDR)
g
1
-.O8
(
-46
1
3.3248
Table 15: Croup Meam by Predictor Variables.
Cluster Groupings
Predictor Variables
1
2
3
Counselled b Pharmacist
abou<L
3.12
554
5 .O6
Counselled by Doctor
about Dru
(coUNSL~~)
4.37
555
5.77
Counselled b y J?octor
about Conditton
(CNSLDRCN)
1.83
2.4 1
2.43
Counselled b y P m a c i s t
about Condition
(CNSLPHCN)
-16
-50
-44
No. of.Sources Used for
Information about Conhtion
(SRCHCON#)
-73
-97
.54
Searched for .formation on
Condition
-40
-5 1
.3 1
No. of Sources Used for
Information about Drug
(ANYIM;#)
-62
.86
-55
Asked Pharmacist about Dnrg
50
.56
27
Searched for Information about
39
-47
.34
.74
.74
-44
(COUNLPA)
L
4
(SRCHCOND)
(QIIESTPH)
~~0~
L
Asked Doctor about Drug
(QUESmR)
r
On the second discriminant function. which separates cluster 3, the Uninfomed
Followers from cluster 1 and 2. predictors related to information search behaviour. such
as whether or not search was conducted for either the drug prescnbed or the condition
being treated (ANYINFO: SRCHCOND.respectively), as well as the number of sources
sought for information were the best predictors for distinguishing between Cluster 3
venus the other two groups (ANYINF#; SRCHCON#, respectively). As shown in Table
15, the Uninfomed Followen were less likely to search for information about the
condition for which they were being treated (mean = . 3 l ) than Systern Skeptics (mean =
-40)and Confident Decision Makers (mean = -5 1). and cited fewer sources when they
conducted information search about their condition (mean = 254) than the other two
groups (mean = .73; mean = .97. respectively). This group is also less iikely to engage in
search for information about the dmg they were prescribed (mean=.34) than System
Skeptics (mean = .39) and Confident Decision Makers (mean = .47), and cited fewer
sources when they conducted information search about the drug (mean = -55vs. Cluster
1=.62 and Cluster 2=.86). Cluster 3, the Uninformed Followers were also less likely to
ask questions of their pharmacist (QUESTPH) (mean=.27) or doctor about the dmg they
were prescribed (QUESTDR) (mean=.44) than the other two ciusters (mean=.50; mean =
.56 respectively for pharrnacist; mean=.74; mean=.74 respectively for doctor).
The classification results indicate that a total of 52.2% of the cases were correctly
classified, compared to 37.6 % that would be correctly classified if al1 respondents
belonged to the largest cluster grouping, or 33.6% with a classification accuracy achieved
by random or chance grouping.
Table 16: Classification Results for Behavioural Predictors
Actual Groups
Group
Predicted Croup Membership
No. of Cases
1
2
3
I
131
71
54.2%
37
28.28
1 7.6%
2
155
34
21.9%
89
57.4%
32
20.6%
3
126
27
21.4%
44
34.9%
55
43.7%
23
1
Percent of "grouped"cases comcctly classified: 52.18%
The stability of the classification procedure was checked by a cross-validation mn.
Approximately 25% of the cases were withheld from calculation of the classification
functions. For the 75% of the cases from whom the ~Iassificationswere derived, there
was a 52.4%correct classification rate. For the cross-validation cases, 42.4%were
correctly classified, compared to 33.6% that would be achieved by chance or random
grouping.
Table 17: Classification Results for Cases Selectec for Use in the Analysis
1
Actual Groups
I
1
I
Croup
I
1
M i c t e d Group Membership
No. ofCases
94
Percent of "grouped"cases correctly dassified: 5238%
Table 18: Classification Results for Cases Not Selected for Use in the Analysis
Group
1
1
Actual Groups
1
Predicted Group Membership
No. of Cases
I
2
3
I
37
18
48.696
13
35.18
6
16.2%
2
41
24.4%
10
16
39.0%
36.6%
3
40
14
35 .O%
1O
25.0%
16
40.046
Percent of "grouped"cases correctly classified: 4237%
15
4.6.2
Demographic Measures as Predictors
The 13 demographic measures used in the discriminant procedure were as
follows:
employment status (white collar vs. blue collar):
household size;
gainfully employed in the workforce or not;
gender;
education;
Iive in British Columbia;
live in Quebec;
live in Atlantic provinces;
live in Prairie provinces;
live in Ontario;
age;
marital status;
household income;
in the second run of the discriminant procedure. demographic variables were used
to see if demographic indicators could be useful in predicting cluster membership.
Though the two discriminant functions generated were not significant at the 95%
confidence interval, a greater proportion of prescription dnig users were conectly
classified than could have been expected by chance (47.7% correctly classified versus
33.7% by chance).
Tabie 19: Classification Results for Demographic Predictors
1
Group
1 Percent
1
1
Actual Croups
1
Predicted Group Membership
No.of Cases
1
2
3
I
133
44
33.1 %
60
45.1%
29
2 1.8%
2
165
29
17.6%
1 07
64.8%
29
17.6%
3
130
16
12.3%
46.9%
-
-
of "gmupdmcases correctly classificd 47.66%
61
53
40.8%
Using the demographic indicators. two discriminant functions were calculated.
with a combined x2(24)=35.56. p C. 10. After removai of the first function. there was no
strong association between groups and predictors. The Tint discriminant function itself
accounted for 64% of the between-group variability (Table 20).
Table 2û: Canonical Discriminant Functions
Function
Pe-nt of
Vmance
Cumulative
Percent of
Variance
WilksO
Lambda
Chi-Square
df
Significance
tevel
1
64.09
64.09
0.92
3556
24
.O6
2
35.9 1
100.00
0.97
12.87
1i
30
Examination of the group centroids shows the first discriminant function
maxirnally separates Cluster 3, the Uninformed Followers. from the other two cluster
groups.
Table 21: Canonical Discriminant Functiom Evaluated At Croup Means
Cluster Croups
I
Funcuon t
I
Function 2
The loading matrix of comlations between predictors and discriminant functions.
as seen in Table 22, suggests that the best predictors for distinguishing between cluster 3.
the Uninformed Followers and the other two groups (fust function) are employment
status (STATUS-N) (i.e. white collar vs. blue collar). household sire ( H H S I E ) . whether
respondent is gainfully employed, unemployed or retired (EMPLOY-N). age
I
(RESP-AGE) and gender (VAR40). As shown in Table 23. Clusrer 3, the Uninformed
Followen are more iikely to be retired (rnean=3.24) than the System Skeptics
(mean=2.95) and Confident Decision Makers (mean=2.74), tend to live in smaller sized
households (mean=2.24), and are less likely to be employed in the workforce (mean= 1-42
vs. 1.47 for System Skeptics and 1.56 for Confident Decision Makers). Uninfomed
Followen also tend to be slightly older than the other two cluster groups. bom in 1939
venus 1945 for System Skeptics and 1944 for Confident Decision Makea, and are less
likely to be female (mean=1.72). The other discriminant function was not interpreted
because it did not approach statistical significance at either the 95% or 90% confidence
interval.
Table 22: Results of Discriminant Function Analysis of Demographic Variables
1
1
Predictor Variables
I
(HHSIZE)
Houehoid
Sue
1
Correlations of
mdictor variables
I
l
1
1
-53
1
1 Univariate
R2.425 1
2
1
-16
1
4.7191
GENDER
WARW
-Al
-,
18
2.204 1
Resident of 6.C
-27
-.22
12113
Education Level
(RESP-LN)
-21
.W
.5 109
Resident of Atlantic Provinces
-.O4
.O 1
.O222
(RESP-EE) -51
-56
5.1476
Resident of Prairies
(PRAIRIES)
- .O9
-43
1.3096
Marital Status
(MAUT-N)
-.19
-33
1.1493
Household lncome
(HHI)
.O7
-22
.3798
(BC)
Residentof uebec
(QUEB&
(ATLANTIC)
Age of Res ndent
1
Resident of Ontario
(ONTARIO)
(
-08
1
-.I2
(
.l766
Table 23: Gmup Means by Demwaphic Predictor Variables
Cluster Groupings
I
h
Predictor Variables
I
2
3
Em loyment Sianis
&TATUS-N)
2.95
2.74
3 -24
Household Size
(HHSIZE)
2.67
2.6
2.24
GainfuiIy
Empfo ed/Unem loyed
1.47
156
I -42
GENDER
WARW
Age of Res ndent
1 -76
1 -83
1.72
1944.08
19395
&PLOY-PI)
(RESP-EE)1945.78
The stability of the classification procedure for the demographic discriminant
analysis was checked by a cross-validation run. Approximately 25% of the cases were
withheld from calculation of the classification functions. For the 75% of the cases from
whom the classifications were derived, there was a 45.3% correct classification rate. For
the cross-validation cases, 42.5% were correctly classified. However, the development of
an accurate function was not possible at an appropriate confidence level using crossvalidation. Therefore. the results can not be confidently be considered stable.
4.6.3
Situational Measures as Predictors
Situational measures refer to elements that describe the outcornes and processes of
prescription dmg use. The 15 situational measures used in the discriminant procedure
were as follows:
1.
2.
3.
4.
5.
6.
level of satisfaction with information given by the pharmacist about the dmg;
level of satisfaction with information given by the doctor about the drug;
level of satisfaction with information given by the pharmacist about the condition;
level of satisfaction with information given by the doctor about the condition;
overall level of knowledge about the drug prescribed;
received written information from the doctor about the drug;
received written information from the pharmacist about the drug;
received written information from the doctor about the condition;
received w ~ t t e ninformation from the pharmacist about the condition;
illness from which patient suffers:
number of visits to doctor in last year.
type of drug purchase:
type of condition (chronic vs. episodic);
number of drugs taking;
drug plan coverage;
The third, and final, discriminant mn used charactenstics related to the
respondent's health, and their satisfaction with elements related to their health in order to
determine if these variables could be used to predict group membership. Through this
procedure. two discriminant functions were calculated, with a corn bined x2(48)=
133-40, p
c.01. After removal of the first function, there was no strong association between groups
and predictors. The first discriminant function accounted for 84% of the between-group
variability (Table 24).
!
24: Canonical Discriminant Functions For Situational Variables
Function
Percent of
Variance
Cumulative
Wilks'
Lambda
I
84.16
84.1 6
2
15.84
100.00
Percent of
Variance
Signifrcmce
ChiSquare
df
0.60
133 -40
48
-00
0.9 1
24.33
23
-39
Level
Examination of the group centroids shows the fint discriminant function
maxirnally separates Cluster 1, the System Skeptics, from the other two cluster groups.
Table 25: Canonical Discriminant Functions Evaluated At Group Means
Cluster Croups
Function I
Function 2
1
1 . 1 1946
-.O0903
2
-.44549
.32512
3
-.47747
-.4Z102
The loading maîrix of comlations between predictors and discriminant functions,
as seen in Table 26. suggests that the best predicton for distinguishing between cluster I l
the System Skeptics and the other two groups (first function) are satisfaction levels with
the information given by the pharmacist and doctor about the dmg, and about the
condition. as well as knowledgeability about the clmg prescnbed. Anaiogous to their
name. Cluster 1. the System Skeptics tend to be more dissatisfied with the information
provided by traditional health care providers about the dmgs they prescribe or dispense.
and about the conditions for which they m a t As shown in Table 27. this group is less
inclined to report high levels of satisfaction with the pharmacist in terms of the
information provided about the drug (mean=2.2 1, where l=very satisfied 4= not at al1
satisfied), as well as about the condition for which they are being treated (mean=2.64).
The System Skeptics tend to be dissatisfied with the information provided by their doctor
about the drug they were prescnbed (mean=2.23) and about the condition from which
they suffer (mean=2.15) (Table 27). Possibly as a function of the dissatisfaction they
express with traditional health care professionals, they also report the lowest level of
knowledge about the drugs they were prescnbed (meand. 10)compared to Confident
Decision Makers (mean=5.20) and Uninforrned Followers (mean=4.96).
Table 26: Results of Dixriminant Function Analysis of Health Characteristics
Predictor Variables
Satisfaction with information iven by phamacist
about drug
IB)
Satisfaction with information iven by doctor about
d m g (VARA A)
Satisfaction with infoyation iven b pharmacist
about condition $AR 18d)
Overali level of knowled e &out dmg prescribed
(VA1
(vA7)
Satisfaction with infolmation iven by doctor about
condition (V 1 8 ~ )
Received wrïtten information fmm doctor about dnig
(VAR IOA)
Suffer from Res irato problems
di
(RESB~RA~
Received written information from pharmacist about
dnig (VARIOB)
Number of times visited doctor in last year
NAR321
Received wrîtten information from doctor about
condition (VAR 17A)
Type of dnig purrhase (VAR2)
Received wtitlen infornation from harmacist about
conditton (VAR1
Type of condition chronic vs. short-tem (VAR6B)
Suffer frorn dermatolo ical problerns
(DERMAT~L)
Suffer h m rniscellaneous illnesses
d)
(OTHER)
Nurnber of dmgs taking at given time (VAR33)
Suffer h m OblGyn problerns (OBGYNE)
Suffer fiom Bowel problerns
(BOWEL)
Suffer frorn Musculoskeletal problems
(MUSCULO)
Covered b dnig plan
Univariate
F(2.4 15)
Correlations of
p d i c t o r v-ariables
with discriminant
functions
2
I
-0.1
0.7 1
,
36.024 1
0.58
0.04
24.1717
0.42
0.09
12.8267
439
0.2 1
1 1.4448
0.39
O. 14
10.8569
0.28
0.05
5.4905
I
I
"-" I
0.18
0.06
2.3778
-0.17
-0.02
2.0752
-0.11
2.0772
0.15
0.15
-0.09
0.1 3
1.8796
1.624
0.14
037
3.O989
0.02
0.33
1.4813
-0.03
-0-3
1.3127
4-23
0.05
-0.26
0.25
-0.23
4.7864
1 .O202
1.1844
0.16
4.08
'
'
-
4.02
0.2 1
0.63 14
0.05
0.2
0.7 182
-0.03
0.16
0.4 154
4.03
0.1 2
0.24 14
0.0 1
-O. f
0.1 1
-0.1 1
0.1861
0.8987
O
0.04
0.02 12
(vAK34)
Suffer from Endocrine problems
(ENDOCRIN)
Suffer from Nutrition pmblems
(
N
m
m
Suffer from Pain problems ( P A N )
Suffer frorn Cardiovascular problems
(CARDIO)
Suffer from Mental Health problems
(MENTAL)
'
1
Table 27: Group Means by Predictor Variables
I
1
PRdictor Variables
Satisfaction with-information
given by pharmacist about dmg
(VARI IB)
1
l
Cluster Groupings
1
1
2
1
3
2.2 1
1.41
1.45
2.15
1.73
1.64
1
---
Satisfaction with information
given by doctor about dmg
(VARI I A)
Sahsfaction with in-nnation
given by phmacist about
condition
(VARI SB)
Overall level o f knowledge
about dm rescribed
WU7,
Satisfaction with infonnatjon
given by doctor about condition
(VAR1 SA)
The classification results indicate that a total of 52.2% of the cases were correctly
classified, compared to 38.5 % that would be comctly classified if al1 respondents
belonged to the largest cluster grouping, or 33.7% with a classification accuracy achieved
by random or chance grouping.
Table 28: CIassification Results for Situational Predictors
Predicted Group Membership
Actual Groups
No. of Cases
Gmup
7
3
I
129
77
59.7%
34
26.46
18
14.09
2
16 1
26
16.1%
99
61.5%
21-48
3
128
10
70
54.78
32.8%
1 2.5%
36
42
Percent of "grouped"cases correctly classified: 52.15%
The stability of the classification p r o c e d u ~was checked by a cross-validation run.
Approximately 25% of the cases were withheld from calculation of the classification
functions. For the 75% of the cases from whom the classifications were derived, there
was a 59.0% correct classification rate. For the cross-validation cases, 40.5% were
correctly classified, though the sarnple size for this classification procedure was too small
for accurate classification.
Table 29: Classification Results for Cases Selected for Use in the Analysis
Gmup
I
I
Actual Gmups
1
62
64.6%
96
1
1
Predicted Gmup Membership
25
26.0%
9
9.38
1
I
Percent of "grouped"cases correctly classified: 58.96%
Table 30: Classification Results for Cases Not Selected for Use in the Analysis
1
1
Actul Gmups
No. of Cases
Gmup
I
33
2
42
3
36
1
Predicted Gmup Membership
1
2
3
424%
I4
11
33.3%
8
24.2%
6
14.3%
45.2%
5
13.9%
19
19
52.8%
17
30.55
12
33.3%
Percent of "grouped" cases correctly classified: 40.54%
4.7
Logistic Regression
In addition to the discriminant analysis pmcedures, a logistic regression was
conducted in order to better identify the demographic variables that discnminated arnong
cluster groups. This procedure used cluster membership as a dichotomous dependent
variable and both continuous and categoncal demographic indicators as independent
variables.
Logistic regression was used in this case because the demographic variables were
non-normally distributed. However, in order to accommodate 3 cluster groups as the DV,
three separate analysis had to be performed. The fint mn of the logistic regression
procedure used cluster group 1, the Systern Skeptics and cluster group 2, the Confident
Decision Makers as the dependent variable. The second logistic regression analysis used
cluster group 1. the System Skeptics and cluster group 3, the Uninformed Followers as
the DV, while the third, and final, exarnined cluster group 2 and cluster group 3 as the
dependent variable.
The first run used cluster 1 and cluster 2 as the dichotomous dependent variable,
and the following demographic indicators as independent variables:
gender;
household size;
household income;
age;
marital status;
education level;
empioyment status;
and, province of residence.
The likelihood value (-2LL) examining cluster 1 versus cluster 2 was calculated to
be 392.96. According to Hair et al. (1987), a small-2LL value indicates a well-fitting
model. An assessrnent of the fit of the model can also be assessed by creating a
classification table which compares the actual events versus the predicted values. As
seen in Table 3 1, 63.4% of the cases are correctly classified. However, a more detailed
examination of the correct classification for the individual groups indicates that cluster 1
was not predicted as well by the independent variables as cluster 2. Cluster 1, had a
correct classification rate of only 45.996, compared to 77.6% correct classification for
Cluster 2.
Table 31: Classification Matrix Using Demographic indicators to Predict Group Membership
1
I
1
I
Observed
I
Predicted
I
1
1
I
Comct Classification
2
1
1 Overail classification: 63.42%
Two individual predictors, gender and age contributed significantly to the
classification of cluster 1 versus cluster 2.
The second logistic regression that was run used cluster group 1 versus cluster
group 3 as the dichotomous dependent variable. and the demographic variables as
independent variables. For this analysis. the likelihood value (-2LL) exarnining cluster 1
versus cluster 3 was calculated to be 347.15. An assessrnent of the fit of the mode1 was
also assessed by examining the classification table comparing the actual events versus the
predicted values. As seen in Table 32, 63.1% of the cases are correctly classified. Both
Cluster I and Cluster 3 had a correct ciassification rate of 63.1%.
Table 32: Classification Matrix Using: Demographic Lndicators to Predict Group Membership
I
I
Predicted
Observed
1
3
Comct Classification
I
84
49
63.16
3
48
82
63.O8
1 Overall classification: 63.12%
I
1
The only predictor that was significant at the .O5 level was the respondent's age.
The third, and final, analysis used cluster 2 versus cluster 3 as the dichotomous
dependent variable. In this case, the likelihood value (-2LL) examining cluster 2 versus
cluster 3 was calculated to be 380.17, An assessrnent of the fit of the mode1 was also
assessed by cnating a classification table which compared the actual evenü versus the
predicted values. As seen in Table 33,6 1.7% of the cases are correctly classified.
However, a more detailed examination of the correct classification for the individual
groups indicates that cluster 3 was not predicted as well by the independent variables as
cluster 2. Cluster 3, had a correct classification rate of only 40.0%. cornpared to 78.8%
correct classification for Cluster 2.
Table 33: Classification Matrix Using Ikmographic Indicators to Predict Group Mernbership
Predicted
Observed
2
1
3
Correct Classification
1 Overall classification: 6 1.69%
Three individual predictors. gender, household size, and residence in the province
of Quebec contributed significantly to the classification of cluster 2 versus cluster 3.
4.8
Multinominal Logit
Multinominaf logit was used to confirm the logistic gre es si on analyses.
According to Tabachnick & Fidell (1989), logit analysis is better in situations where the
dependent variable has more than 2 categoties because it has no pararnetric assumptions
1
that are likely to be violated. in the logit model, the 3 cluster grouping was used as the
DV,and the demographic indicators were used as IVs. The demographic indicators used
were as follows:
gender;
household size;
household income;
age;
marital status;
education levei;
employment status;
and, province of residence.
The overall model was not significant at the 90% or 95% confidence interval (x2=29.5,
It is possible that in the original analysis a significant result was obscured by error
variance from irrelevant IVs. Specifically. the original analysis was conducted on 13
independent variables. which resulted in 22 degrees of freedom. If a similar chi square
result can be obtained with less degrees of frccdom (Le. less variables). selected
demographic variables rnay indeed predict cluster membership. Consequently. a second
analysis was conducted using a reduced set of IVs. It was hoped chat by focusing on
fewer variables significant results would not be obscured by "noise" from erroneous IVs.
The IVs used in this analysis were as follows:
gender;
household size;
household income;
age.
The overall mode1 was significant at the 95% confidence interval (x2=21.1, d.f.=S.
~ ~ 0 5 ) .
As shown in Table 34, Cluster 1, the System Skeptics, are defined only by their
year of birth. Members of this group are more likely to be bom in the 1960's (36.24%)
than in the 1930's (27.49%). Gender, household size, and household income do not
predict membership in Cluster 1. Cluster 2, the Confident Decision Makers, are defined
by gender and household size. Members of this group are less likely to be represented by
men (29.15%) compared to women (41.94%), and are more likely to live in dwellings of
4 or more people (44.55%)than single person dwellings (32.87%). Age and household
income do not predict membership in Cluster 2. Cfuster 3. the Uninfomed Followers,
are defined by gender, year of birth and household size. They are more likely to be
represented by men (36.48%) than women (28.04%), are more likely to be bom in the
1930's (33.1 1%) versus the 1960's (25.938). and are more likely to live in single person
dwellings (37.86%).
Table 34: Prodictecl Percenîages Based on Coefficients from LIMDEP Output
1 Base
I
Clrister 1
I
Cfmter 2
l
3 1-15
l
38.92
I
Cluster 3
I
29-94
I
Gaidcr:
Men
3437
29.15
36-48
Women
30.02
4 1 -94
28-04
Boni in 1930
27.49
39.4
33.1 1
Born in 1950
33.21
3853
28.26
Born in 1960
36.24
37.83
25 -93
4 or more
32.18
4455
23 -27
Y a r Born:
V.
DISCUSSION
In this section of the report, a summary of the results and conclusions will be
presented, along with a discussion of the implications and recommendations flowing from
the results.
5.1
Introduction
The purpose of the research was to identify whether distinct segments of
prescription drug users existed by classifying consumers according to their sirnilarity on
attitudinal dimensions related to their health. prescription dnigs, and the conditions from
which they suffer. Following this, the goal was to profile these segments on demographic.
behavioural and situational descriptors to better understand the composition of each cluster
grouping. It was thought that such a typology would be useful in providing a descriptive
account of how various groups of prescription drug usea think and act. thereby, enabling
various stakeholden to develop marketing communications and consumer educational
programs aimed at the different target groups. The main results of this study lead to the
conclusion that, in the sample studied, there are different segments of prescription drug
usen, which c m be inferred from their similarity on attitudinal dimensions. However.
traditional demographics variables only weakly predicted group membership. This
weakness will hinder efforts to identify and thus ultimately influence System Skeptics.
Confident Decision Makers, and Uninformed Followers. Keeping this in mind. the
following section will discuss how different communications programs and educational
progmns could be developed to reach each of the target groups identified in this study. and
how the results. in particular, may be useful for d i f f e ~ nstakeholder
t
groups.
5.2
Attitudinal Dimensions
Analyses of the 32 attitudinal variables indicated that. for this population of
prescription dmg users. there were eight psychographic dimensions. These dimensions
comsponded to infomation involvement, information avoidance, self-assured knowledge.
self-care orientation, manufacturer-oriented information, doctor-oriented information,
pharmacist-onented information. and patient decision making.
Al1 the variables loading highly on the fmt psychographic dimension. information
involvement, stressed concem about acquiring information about medications either
independently through written matenals, or by asking questions of health professionais.
The second psychographic dimension, information avoidance, was characterized by
barriers. or inhibitors to receiving information about medications. The perceived diff~culty
in understanding the information. the doctor's use of language. the phannacist's lack of
visibility, and the lack of pnvacy at the pharmacy were deemed to represent baniers to
receiving this information.
The third psychographic dimension, self-assured knowledge. was defined by three
items. The three statements that loaded highly on this factor weh the perception that they
are more knowledgeable than others about the condition frorn which they suffer. and of the
medication they take. and in knowing where to fmd al1 the information they need. These
variables. in combination. were deemed to represent the patient's perceived
knowledgeability about prescription medications.
The variables loading highly on the fourth psychographic dimension. self-care
orientation, focused on the desire to take control of one's health and treatment decisions,
rather than relying on doctors to make these decisions on their behalf. Negatively conelated
items on this factor were, "1 wouId rather have the doctor make the decisions about my
treatment than for him or her to give me a whole lot of choices". and 'There is no need to
ask questions about prescription dmgs if you mist the doctor". This factor was characterized
by the patient's desire to take a proactive role in their healthcare decisions, and.
correspondingly. to rely less on traditional healthcare providers.
The fifth psychographic dimension. manufacturer-oriented information was
charactenzed by four variables. Two of the four variables had loadings of .65 or more ( 2 4
"Dmg companies ought to inform consumen about health issues and medications"; 24D
'The Company who makes the dmg should tell me what 1 need to know about my
medication*'),while the other two (24DD"It would be helpful if the doctor provided wriiten
information about the dmgs helshe prescribes"; 24T "1 want information about my
prescription dmg so 1 can determine if the medication is working or not") had significantly
weaker loadings of .34 and .32 respectively. Consequently, this factor was deemed to
principdly represent prescription dmg users' desire for dmg manufacturen to be involved
in providing information about the dmgs they manufacture.
The sixth psychographic dimension. doctor-oriented information. was defined by
three items. The three variables were related to the approachability of the doctor in
answering patient questions, the doctor's role in providing information to patients. and the
patient's assertiveness in asking the doctor questions about the medication prescnbed.
These variables represented the "doctor" factor.
The seventh variable. phannacist-onented information, was also defmed by three
items. These three variables were associated with the pharmacist's role in providing
information to patients, the patient's assertiveness in asking the phannacist questions about
the medication prescribed, and the perceived accessibility of the pharmacist at the pharmacy
counter. These three variables ~presentedthe "phannacist" factor.
The eight. and final. factor, patient decision making, was charactenzed by ihree
items which were related to the patients' desire for making decisions regarding their
medications. These decision making items were 1) whether to take the medication or not.
2) to determine whether the medication was working or not, and 3) in making choices of
which medication to take among a set of available alternatives.
5.3
Psychographic Segments
The cluster procedures resulted in the population of prescription drug users being
classified into three different groups on the b a i s of the similarity of each drug user's score
on each of the attitudinal dimensions. These groups of consurners. together. made up the
typology of prescription drug usee.
5.3.1
Cluster#1 -SystemSkeptics
The System Skeptics were distinguished by their tendency to lack faith in traditionai
heaithca~providers -- narnely. doctors and pharmacists. Aititudinally, they were more
Likely to perceive barries to receiving prescription d m g information, particularly from
health professionais; and appeared to lack confidence in their ability to make informeci
health care decisions. Funher, they expressed more discontent with the accessibility of
doctors and pharrnacists, particularly when it coma to providing information to patients
about prescription medications. They were the least Iikely to feel that both doctors and
pharmacists provided thern with information about their medication, and they were the least
likely to ask questions about their medication frorn these professionals.
The System Skeptics can be profiled dong a number of behavioural. demographic
and situational descriptors that were exarnined. Behaviourally, they were less likely to
report that they receiving counselling from both the doctor and pharmacist regarding their
condition and of the prescription medication they were prescnbed. Further, they were
among the least satisfied with the information that is k i n g provided by doctors and
pharmacists. They also claim to be the least knowledgeable about the dmgs they were
prescnbed -- possibly as a function of their perceived lack of assistance from hedth
professionals. Demographically, age was the only variable that distinguished the System
Skeptics from the other two cluster groups. They tended to be younger than their
counterparts in the other two groups -- bom in the 1960's rather than the 1930's.
5.3.2
Cluster #2 - Confident Decision Makers
The Confident Decision Makers can be distinguished by their more positive and
proactive orientation to decisions about their health. Not only were they more likely to take
an active role in seeking out information about their medications from friends and family
and published materials, they tended to feel more knowledgeable and, expressed comfort in
taking their medications when they have this information in hand. Confident Decision
Makers also more often wanted to play a greater role in treating themselves, and in making
decisions about their medication. in particular, they were more likely to feel they should be
relying on doctors l a s , and relying more on themselves for these types of decisions.
Despite this orientation. they morp often appeared to be receptive to information from a
variety of channels. They felt that manufactures should provide patients with the
information they need to know about their medication, d o n g with information from doctors
and pharmacists. Their willingness to receive information fiom a variety of sources appears
to stem from their desire to make educated health decisions.
The Confident Decision Makers were not predicted well by the behavioural.
demographic and situational descriptors that were examined. No behaviour nor situational
descnpton distinguished this cluster grouping from the other two groups.
Demographically. gender and household size were the only variables that distinguished the
Confident Decision Makers from the other two cluster groups. In particular, they were more
heavily represented by women, and those living in larger-sized households of 4 or more
people.
5.3.3
Cluster #3 - Uninformed FoIlowers
The Uninformed Followers were distinguished by their lack of desire to be involved
in decisions affecting their health. They tended to be much more cornfortable leaving these
decisions to traditional healthcare providers -- namely, docton and pharmacists.
Attitudinally, they were less likely to perceive barriers to receiving prescription dmg
information. However. they less often wanted to bear the responsibility of making
medication decisions thernselves. This group of consumers were more confident in placing
these decisions in the hands of traditional healthcare providers - doctors, and to a lesser
extenc pharmacists.
The Uninformed Followers can be profiled dong a number of behavioural.
demographic and situational descriptors. Behaviouraily, the Uninformed Followers were
the least likely to seanih for information about their condition and of the drug they were
prescribed; and cited fewer sources when they actually did conduct a search for this type of
information. They were also less likely to ask questions of their pharmacist or doctor about
the drug they were prescnbed - consistent with their desin and comfort in leaving these
types of decisions to traditional healthcare providers.
Demographically, employrnent status, household size, age and gender were the
variables that distinguished the Uninformed Followers from the other two cluster groups.
They tended to be older, male, retired, and Living in smaller-sized households.
5.4
Implications and Recommendations
The results of this research have implications for a number of stakeholders,
including govemment, health professionals. pharmaceutical manufacturers, and retailes.
Distinct groups of consumers existed based on their attitudes toward their health.
prescription dmgs, and the conditions from which they suffer. Understanding the motives
that underlie consumer interest or disinterest in their medications and framing
communications that seek to meet the informational needs identified may help to encourage
patients to play a more active role in the decisions affecting their health. However. it is
important to note that efforts to target information appropriately will be hampered by the
poor ability of the assessed demographics to predict the key consumer groups and the
dificulty in identifying people by their attitudes alone.
Given the different objectives of various stakeholders (i.e. government agencies.
health professionals, pharmaceuticals manufacturers. and consurners), the fuidings and
implications of this research differs. The subsequent section will discuss the relevance of
these findings in the context of the objectives of these stakeholder groups: health
ministries, physicians, manufafturers, pharmacists, and consumen.
First, the fedeml and provincial health ministries have an interest in the area of
information search and prescription drug usen. given that their mandate is to fund a system
that improves the health and well-king of the public, to educate the public about proper
dmg utilization. and to reduce healthcare costs. This research identifies that there are
attitudinal differences among prescription drug users - each with a different orientation to
the traditional healthcare system, and the role the patient plays in this system. System
Skeptics tend to be less positive about the role of traditional healthcare providers and want
less involvernent from these professionals, while the Confident Decision Makers want to
take a more proactive role in the decisions involving their health alongside traditionai
healthcare providen. Uninformed Followers, on the other hand, want to leave these issues
in the hands of traditional healthcare providen. Although untested in this research. it is
possible that a relationship exists between a patient's active role in their healthcare
decisions, and their overall health and well-being. It is postulated that if patients play a
more active role. they may be more inclined to provide more information to the doctor at the
tirne of diagnosis or to the phannacist at the time of purchase. in terms of discussing
symptoms. side effects. and the List of the drugs they are taking at the present time so that
complications from dmg interactions can be mitigated. and more educated decisions can be
made frorn the doctor's perspective. if such a positive relationship exists. it may result in
fewer visits to the doctor. thereby reducing health care costs, and may also lead to greater
compliance with the prescribed regimen. Greater cornpliance would be a cost-saving
measure as well. given that roughly 1 3 4 of hospitalizations among the elderly is due to
incorrect use of prescription medications (Morris et al., 1994). if' this relationship is
confirmed through empirical testing, it may be useful to embark on an educational
campaign that moves prescription dmg users towards this proactive role by focusing on the
benefits that can be achieved through active participation in heaithcare decisions.
Second. the physician's mandate is to mat patients by curing illness and promoting
wellness and well-being. This research identifies that there are diffe~ncesbetween groups
in terms of their interactions with traditional healthcare providers. For exarnple. System
Skeptics do not feel they are adequaiely being counselled by their physician about the drug
prescribed and about the conditions frorn which they suffer. while Confident Decisions
Maken want to be told about al1 of their available treaûnent options in order to arrive at an
educated decision in conjunction with their heaidicare provider. Uninfomed Followers. on
the other hand, don? want to be involved in the decisions involving their health; they would
rather leave these decisions in the hands of their physician. Therefore. this research may be
useful to physicians in recognizing that differences exist among different patients. thereby
ailowing them to tailor their "bedside mannef' according to the unique desires of their
patients.
This research suggests that Confident Decision Makers more than iikely want
detailed information at the time a dmg is prescribed. and more emphasis given to discussing
their avaiIable alternatives. On the other hand, Uninformed Followers want to rely solely on
the physician to make decisions on their behalf. They probably don? want a s much
information as the Confident Believers. System Skeptics appear to want information, but
perhaps not from the physician, yet they feel they are not getting proper information from
these healthcare providers. Information outside of the doctor's office. such as videos,
brochures. and diagnostic software may be useful for this segment. Sensitivity should be
given to this segment, however. given their apprehensive attitude towards physicians.
Third, the manufacturer's mandate is to sel1 more drugs by encouraging physicians
to prescribe their drugs over another manufacturer's. This research is useful to
manufacnirea because it identifies that there are difkrences between the groups in tems of
their interactions with physicians, as well as in their intentions to specifically ask for a
specific h g . The goal of encouraging physicians to prescribe a specific dmg could
possibly be achieved by providing appropriate informational materials to physicians so they
can properly address patients' questions. Furthemore, pharmaceutical manufacturers could
develop educational materials that could be passed on to consumers via the physician, with
the airn of encouraging patients to ask for a specific medication. Given that Confident
Decision Makers are more likely than the other two segments to ask a physician to prescribe
a specific medication. rnanufacturers would probably want patients to move towards this
more proactive segment, if they are in fact requesting a product in which they manufacture.
Manufacturers could also use this information to tailor their educational materials
depending on the needs of the patient. For exarnple. Confident Decision Makers are more
likely to want information detailing their available options, including (but not limited to) the
manufacturer's product; while the Uninformed Followers are probably more inclined to
want simple, easy-to-read infomatioii that their physician c m explain to them at the time of
their appointment
Fourth. the phmacist's mandate is to dispense the appropriate medication to
consumers. to encourage corn pl iance and reinforce the information given by the ph ysician.
as well as to encourage repeat purchases at the pharmacy. This research identifies that there
are differences between groups in terms of their attitudes towards phamacists as well.
Sirnilar to their interactions with physicians, System Skeptics do not feel they are
adequately being counselled by their pharmacist regarding the dmg prescribed or of the
condition from which they suffer. They also perceive the greatest barriers to obtaining this
information at the pharmacy due to a perceived lack of accessibility of the phannacist and
of a lack of privacy at the pharmacy counter. Due to these perceived baniers, it rnay be
useful to promote the role of the phannacist by moving hem in front of the counter, thereby
making them appear more accessible. Establishing counselling areas beside the dispensing
counter may also prornote the accessibility of the pharmacist, as well as creating a more
private environment for its customen.
This information may allow pharmacists to tailor their counselling activities
depending on the needs of their customers. Again. Confident Decision Makers more than
iikely want detailed information from the phamacist at the time of purçhase to reinforce the
information supplied by the physician. or if information has not been provided, to educate
them about their available alternatives. Pharmacists that offer such information wiU likely
be more attractive to Confident Decision Makee than phmacists that do not On the other
hand, Uninfonned Followen probably want to rely solely on the information supplied by
their physician. This group simply wants to have their prescription filled. System Skeptics,
on the other hand, appear to want information, but may be better reached through point-ofsale materials (e.g. brochures), video iibraries or selfdiagnostic tests available in the
pharmacy. They rnay also be receptive to the concept of more pnvate counselling areas,
although this is speculative and has not been tested in this research.
Finally, the consumer's goal is to protecf and promote their o w n health and well-
king. This research is useful to patients by virtue of the fact that it identifies that there are
like-minded groups of patients in the Canadian population. sharing the sarne beliefs and
concems as themselves. Knowledge of other people with similar information concerns may
motivate particular patient groups (e.g. Confident Decision Makers) to organize themselves
into discussion or consumer advocacy groups, with the mandate of promoting more active
participation in their heaith. Such advocacy groups will likely enhance the patient's overall
health and well-being by creating an environment which promotes an exchange between
patients and healthcare providers that will allow for more informed decisions to be made.
5.5
Recommendations For Future Research
This section discusses areas of improvement for future research that rnay be
undertaken in the field of information search behaviour and prescription drug users.
Fit, future research should be directed at testing the reliability and validity of the
cluster groupings. In particular. it would be desirable to test the stability of the cluster
groupings by repeating the study at another t h e . Though other natural grouping may have
existed in the data set - which would have clustered subjects into different hornogeneous
categories - of al1 the solutions attempted. the present solution offered the most distinct,
homogeneous and interpretable groupings. Greater confidence could be p laced in the
results if future research validated the results gleaned from this study.
Second. this study was conducted with prescnption drug users who were part of a
consumer mail panel. Consumer mail panels are a self-selected group which may differ in
some relevant and systematic way from prescnption users in general. In the future. it would
be desirable to test the reliability of this research by repeating it with a group of randomly
selected prescription drug users to see if the sarne results are obtained.
Third, the sarnple used in this research was repmented more heavily by those
refilling a prescription medication than those filling new prescriptions (72% vs. 2856). It
could be postulated that users filling a new prescription may hold different attitudes and
engage in different behaviours than those simply refilling or repeating a prescnption.
Future research may want to examine the different strategies employed by these two groups
of prescription drug users. in addition to the proposed usefulness of exarnining differences
between those filling new prescriptions versus those refilling a prescnption. the sarnple was
also comprised prirnarily of prescription dnig users who were taking their medication for
the treatment of
a chronic illness (70%)rather than for an acute or temporary illness (30%).
Given these two issues. it may be useful to segment based on usage patterns rather than
demographics, psychographics, benefits or behaviours in future research studies regarding
information search and prescription drug users.
In addition to using usage patterns as the basis for segmentation, it may also be
useful to conduct a segmentation based on demographics. using age as a key variable. since
older prescription drug users held different attitudes than their younger counterparts. in this
siudy, the sample was comprised prirnarily of elderly prescription drug uses (45%were 55
years of age or older; 2 8 4 were 65+)than non-elderly drug usea. Previous lesearch has
also identified that health professionals interact and provide different information to these
two groups of patients (Morris et al.. 1987). Therefore. future research may ais0 want to
examine differences between younger and older prescription h g users.
Fourth, in the case of scale development, it would be advisable to continue external
validation. Presently, these scales have k e n validated against self-reporteci behaviours of
information search (e.g., the CoriTident Decision Maken who scored highly on information
Involvernent were more likely to report they had searched for information about their drug
and condition and had used a greater number of sources for this purpose). It would further
validate these scales if they could be associated with observed. rather than self-reported.
behaviours.
Fifth, after further examining the eight factors denved frorn the Principal Axis
Factoring rnethod. it may be helpful to include a greater number of variables related to
knowledge, decision making, and the doctor and pharmacist to see if additional variables
would load on the four factors deemed to repnent "self-assured knowledge". "doctororiented information", "pharmacist-oriented information" and "patient decision making".
Presently, each of these factors only had three variables with loadings of .3 or more.
Therefore, it would provide p a t e r confidence in the nature of these four factors if
additional variables loaded on these factors.
Finally, steps should be taken to avoid or lessen ~spondentfatigue in answering the
questionnaire. Without such action, the result may be pattemed responses and unanswered
items. While the direction of a few of the 32 attitudinai items were varied from positive to
negative to prevent a pattemed set of responses, the order of presentation of staternents
shouId also be varied to further leduce any order bias fiom occuning.
REFERENCES
Aaker, D.A.& Day, G.S.(1983) Marketing Research: (2nd ed). Toronto: Wiley & Sons.
Govemment of Saskatchewan, Saskatchewan Health, Prescription Dnig Services
Branch.(1996). Annual Statisticai Re~ort(1995- 19961. (pp. 13- 14).
Beales, H., Mazis, M., Salop, S., & Staelin, R. (1981). Consumer Search and Public
Policy. Journal of Consumer Research. 8, 11-22.
Beatty. S.E., & Smith, S.M. (1987). Extemai Search Effort: An Investigation Across
83-95.
Several Product Categories. Journal of Consumer Research,
o.
Bettman, J.R.(1978). Consumer Information Acquisition and S e m h Strategies. In
A.A.Mitchel1 (Ed.), The Effect of Information on Consumer and Market Behavior
(pp. 35-48). Chicago, Illinois: American Marketing Association.
Bemiian, J.R. (1979). An Information Processin2 Theorv of Consumer Choice. Reading,
MA: Addison-Wesley.
Bettman, J.R., & Park,C.W. (1980). Effects of Pnor Knowledge and Experience and
Phase of the Choice Process on Consumer Decision Processes. Journal of
Consumer Research, 1,234-248.
Bloch, P.H., Sherrell, D.L., & Ridgway, N.M.(1986). Consumer Search: An Extended
Frarnework. Journal of Consumer Research, 13,1 19-126.
Booth, A.. & Babchuk, N. (1972). Seeking Health Care From New Resources. Journal
of Health & Social Behavior, i3,90-99.
Bmcks. M. (1985). The Effects of Product Class Knowledge on Information Search
Behavior, Journal of Consumer Research, i2, 1- 16.
Claxton, J., Fry, J., & Portis, B. (1974). A Taxonorny of Prepurchase infornation
Gathering Patterns. Journal of Consumer Research, 1,35-42.
Cox, D.F. (Ed.). (1963). Risk Taking and Information Handlinn in Consumer Behavior.
Boston: Harvard University Press.
Duncan. C.P.. & Olshavsky, R.W.(1982). External Search: The Role of Consumer
Beliefs. Journal of Marketing Research, XIX, 32-43.
Engel. J.F., & Blackwell, R.D.(1982). Consumer Behavior (4th ed.). New York: CBS
College Publishing.
Festinger, L. ( 1957). The Theorv of Cognitive Dissonance. Stanford: Stanford
University Press.
Furse, D.H,Punj, G.N. & Stewart, D.W. (1984). A Typology of Individual Search
Strategies Arnong Purchasers of New Automobiles. Journal of Consumer
Research, IO,4 17-431.
Hair, J.F., Anderson, R.E., Tatham, R.L.& Black, W.C. (1992). Multivariate Data
Analvsis with Readings: (3rd ed.). New York: MacMillan.
Horton, R.L. (1984). Buyer Behavior: A Decision-Making Approach (pp. 24-47).
Columbus, Ohio: Memll Publishing Company.
Howard, J.A. (1977). Consumer Behavior: Application of Theory. New York:
McGraw-Hill.
Howard, J.A., & Sheth, J.N. (1969). The Theorv of Buyer Behavior. New York: John
Wiley & Sons.
Hoyer, W.D. (1984). An Examination of Consumer Decision Making for a Common
Repeat Purchase Product. Journal of Consumer Research, iI, 822-829.
Jacoby, J., Chestnut, R., Weigl, K. & Fisher, W. (1976). Pre-Purchase Information
Acquisition: Description of a Process Methodology. Research Paradigm, and
Pilot Investigation. In B.B. Anderson (Ed.),Advances in Consumer Research (pp.
306-3 14). Chicago, Illinois: Association for Consumer Research.
Katona, G. & Mueller, E. (1955). A Study of Purchase Decisions in Consumer Behavior.
In L.H. Clark (Ed.), Consumer Behavior: The Dvnarnics of Consumer Reaction:
Vol 1. (pp. 30-87). New York: New York University Press.
Kiel, G.C.,& Layton, R.A. (1981). Dimensions of Consumer Information Seeking
Behavior. Journal of market in^ Research, XVIII, 233-239.
Klippel, R.E., & Sweeney (1974). The Use of Information Sources by the Aged
Consumer. The Gerontoloaist, 14(2), 163- 166.
Kranz. D., Baum, A. & Wideman, M. (1980). Assessment of Prefer~ncesfor SelfTreatment and Information in Health Care. Journal of Personalitv and Social
Psychologv, 39(5),977-990.
Laurent, G.,Kapferer, J. (1985). Measuring Consumer Involvement Profiles. Journal of
Marketing Research, 22.4 1-53.
Lenz. E.R.(1984). Information Seeking: A Component of Client Decisions and Health
Behavior. Advances in Nursing Science, 5(3),59-72.
Locander, W.B., & Hermann, P.W. (1979). The Effect of Self-Confidence and Anxiety
on Information Seeking in Consumer Risk Reduction. Journal of Marketing
Research, X W ,268-274.
Lumpkin, J.R., & Festervand, T.A. (1987). Purchase Information Sources of the Elderly.
Journal of Advertisinn Research, 27(6), 3 1-43.
Lutz, R.J. and Reilly. P.J. (1973). An Exploration of the Effects of Perceived Social and
Performance Risk in Consumer Information Acquisition. In S. Ward and P.
Wright (Eds.), Advances in Consumer Research . Urbana, Illinois: Association
for Consumer Research.
Malhotra, N. K. ( 1983). On 'Individual Differences in Search Behavior for a Nondurable'.
Journal of Consumer Research, 10,125-131.
Midgley, D.F. (1983). Patterns of Interpersonal Information Seeking for the Purchase of
a Symbolic Product. Journal of Marketing Research, m,74-83.
Moore, S.. Kalu, M., & Yavaprabbas, S. ( 1983). Receipt of Prescnption Drug
Information by the Elderly. Drug Intelligence and Clinical Pharmacy, i7,920923.
Moore, W.L., & Lehmann, D.R. (1980). Individual Differences in Search Behavior For a
Nondurable. Journal of Consumer Research, Z, 296-307.
Morris, L., Grossman, R., Barkdoll. G., Gordon, E., & Soviero, C. (1984). A Survey of
Patient Sources of Prescnption Dnig Information. Arnerican Journal of Public
74(IO), 1161-1162.
Health 9-
Monis, L., Bnnberg, D., Klimberg, R., Rivera, C., & Millstein. L. (1986). The Attitudes
of Consumen Toward Direct Advertising of Prescription Dmgs. Public Health
Rewrts, N ( l ) , 82-89.
Monis, L., Grossman, R., Barkdoll, G., & Gordon, E. (1986). A National Survev of
Prescription Drug Information Provided to Patients (Report No. PB #84- 10003).
Springfield, VA: National Technical Information Service.
Morris, L., Grossman. R., Barkdoll, G., & Gordon. E. (1987). A Segmentational Analysis
of Prescription Dmg Information Seeking. Medical Care, 25(10), 953-964.
Moms, L., Grossman. R., Barkdoll, G., Gordon. E., & Chun, M. (1987). Information
Search Activities Among Elderly Prescription Drug Users. Journal of Health
Care Marketing, 2(4), 5-15.
Morris, L.A., Tabak, E.R.,Olins, N.J. (1992). A Segmentation Analysis of Prescription
Dmg Information-Seeking Motives Among the Elderly. Journal of Public Policv
and Marketing, il(2), 115-125.
Newman, J.W. (1977). Consumer External Seanih: Amounts and Determinants. In A.
Woodside, J. Sheth, & P. Bennett (Eds.), Consumer and Industrial Buving
Behavior (pp. 79-94). New York: North-Holland.
Newman, J.W., & Lockeman, B.D. (1975). Measunng Repumhase Information Seeking.
Journal of Consumer Research, 5 2 16-222.
Newman. I., & Staelin, R. (1972). Prepurchase Information Seeking for New Cars and
Major Household Ap pliances. Journal of Marketing Research, IX,
249-257.
Olshavsky, R.W.,
Granbois, D.H. (1979). Consumer Decision Making - Fact or Fiction?.
Journal of Consumer Research, &93- 100.
Phillips, L., & Stemthal, B. (1977). Age Differences in Information Processing: A
Perspective on the Aged Consumer. Joumal of Marketing Research, XIV, 444457.
Punj, G., & Staelin, R. (1983). A Model of Consumer Information Search Behavior for
New Autorno b iles. Journal of Consumer Research, 9, 366-380.
Schaninger, C., & Sciglimpaglia, D. (198 1 ). The Influence of Cognitive Personality
Traits and Demographics on Consumer Information Acquisition. Journal of
Consumer Research, 8, 208-2 18.
Sheth, J. & Venkatesan, M. (1968). Risk Reduction Processes in Kepetitive Consumer
Behavior. Journal of Marketing Research, y, 307-3 10.
Snnivasan, N. & Ratchford, B. ( 1991). An Empirical Test of a Model of Extemal Search
for Automobiles. Journal of Consumer Research, 18,233-242.
Stemthal. B.. & Craig. S. ( 1982). Consumer Behavior: An Information Processing
Perspective. Englewood Cliffs, New Jersey: Prentice-Hall.
Stnitton. H.D..& Lumpkin, J.R. (1992). information Sources Used by Elderly Health
Care Product Adopters. Journal of Advertisine Research, 32(4), 20-30.
Swan, J.E. (1969). Experimental Analysis of Predecision Information Seeking. Journal
of Marketing Research, VT, 192-197.
Tabachnick, B.G. & Fidell, L.S. ( 1989). Using Multivariate Statistics. (2nd ed). New
York: HarperCollins.
Urbany, J.E., Dickson. P.R., Wilkie, W.L. (1989). Buyer Uncertainty and hformation
Search. Journal of Consumer Research,
208-2 15.
s,
Wallston, K., Maides, S., & Wallston, B. (1976). Health-Related Information Seeking as
a Function of Health-Related Locus of Control and Health Value. Journal of
Research in Personalitv, u,215-222.
Wells, W.D. ( 1975). Psychographics: A Critical Review. Journal of Marketing
Research, JXI 196-2 13.
Westbrook, R., Fornell, C. (1979). Patterns of Information Source Usage Among
Durable Goods Buyers. Journal of Marketing Research, XVI, 303-312.
-
APPENDE 1:
RESULTS FROM PRINCPAL COMPONENTS ANALYSIS
1O9
-> FACTOR
/VARIABLES var24a var24b var24c var24d var24e var24f var24g var24h var24i
->
var24j var24k var241 var24m var24n var240 var24p var24q var241 var24s
->
var24t var24u var24v var24w var24x var24y var242 var24aa var24bb var24cc
->
->
var24dd var24ee var24ff /MISSING MEANSU' /ANALYSIS var24a var24b var24c
var24d var24e var24f var24g var24h var24i var24j var24k var241 var24m
->
var24n var240 var24p var24q var24r var24s var24t var24u var24v var24w
->
var24x var24y var242 var24aa var24bb var24cc var24dd var24ee var24ff
->
->
/PRINT INITIAL EXTRACTION ROTATION
->
/FORMAT SORT
/PLOT ROTATION
/CRITERIA MINEIGEN (1) ITERATE ( 2 5 )
->
/EXTRACTION PC
->
/CRITERIA ITERATE(25)
->
/ROTATION VARINAX
->
/SAVE REG(ALL) .
110
_ _ _ _ _ _ _ _ _ _ _
A N A L Y S I S
- - - - - - - - - - -
Replacement of missing values w i t h the mean
Analysis number 1
Extraction
F A C T O R
1 for analysis
Principal Components Analysis
Initial Statistics:
Variable
PC
Cornmunality
extracted
9 factors.
Eigenvalue
~ c of
t Var
C m Pct
- - - - - - - - - - -
A N A L Y S I S
F A C T O R
- - - - - - - - - - -
F a c t o r Matrix:
Factor
1
Factor
2
Factor
3
Factor
4
Factor
6
Factor
7
Factor
8
Factor
9
Factor
5
-
-
-
-
-
C
_
_
-
-
F A C T O R
-
*
Variable
VARIMAX
VARI=
rotation
Factor
1 for e x t r a c t i o n
- - - - - - - - - - -
A N A L Y S I S
Eigenvalue
P c t of Var
1 i n analysis
1
-
Cum Pct
Kaiser Normalization.
converged i n 2 3 i t e r a t i o n s -
Rotated Factor Matrix:
Factor
Factor
Factor
Factor
Factor
- - - - - - - - - - Factor
A N A L Y S I S
F A C T O R
Factor
6
7
Factor
8
- - - - - - - - - - Factor
9
F i n a l S t a t i s tics :
Variable
*
*
*
*
*
*
*
f
*
Factor
1
2
3
Eigenvalue
5
5.14317
3.51780
2.14859
1.67526
1.46265
4
6
1.25428
7
1.18550
8
1.14203
Pct of V a r
Cum P c t
APPENDIX II:
RESULTS FROM PRINCIPAL M S FACTORING
-> FACTOR
11s
->
/VARIABLES var24a var24b var24c var24d var24e var24f var24g var24h var24i
->
var24j var24k var241 var24m var24n var240 var24p var24q var24r var24s
->
var24t var24u var24v var24w var24x var24y var242 var24aa var24bb var24cc
->
var24dd var24ee var24ff /MISSING MEANSUB /ANALYSIS var24a var24b var24c
var24d var24e var24f var24g var24h var24i var24j var24k var241 var24m
->
var24n var240 var24p var24q var24r var24s var24t var24u var24v var24w
->
var24x var24y var242 var24aa var24bb var24cc var24dd var24ee var24ff
->
->
/PRINT INITIAL CORRELATION SIG DET KM0 A I C EXTRACTION ROTATION
->
/FORMAT SORT
->
/CRITERIA FACTORS ( 8 ) ITERATE ( 2 5 )
->
/EXTRACTION PAF
->
/CRITERIA ITERATE (25)
->
/ROTATION VARIMAX .
116
- - - - - - - - - - -
Analysis nwnber 1
F A C T O R
A N A L Y S I S
- - - - - - - - - - -
Replacement of missing values w i t h t h e mean
C o r r e l a t i o n Matrix:
VAR2 4A
1.00000
.15270
,21918
O4854
-53365
.
.28974
.28611
- -00259
-20524
- -00842
- -05290
,27904
-39488
08459
- ,04382
.O2740
-11970
38149
-09441
,16417
- ,17038
-12048
-45686
,24255
.34788
10593
-00968
,03999
.IO714
-08789
-.O6501
,10690
-.
-
-
VARS 4H
1.00000
.26466
.29626
VAR2 4F
VARS 4G
-
F A C T O R
A N A L Y S I S
--
- - - - - - - - - - -
F A C T O R
Determinant of C o r r e l a t i o n Matrix =
A N A L Y S I S
.O001886
Kaiser-Meyer-Olkin Measure of Sampling Adequacy =
.a0416
B a r t l e t t Test of Sphericity = 3451.7599, Significance =
A n t i - M a g e Covariance Matrix:
VAR2 4A
VAR2 4B
VAR2 4C
VAR2 4 D
VAR2 4 E
VAR2 4 F
VAR2 4G
VAR2 4H
VAR241
W24J
VARS 4 K
W24L
VAR2 4M
- - - - - - - - - - -
- O0000
F A C T O R
VAR2 4A
.O1509
-. 01847
-. 00631
-.02154
-. 09312
- 02836
-.03172
-01627
-01499
-007611
- 01134
-.06918
.O4044
00687
00941
02596
-.O3699
04243
O0290
-.
-.
-.
-.
.
VARS 4F
,69434
.O0759
-,02297
,05118
--O3351
-07595
00743
-.O0386
02734
.O8542
.O8447
04093
.O2160
-.04331
06921
O0510
02094
-.O7999
01034
.O4821
-02293
01504
- .O8419
.O2430
-.
-.
-.
-.
-.
-.
-.
-.
F A C T O R
- - - - - - - - - - -
VAR242
VAR2 424A
VAR2 4BB
VAR24CC
VAR24DD
VAR24EE
VARS 4FE'
.54175
.O1707
14413
-.01043
01965
06082
-.03854
-.
.
-.
F A C T O R
.65046
- 14365
A N A L Y S I S
.72676
--O5185
-. 06228
.O7794
-.O1377
04745
-.O1604
.O1064
.O1036
-.
Anti-image Correlation Matrix:
- - - - - - - - - - -
- 718 90
-.07213
-,10598
.O2191
- - - - - - - - - - -
F A C T O R
A N A L Y S I S
- -
123
- - - - - - - - - - -
F A C T O R
A N A L Y S I S
-
-----------
F A C T O R
Measures of Sampling Adequacy
(MSA)
A N A L Y S I S
are printed on t h e diagonal.
1-tailed Significance of C o r r e l a t i o n Matrix:
' . ' is printed f o r d i a g o n a l elements.
VAR2 4A
VAR243
VAR24C
VAR24D
VAR24E
VAR2 4F
VAR2 4G
VARL 4):
VAR241
VAR245
VAR24K
VAR24L
W 2 4M
VAR2 4N
VAR240
VAR24P
VAR244
VAR24R
VAR24S
VAR2 4T
VAR24U
VAR2 4V
VAR2 4W
VAR2 4X
VAR24Y
VARS 4 Z
- - - - - - - - - - -
F A C T O R
A N A L Y S I S
- - - - - - - - - - -
F A C T O R
VARS 4K
A N A L Y S I S
- - - - - - - - - - -
F A C T O R
VAR2 4V
Extraction
1 for analysis
A N A L Y S I S
VAR2 4W
1, Principal
- - - - - - - - - - VAR24X
VAR2 4Y
mis Factoring (PAF)
I n i t i a l Statistics:
Variable
VAR2 4 A
VAR2 4B
VAR2 4C
VAR2 4D
VARS 4E
VAR24F
VAR2 4 6
VAR24H
VAR241
VAR245
VAR2 4K
Communality
*
*
Factor
Eigenvalue
P c t of Var
Cum
Pct
128
_ _ _ _ _ _ _ _ _ _ _
Factor
Variable
PAF
A N A L Y S I S
F A C T O R
extracted
8 factors.
- - - - - - - - - - -
pct of Var
Eigenvalue
Cum P c t
11 i t e r a t i o n s required.
F a c t o r Matrix :
Factor
1
Factor
2
Factor
3
Factor
4
Factor
5
- - - - - _ - _ _ - Factor
1
.O1170
-02944
.22479
-25362
,12988
.37497
.O6447
Factor
6
A N A L Y S I S
F A C T O R
Factor
2
Factor
3
- - - - - - - - - - Factor
4
Factor
5
-. 17541
.12669
-. 19165
- .13170
-.16506
-12688
.O5884
-,O7904
.13349
.O1496
.15529
,19583
.25335
.41563
.O3593
03451
.24201
.O9715
.31773
.O9835
,00268
-21231
.O9744
.15736
.15466
Factor
7
-.00904
-08239
,28569
Factor
8
-.
- - - - - - - - - - Factor
F A C T O R
Factor
6
A N A L Y S I S
Factor
7
- - - - - - - - - - -
8
Factor Transformation Matrix:
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
Factor
1
Factor
2
Factor
3
Factor
6
Factor
7
Factor
8
1
2
3
4
5
6
7
8
Factor
4
Factor
5
APPENDIX DI:
RESULTS FROM HJERARCHICAL CLUSTER ANALYSIS
132
-> PROXIMITIES
->
s c a l e l scale2 s c a l e 3 scale4 s c a l e 5 scale6 s c a l e 7 s c a l e 8
->
/MATRIX OUT ( ' C :\WINDOWS \TEDfP\spssclus. tmp l )
Data Information
4 3 3 unweighted cases accepted.
O cases rejected because of missing value-
Squared Euclidean measure used.
134
-> CLUSTER
->
/MATRIX IN ('C:\W1NDOWS\TEMP\spss~lus.trnp')
->
/METBOD WARD
->
->
/PRINT SCHEDULE
/PLOTS NONE.
* * * * * * H I E R A R C K I C A L
135
C L U S T E R
A N A L Y S I S ' * * * * *
Agglomeration Schedule using Ward Method
Stage
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
Clusters
Cluster 1
Conhined
Cluster 2
192
127
51
141
39
38
30
77
130
88
26
22
133
103
80
73
14
331
286
132
4
139
158
45
203
36
70
63
10
11
81
322
42
18
233
173
217
323
281
147
332
406
278
117
84
266
221
2
15
129
105
137
20
13
223
47
285
265
238
319
171
415
384
232
12
155
429
177
216
74
97
114
56
16
144
377
316
107
35
100
283
200
214
272
76
279
17 5
350
Coefficient
Stage C l u s t e r 1st Appears
Cluster 1
Cluster 2
Next
Stage
169
291
78
63
141
62
64
212
119
209
198
186
336
171
231
103
105
145
124
98
181
108
144
134
252
61
171
201
150
199
154
248
84
163
246
53
244
221
189
199
150
169
176
257
* * * * * * H I E R A R C H I C A L
136
C L U S T E R
A N A L Y S I S * * * * * *
Agglomeration Schedule using Ward Method (CONT.)
Clusters
Stage
Cluster1
Combined
Cluster2
Stage C l u s t e r 1st Appears
Coefficient
Cluster 1
Cluster 2
Next
Stage
137
* * * * + * H I E R A R C H I C A L C L U S T E R
A N A L Y S I S * " * * * *
Agglomeration Schedule using Ward Method ( C O N T . )
Clusters
Stage
Cluster 1
Combined
Cluster
2
Stage C l u s t e r 1st Appears
Coefficient
Cluster 1
Cluster 2
Next
Stage
138
* * * * * * H I E R A R C H I C A L
C L U S T E R
A N A L Y S I S * * + * * *
Agglomeration Schedule using Ward Method (CONT.)
Clusters
Stage
Cluster 1
Stage C l u s t e r 1st Appears
Combined
Cluster 2
Coefficient
Cluster I
Cluster 2
Next
Stage
139
* * * * * + H I E R A R C H I C A L C L U S T E R
A N A L Y S I S * * * * * *
Agglomeration Schedule using Ward Method (CONT.)
Clusters
Stage
Cluster 1
Stage Cluster 1st Appears
Conibined
Cluster 2
Coefficient
Cluster 1
Cluster 2
Next
Stage
* * * * * * H I E R A R C H I C A L
140
C L U S T E R
A N A L Y S I S * * * * * *
Agglomeration Schedule using Ward Method (CONT.)
Clusters
Stage
Cluster 1
S t a g e Cluster 1st Appears
Combined
Cluster 2
Coefficient
Cluster 1
Cluster 2
Next
Stage
Agglomeration Schedule using Ward Method (CONT.)
Clusters
Stage
Cluster 1
Stage Cluster 1st Appears
Combined
Cluster 2
Coefficient
Cluster 1
Cluster 2
Next
Stage
APPENDIX TV:
RESULTS FROM NON-HIERARC'XICAL CLUSTER ANALYSIS
COMPUTE scalel = (var24u 1 + var24m-1 + var24@2
+ var24f-1 + var24i-1 +
var24a-1 + var24t-1) /-7
.
EXECUTE .
COMPUTE scale2 = (var24n-l + var24u-1 + var24c-2 + var24e-2 + var24k-1 +
var24j-1) / 6 .
EXECUTE
COMPUTE scale3 = (var24x-1 + var241-1 + var24y-1) / 3 .
EXECUTE
.
.
RECODE
var24a -2 var24v-l var24p-1 var24e-2
(1=7)
(2=6) (315) ( 5 x 3 )
(6=2) (7=1)
var24pln var24e2N .
EXECUTE .
(4=4) INTO
var24a2n
var24vln
RMV
/var24a 3=SMEAN(var24a2n) /var24~-3=S~~AN(var24vln)
/var24p 3-SMEAN
(var24pln) /var24e-3=~MUW(var24e2n)
COMPUTE scale4 = (var24a 3 + var24v 3 + var24h-l + var24i-1 + var24p-3 +
var24j-1 + var24b-l + var24b-2) /-8
EXECUTE .
COMPUTE scale5 = (var24d-2 + var24d-1 + var24s-1 + var24t-1) / 4
EXECUTE
COMPUTE scale6 = (var24r-1 + var24e-1 + var24a-1) / 3 .
EXECUTE COMPUTE scale7 = (var24e-3 + var24c-1 + var24g-1) / 3
EXECUTE .
COMPUTE scale8 = (var24z-1 + var24b-2 + var24t-1) / 3 .
EXECUTE
.
.
.
.
.
.
-> QUICK CLUSTER
->
scalel scale2 scale3 scale4 scaleS scale6 scale7 scale8
->
/MISSING=LISTWfSE
->
/CRITERIA= CLUSTER(3) MXITER(10) CONVERGE(.OZ)
->
/INITIAL = (5.60 3.83 3.73 3.90 5.65 5.17 4.14 5.10
->
6 - 3 0 3.04 5.32 4.44 6.03 6.14 5.92 5.99
->
5.04 2.51 3.91 3.17 4.11 6.09 5.44 3.27)
->
/METHOD=KMEANS (UPDATE )
->
/SAVE CLUSTER
->
/PRINT INITIAL ANOVA
-> .
Initial Cfuster Centers. (Front subcomand INITIAL)
Cluster
Cluster
Convergence achieved due to no oe small distance change.
The maximum distance by which any center has changed is -0201
Current iteration is 2
Minimum distance between initial centers is 2.9966
Iteration
1
2
Change in Cluster Centers
1
2
3
.2300
.2554
.2876
.O227
O015
.O304
.
F i n a l Cluster Centers.
Cluster
SCALE5
SCALE6
SCALE7
SCALE8
Analysis of Variance.
Variable
Cluster MS
DF
E r r o r MS
Number of Cases i n each Cluster.
Cluster
unweighted cases
Missing
Valid c a s e s
Variable Saved i n t o Working File.
QCL-1
(Cluster Number )
weighted cases
DF
F
Prob
APPENDIX v:
RESULTS FROM DISCRIMTNANT ANALYSIS
147
-> DISCRIMINANT
->
/ G R O U P S = C ~ U ~ - S (1 3 )
->
/ ~ I A B L E S = c o u n s l _ i questd-1 questp-1 counlp 1 anyinf-1 cnsldr-1 cnslph-l
->
askphc-1 askdrc-1 srchco-1 anyinf-2 srchco-2->
/ANALYSIS ALL
->
/PRIORS
SIZE
->
/STATISTICS=MEAN STDDEV UNIVE' BOXM COEFF RAW TABLE
->
/PLOT=cOMBINED
->
/CLASSIE'Y=NONMISSING POOLED MEANSUB .
_ _ _ - _ _ - -D
I S C R I M I N A N T
On groups defined by CLU3-2
A N A L Y S r S
Ward Method
412 (Unweighted) cases were processed.
O of these were excluded f r o m the analysis.
412 (Unweighted) cases will be used in the analysis.
Number of cases by group
Number of cases
Unweighted
Weighted
1
131
131.0
2
155
155.0
3
12 6
126.0
CLU3-2
Total
412
Label
412.0
Group means
CLU32
COUNSL-1
QUESTD-1
Total
CNS LDR-1
Total
- - - - - - - -
Total
.36893
Group standard deviations
Total
2.68352
Total
-49155
Total
.67619
W i l k s ' Lambda ( U - s t a t i s t i c ) and univariate F-ratio
w i t h 2 and 4 0 9 degrees o f freedom
variable
sR C H C O ~ I
ANY I N F 2
SRCHCO 2
Wilkst Lambda
- - - - - - - -
D I S C R I M I N A N T
On groups defined by CLU3-2
Analysis number
Direct method:
A N A L Y S I S
Ward Method
1
a l 1 v a r i a b l e s passing the t o l e r a n c e test a r e rntered.
Minimum tolerance level..
.....-.--..-....- 0 0 1 0 0
Canonical Discriminant Functions
-
2
Maximum number of functions .............
Minimum c u m u l a t i v e p e r c e n t of variance. .. 1 0 0 . 0 0
Maximum significance of W i l k s ' Lambda .... 1 . 0 0 0 0
Prior probabilities
Group
Prior
Total
1.00000
Label
Classification f u n c t i o n coefficients
(Fisher's l i n e a r d i s c r i m i n a n t functions)
QUESTP-1
COUNLP-1
ANYINF-1
CNS LDR-1
CNSLPH-1
SRCHCOII
ANY INF-2
SRCHCO-2
(Constant)
- - - - - - - -
Canonical Discriminant Fmctions
P c t of
Fcn Eigenvalue Variance
Cum
Pct
Canonical
Corr
After
Fcn
Wilks'
Lambda
Chi-square
df
S i g
* Marks the 2 canonical discriminant functions remaining in the analysis.
Standardized canonical discriminant function coefficients
Func
COUNS L-1
QUESTD-1
QUESTP 1
COUNLPI~
ANYINF-1
CNS LDR-1
CNS LPH-1
ASKPHC-1
ASKDRC-1
S RCHCO-1
ANY INF-2
SRCHCO-2
1
Func
2
Structure matrix:
Pooled within-groups correlations between discriminating variables
and canonical discriminant functions
(Variables ordered by size of correlation within function)
Func
1
COUNLP-1
COUNS L-1
CNSLDR-1
CNS LPH-1
ASKDRC-1
.91063*
,54844*
.50514*
.39360*
-,17195*
SRCHCO 1
SRCHCO:~
QUESTP-1
QUESTD-1
ANYINF-2
ANYINF-1
ASKPHC-1
.15115
.14180
-.01669
- .O8271
.19243
.Il166
,14605
Func
2
-.02081
-.29606
-.17311
02063
,00617
-.
,61518*
.59752*
.55676*
.45639*
.44831*
.37886*
.15923*
* denotes largest absolute correlation between each variable and any
discriminant function.
Unstandardized canonical discriminant function coefficients
Func
COUNS L-1
QUESTD-1
QUESTP-1
COUNLP-1
ANYINF 1
CNSLDR:~
CNSLPH-1
ASKPHC-L
ASKDRC-1
SRCHCO-1
ANY INF-2
SRCHCO-2
(Constant)
f
Func
2
Func
Group
1
Func
2
Test o f Equality of Group Covariance Matrices Using Box's M
The ranks and natural logarithms of determinants printed are those
of the group covariance matrices-
Group Label
1
2
3
Pooled within-groups
covariance matrix
Box's M Approximate F
294 -28129
1.80862
Hi-Res C h a r t
Rank
12
12
12
12
Log Determinant
-7.656397
-4.759309
-7.994058
-5.949245
Degrees of freedom
156,
421944.8
# 6:ALl-groups scatterplot
Significance
.O000
Classification results
Group
I
Group
2
G r oup
3
-
Percent o f "grouped" cases c o r r e c t l y classified:
52.18%
Classification processing s m m a r y
412 (Unweighted) cases were processed.
O cases were excluded for missing o r out-of-range group codes.
412 (Unweighted) cases were used f o r printed output.
-> DISCRIMINANT
->
/GROUPS=clu3-2(1 3 )
->
->
->
->
->
->
->
156
/VARIABLES=var40 hhsize hhi resp-age mar-st-n
atlantic quebec o n t a r i o prairies bc
/ANALYSIS ALL
/PRIORS
SIZE
/STATISTICS=TABLE
/ PLOT=COMBINED
/CLASSIFY=NONMISSING POOLED MEANSUB .
resp-e-n employ-n
status-n
_ - _ _ - _ - -D
I S C R I M I N A N T
On groups deiined by CLU3-2
A N A L Y S I S
- - - - - - - -
Ward Method
428 (Unweighted) cases were processed.
10 of these were excluded from the analysis.
O had missing or out-of-range group codes.
10 had at least one missing discriminating variable.
418 (Unweighted) cases will be used in the analysis.
Number of cases by group
CLU3-2
1
2
3
Total
Number of cases
Unweighted
Weighted
131
131.0
160
160.0
127
127.0
Label
- - - - - _ - -
D I S C R I M I N A N T
On groups defined by
CLU3-2
Analysis ntrmber
Direct method:
A N A L Y S I S
- - - - - - - -
Ward Method
1
al1 variables passing the tolerance test are entered.
Minimum tolerance l e v e l .
.................
.O0100
Canonical Discriminant Functions
Maximum number of functions..............
Minimum cumulative percent of variance ...
Maximum significance of Wilks' Lambda ....
2
100.00
1.0000
Prior probabilities
~roup
Total
Prior
Label
1.00000
The following variable failed the toierance test.
Variable
Within
Groups
Variance
Tolerance
Minimum
Tolerance
BC
Canonical Discriminant Functions
Pct of
Fcn Eigenvalue Variance
C u
Pct
Canonical After Wilks '
Corr
Fcn Lambda
Chi-square df
Sig
* Marks the 2 canonical discriminant functions remaining in the analysis.
Standardized canonical discriminant function coefficients
Func
1
Func
2
VAR40
HHSIZE
HHI
RES P-AGE
MAR-ST-N
RES P E N
EMPLOYN
STATUSN
ATLANTIC
QUEBEC
ONTARIO
PRAIRIES
Structure matrix:
Pooled within-groups correlations between discriminating variables
and canonical discriminant functions
(Variables ordered by size of correlation w i t h m function)
Func
1
Func
2
STATUS N
BHSIZEEMPLOY-N
VAR40
BC
QUEBEC
RE S P - E N
ATLANTIC
RES P-AGE
PRAIRIES
MAR ST-N
HHI-
ONTARIO
-.5 0 7 1 1
- -09046
-.19242
,06904
.08215
.56410*
.42790f
.32776*
.22095*
12090X
-.
* denotes largest absolute correlation between each variable and any
discriminant function.
Canonical discriminant functions evaluated at group means (group centroids)
Group
Hi-Res C h a r t
Func
1
Func
# 2:AJ.l-groups
2
scatterplot
C l a s s i f i c a t i o n results
-
Group
1
133
Group
2
165
Group
3
130
Percent of "grouped" c a s e s c o r r e c t l y c l a s s i f i e d :
47.66%
C l a s s i f i c a t i o n processing summary
428 (Unweighted) cases were processed.
O cases were excluded f o r missing or out-of-range group codes.
428 (Unweighted) c a s e s were used for printed o u t p u t .
162
-> DISCRIMINANT
->
/GROUPS=clu3_2(T 3 )
->
/VARIABLES=varZ var6b var7 varlOa varlob varlla varllb varlia varlîb
->
varl8a varl8b respirat obgyne bowel cancer musculo mental cardio dermatol
->
pain blood nutrit neurolog hepatic endocrin genita other var32 var33 var34
->
/ANALYSZS ALL
->
/PRIORS SIZE
->
/STATISTICS=TABLE
->
/PLOT=COMBINED
->
/CLXSIFY=NONMISSING POOLED MEANSUB .
_ _ _ - - - _ -D
I S C R I M I N A N T
On groups defined by CLU3-2
A N A L Y S I S
- - - - - - - -
Ward Method
418 (Unweighted) cases were processed.
143 of these were excluded from the analysis.
O had missing or out-of-range group codes.
143 had at l c a s t one missing discriminating variable.
275 (Unweighted) cases will be used in the analysis.
Number of cases by group
CLU3-2
I
Total
Number of cases
Unweighted
Weighted
80
80.0
Label
- - - - - - - -
D I S C R I M I N A N T
On groups defined by CLU3-2
Direct method:
A N A L Y S I S
- - - - - - - -
Ward Method
al1 variables passing the tolerance test are entered.
Minimum tolerance level ..................
.O0100
Canonical Discriminant Functions
Maximum number of functions..............
Minimum cumulative percent of variance ...
Maximum significance of W i l k s ' Lambda ....
2
100.00
1.0000
Prior probabilities
Group
Prior
Total
1.00000
Label
The following 6 variables failed the tolerance test.
Variable
Within
Groups
Variance
CANCER
BLOOD
NEUROLOG
HEPATIC
GENITA
OTHER
.O00000
.O00000
.O00000
.O00000
.O00000
-168746
Tolerance
.O000000
,0000000
.0000000
,0000000
.O000000
.O000000
Minimum
Toler ance
.0000000
.O000000
,0000000
.0000000
.O000000
.O000000
Canonical Discriminant Functions
P c t of
Fcn Eigenvalue Variance
I*
2*
.5200
.O979
84.16
15.84
Cum
Pct
84-16
100.00
Canonical
Corr
.5849 :
.2986 :
After
Fcn
Wilks'
Lambda
Chi-square
df
Sig
133.401
24.331
48
23
.O000
.3857
O -599239
1 .910829
* Marks the 2 canonical discriminant functions remaining in the analysis.
Standardized canonical discriminant function coefficients
VAR2
VAR6B
VAR7
VARlOA
VARlOB
vAR11A
VARllB
VARl 7A
VAR17B
VARl 8A
VAR18B
RESPIRAT
OBGYNE
BOWEL
MUSCULO
mNTAL
CARDI0
DERMATOL
PAIN
NUTRIT
ENDOCRIN
VAR32
VAR33
VAR3 4
Structure matrix:
Pooled within-groups correlations between discriminating variables
and canonical discriminant functions
(Variables ordered by size of correlation within function)
Func
VARllB
VAR1 1A
VAR18B
VÀR7
VAR18A
VARl OA
RESPIRAT
VARl OB
VAR32
VAR17A
VAR2
VARl7B
VAR6B
DERMATOL
OTHER
VAR33
OBGYNE
BOWEL
MUSCULO
VAR3 4
ENDOCRIN
NUTRIT
PAIN
CARDI0
MENTAL
Func
1
2
-.10381
.71231*
.58442*
.42425*
39164*
.38719*
.27768*
,20167*
.18147*
-.17111*
.16404*
.15283*
.14600*
.O3755
-08592
,21196
-13845
.O5336
13044
.O6063
-.01880
-. 11442
-13081
-.O9354
-13544
-02211
-.03408
-.23373
.O5470
-. 08324
01609
.O5387
03376
02573
.O1235
-.10136
00336
.36786*
.32963*
30401*
- .26331*
.24644*
-.22836*
.21459*
.19629*
.15857*
,12087*
.11474*
-. 11372*
.03917*
-.
-.
-.
-.
-.
-.
-.
* denotes largest absolute correlation between each variable and any
discriminant function,
Canonical discriminant functions evaluated at group means (group centroids)
Group
Hi-Res Chart
Func
1
Func
2
# 4:All-groups scatterplot
Classification r e s u l t s
Group
1
Group
2
Group
3
-
Percent of "groupedm cases correctly classified:
52.15%
Classification processing summary
418 (Unweighted) cases were processed.
O cases were excluded for missing or out-of-range group codes.
418 (Unweighted) cases were used for printed output.
APPENDIX VI:
RESULTS FROM LOGISTIC REGRESSION
-> L O G I S T I C REGRESSION cl1.13-1-2
169
/METHOD-ENTER var4O-1 hhsize-l hhi 1 resp-a-1 mar st-1 resp-e-1
->
->
status-1 atlant-1 quebec-1 ontari-ï prairi-1 bc-1->
/CLASSPLOT
->
/ C R I T E R I A P I N ( . O S ) POUT(.lO) ITERATE(2O)
-
employ-1
170
Total number o f cases:
428 (Unweighted)
Number of selected cases:
428
Number of unselected cases: O
Number of selected cases:
428
Number rejected because of missing data: 130
Number of cases included in the analysis: 298
Dependent Variable Encoding:
Original
Internai
Value
1-00
Value
2.00
O
1
171
Dependent Variable..
CLU3-ld2
Beginning Block Number
-2 Log Likelihood
O.
Initial Log Likelihood Function
409.67284
Constant is included in the model.
Beginning Block Number
1.
Method: Enter
Redundancies in Design Matrix:
Variable(s) Entesed on Step Number
SMEAN (VAR40)
1..
VAR40 1
HHSIZE-i
SMEAN(HHS1ZE)
SMEAN (HHI)
HHI-1
SMEAN (RESP-AGE)
RES P-A-1
SMEAN (MAR-STN)
MAR-ST-1
RES PE-1 SMEAN (RESPE-N)
SMEAN ( EMPLOYN)
LMPLOY-1
STATUS-1 SMEAN ( STATUSN)
SMEAN (ATLANTIC)
ATLANT-1
QUEBEC-1 SMEAN ( QUEBEC )
ONTARI-1 SMEAN (ONTARIO)
PRAIRI-1 SMEAN ( P W R I E S )
Estimation terminated at iteration number 3 because
Log Likelihood decreased by less than -01 percent.
-2 Log Likelihood
Goodness of Fit
392.960
299.322
Chi-Square
Mode1 Chi-Square
Improvement
;=
16.713
16.713
ci£ Significance
12
12
-1607
,1607
Classification Table for CLU3-1-2
Observed
1.00
2.00
1
2
Predicted
Percent Correct
2-;
45.86%
128
77.58%
Overall
63.42%
B
Variable
S.E.
Wald
df
Sig
R
Exp(B)
VAR40 1
HHS IzE-1
HHI-1
RES P A 1
MARST-1
STATUS:~
ATLANT-1
QUEBEC-1
ONTARI-1
PRAIRI-1
Constant
Observed Groups and Predicted Probabilities
-
20 --
F
R
15
I
--
E
Q
u
E
N
C
Y
10 --
5
-2
1
22
2
22
2
22
2
22
2
2
2 222
2 2
22
222222 22 22
22 2222212222 22
12222222212221 22
2 212222222212221222 2
122121222222112212222 2
11211122222111221222222
2 11111111121111211222222
22 1111111112111r2111222222
llllilllllllllllllll1112211122
llll1lllllllllllllll111I1111112
1
1
i
T
I
I
I
Predic ted
L
I
1
Prob:
O
.25
.5
.75
1
Group: 111111111111111111111111111111222222222222222222222222222222
Predicted Probability is of Membership for 2 .O0
Symbols: 1 - 1.00
2 - 2.00
Each Symbol Represents 1.25 Cases.
-> LOGISTIC REGRESSION clu3-1-3
->
/METHOD=ENTER var40-1 hhsize-1 hhi-1 resp-a-1 m a o t - 1
->
status-1 atlant-1 quebec-1 ontari-1 prairi-1 bc-l
->
/CLASSPLOT
->
/ C R I T E R I A PIN ( .O5 ) FOUT ( .IO) ITERATE (20) .
resp-e-1
employ-1
Total number of cases:
428 (Unweighted)
428
Number of selected cases:
Number of unselected cases: O
Number of selected cases:
428
Number rejected because of missing data: 165
Number of cases included in the analysis: 263
Dependent Variable Encoding:
Original
Interna1
Value
1-00
3.00
Value
O
I
175
Dependent Variable..
CLU3-1-3
Beginning Block Number
-2 Log Likelihood
I h i t i a l Log Likelihood Function
O.
364.5612
Beginning Block Number
1.
Method: Enter
Redundancies in Design Matrix:
Variable ( s ) Entered
1..
VAR40 1
HHSIZE-1
HHI-1
RESP A l
on Step Number
SMEAN (VAR401
SMEAN (HHSIZE)
SMEAN(IM1)
SMEAN ( RES P-AGE 1
SMEAN ( MAR-S T-N )
SMEAN (RESP-E-N)
SMEAN (EMPLOYN)
SMEAN (STATUS-N)
SMEAN (ATLANTIC)
SMEAN ( QUEBEC1
SMEAN (ONTARIO)
SMEAN ( PRAIRIES)
Estimation terminated at iteration number 3 because
Log Likelihood decreased by less than .O1 percent.
-2 Log Likelihood
Goodness of Fit
347.150
264.386
chi-Square
df Significance
lm
Mode1 Chi-Square
Improvement
17 - 412
17.412
12
12
,1348
-1348
Classification Table for CLU3-1-3
Predicted
Percent Correct
Observed
1.00
1
3.00
3
63.16%
63.08%
Overall
63-12%
Variable
S.E.
.3163
.1486
,0555
.O107
-3890
.1342
,5459
.1834
.5859
.4796
.4449
,5346
20.9560
RES P-A-1
MAR ST 1
PRAIRIII
Constant
Wald
Sig
.O379
1.0883
,0109
4.2835
.3020
-5614
.8456
.2969
.9167
.O385
.5827
.4537
-5806
-8038
.8933
-3807
,6559
.7180
.3052
.O617
.O180
.7685
.1986
.1304
4.1559
.O415
Observed Groups and Predicted P r o b a b i l i t i e s
I
I
1
Predicted
1
I
-75
1
Prob:
O
.25
.5
Group: 111111111111111111111111111111333333333333333333333333333333
P r e d i c t e d P r o b a b i l i t y i s of Membership for 3.00
synibols: 1 - 1 . 0 0
3.00
3
Each Symbol R e p r e s e n t s 1 Case.
-
-> LOGISTIC REGRESSION ~ 1 ~ 3 - 2 - 3
->
/METAOD=ENTER var40-1 hhsize-1 hhi-1 resp-a-l marst-1 resp-e-1
->
status-1 atlant-l quebec-1 ontari-1 prairi-1 bc-1
employ-1
178
Total number of cases:
428 (Unweighted)
N d e r of selected c a s e s :
428
Number of unselected cases: O
Number of selected c a s e s :
428
Number rejected because of missing data: 133
Number of cases included in the analysis: 2 9 5
Dependent Variable Encoding:
Original
Value
2.00
3.00
Interna1
Value
O
I
179
Dependent Variable..
CLU3-2-3
Beginning Block Number
-2 Log Likelihood
Initial Log Likelihood function
O.
404.7945
* Constant is included in the model,
Beginning Block Number
1.
Method: Enter
Redundancies in Design Matrix:
Variable (s) Entered
1..
VAR40-1
HHS IZE-1
HHI-1
RES P-A-I
MAR-ST-1
RLso E 1
EMPLOYI~
Estimation terminateci at iteration number 3 because
Log Likelihood decreased by less than .O1 percent.
-2 Log Likelihood
Goociness of Fit
380.170
293.252
Chi-Square
Mode1 Chi-Square
Improvement
24.624
12
24.624
12
Classification Table for CLU3-2-3
Predicted
2.00
3.00
2 1
3
Observed
2.00
3.00
df Significance
3
Percent Correct
78.79%
40.00%
Overall
.O167
.O167
61.69%
---------------------Variable
3
180
Variables in the Equation
S.E.
Wald
df
----------------------Sig
R
ExpW
VAR40-1
HHSIZE 1
RES P-E-1
EMPLOY-1
STATUS-1
ATLANT-1
Observed Groups and Predicted P r o b a b i l i t i e s
l
I
1
Predicted
1
I
1
Prob:
O
-25
.5
-75
Group: 222222222222222222222222222222333333333333333333333333333333
Predicted Probability
- is of Membership for 3-00
Symbols: 2 - 2.00
3 - 3.00
Each Symbol Represents 1.25 Cases.
APPENDIX VII:
QUESTIONNAIRE
CONSyMER MAIL PANEL
of Canada Lïmited
77 61oor Stmt West. 12th noor. Toronto. Ontario MSS 3A4
1200, McGlIl College, Suite 1660. Momal (Québec) H3B 4G7
Mukrt Fa-
Oear panel rnsmtw,
In my S u m m Flexibut you told me tM eiher Um femaIe or the male heaâ of your househoid had taken msdicaüon(s)
pnsaibeâ by a dodor h the prsvfousâweke montht. The person bking the medication also very kfndty agrsad to amplete
a qusstioM)Ùn about pfusxiptron dnip wage. The purpaa of the questionnaire Ir to find out what Canadians krow and
want to krow abut prssafption dniqs; and w h l sou^ are uscd to obtain th& Information. As I menüoned in the Summn
~exibus,r futttier p r p s e k to halp a postqnduets univenity dodent ampiete a rnastefs degree.
When answœfng thb qu&onnain, piaw think onty about the prwaipüon th& was m o d mcsntiy fillad for you. and
answer the quedons about thrt d m g only. If, by any chance. you hacl m o n than one prescription fiIIcd at Vie same tirne.
plcase anthe quesüoru about only one of them. whichever one you wish to ctiooje.
W o n d# you h w e
pmsalptlonfillad?
Withh the past week ..........................
1 -2wea!aago
..................................
3 -4weekago ....-....-.................*--..*
O
1 2 montfit ago .....,,,.............-.m..--...
O
3 6 monttu ago .....*.............*............
0
7 11 months ago
O
1 yaar ago
0
Mors than one year aga
U
-
-
-
.........*....................
..........................................
.....................
Was this plbJaipti0n a refill of ?n old prescription or a new prescription 0.0. a dnig yau had never taken befon)?
Am you taking mis medication noYes...............O
No ................ 4 SKIP TO QU.4b
a
For how long have you been taking this medication? {PLEASE "Xn ONE BOX UNDER QU.&)
Qu,Qa
H m long
petn taking
L e s titan a week ............................--.a
1 2 weeks.-..................................... O
3 - 4we- ......................................... 0
1 2 montht.................................... a
3 6 manths................................... O
7 11 m o m
O
1 year
O
ivlore than one year
3
.
-
.
.
..................................
................................................
............................
For how long did you take this medicaîlon? (PLEASE
Qu.rlb
HOWlong
took for
a
O
O
O
O
O
O
ONE BOX UNOER QU.4b)
Whut &bas t h e nama of the medlc&on you wem presuiôcd? (PLEASE WRltE DOWN THE DRUG NAME
SHOWN ON THE LABEL OF YOUR BOITLE OR BOX. IF YOU CANT REMEMBER, PLEASE REFER TO THE
B0lTl.E OR BOX, IF AVAiiA8i.E. TO ANSWER THIS QUESTION).
For Mat wndiüoMllness was th& dntg pmsufbd?
1s th& condition a chronic or a short-term illness? (PLEASE @X"ONE BOX)
.................... 0
................................... ,.O
Chmnic ilInes............,
.
.
Short-term iltness
Other (PLEASEDESCRIBE)
Overat!, how knowlwaable do you feel you am about the drug p u were p M b e d ? (PLEASE ClRCLE ONE
NUMBER BELOW)
7.
-
~ o at ail
edaeable
1
2
Extrernely
know(edaeabI8
3
4
5
7
6
How knawledgeable do you feel you are about ... (PLEASE ClRCLE ONE NUMBER FOR EACH S T A f E M W T
8.
BELOW)
Not at ail
~owlodoea
blq
hou many tfmes psr day to take or use the medication 1
2
how much medîlcab'onto trke or use each tfms ................1
2
when the medication should be taken or used
1
2
how to take or use the medicaîjon ..............-....-.-.......-.....
1
2
~ s i â e ~ u f t h s r n d l ~ o...n.................. 1
2
2
posdble conmûs wfth mer madications
1
whd not to eat, d m or do whils Wng. or wing the
madlcatim
........-...........-............-........1
2
othw m e d i d a n t üta! can be usad to tnat the same
~
W
"*.-"
o
~
1
2
athœ non-dmg trwtmenîs thaî can be used to üeat
the srme ~ o n l i O such
n ~as hameopoüiy,
acuppinawd,etc.
1
2
wîtut the madkatron rchialiy does
.... 1 2
hou much the medication cwts
.
.
1
2
Not appR
G&!Q
O
.....
..................
O
O
O
................................
O
O
...........................
...... ..........,........
...................... ~~..........~.............-.......
.......................
.
.
...............
.
.
.
.
.
o
n
.
.
.
.
.
.
.
.
.
.
.
.
O
O
4
4
3
3
3
4
5
5
5
6
8
8
O
7
7
7
O
O
Nw WOU# liks you to te1 ma what information. if any,you rsosived about your p-paon
dnig from the dodor
rrhD preyibed yow mdicstlan. For each of Fe items klow, plùidicR. whethu Viis information was offaced
to you wdhovt having to ask about it, wt~etherR was givtn to you onty aRer you asked about R, or whether you weru
not gken any informationat ail. (PLEASE UX" THE APPROPRIATE BOXES UNOER aU.9a)
90)
9u.91
Told
G M
without
iRef
gsIng &@J
How many tîmes per day to take or
use îhe msdiwüm .............................................. O
How much madicatlon to taks or use each time......O
When the mudication should be taken or used .......
Not
given
O
O
D
Hawtotaksot~tnemedicaîion
.....OO
P-e
side atrscb of the medication ...................
CI
PassiMe conflidrr mth oüter medications................ U
[3
What not to esaf drink or do while taking or
using the rnedldon
U
U
m e r msdications that can be used ta mat the
sams condition/illness........................................
O
Othw non-dnrg tmaûnents that can be
rssed to tmat the same conditiontillness,
such as homeopathy, acupuncture, etc. .-.
..............
a
Whcitthemcdi~onactualiydoes......................... 0
fJ
Haw much the meâicltion costs
O
CI
Other bifomiation m h r s d (PLEASE DESCRIBE)
O
........................
...........................................
.....................
.
....
9b)
100)
a
a
a
Not
ippb
O
a
O
a
a
O
0
O
O
O
CI
O
O
O
Cl
I nrould a& liks you to tell me what information, if any, you received about- your
pnscrlptiondnig from the phannadst who fitled your prrs#lption. For each of the rtems
above, please indiwhether thb information was ofimed io yau wtthout haïfng ta ask
about it, Weti~ertt was g h n to you onIy aibr you asked about it, or wtiether you wem
not given any irrfomation Pt aI1. (PLEASE
THE APPROPRlATE BOXES UNOER
QUAb)
fi
Did you racdve any handwrinen w prlnted infamation about the h g from the dodor who prrsdbed the
AS MANY BOXES AS APPLYf
mediaüon? (PUASE
Qu.iOa
.............
TF
....................
Yes handwrflten infornation
Yw prlnted information
O
No dld not mceive any written or
printed info-on
................... 0
-.
O
1Ob)
4
bid you raceive any handwrftten or pfinted information about the drug from the
phumacist who tllied the pmscziptlon? (PLEASE
AS M M AS BOXES
A3 APPLyI
Qu.lOb
-B- a
cl
h
184
17a)
DM you n a i v e any handwrftten or pfinted information about your cond'tionfillneSS fmm the dodor who -kd
the medicéition? (PtEASE "X" ASMANY BOXES AS APPLY)
Q!sQ
Qu.173
QQGm
Y e s - Handmitten information ............
Yes - Pfinted information....................a
Phamam
0
-
N o Did not receive any m
e
n or
printed infornation ................... O
17b)
18s)
0
--
How siltidled niws you with information the doctor gave you about your wndition/itiness? (PLEASE T ONE
=X)
Ph-
Doaor
.........,........,... ..13
.........-.....-.....-................... O
.................. O
Very satistied
Quito saisfiecl
Not very satisfied
Not at al1 saüsfied
18b)
A
DM you nceivs any handwntten or printed information about your
c o n d i i ~ ~ l l n e from
s s the phamadst who filled the pm.ptfon? (PtEASE "X"
ASMANVAsBOXESASAPPLY)
0
[=I
a
0
How How wem p u witn information the phumacist gave p u about yoor
conditfoMifncssi, (PUEAS€ O X " ONE BOX)
n g the information p u wem-given by the doctor or phsrmacirt. did you ohlon information ab+ the
condiùon/niness for which you a m taking medication h m any dher sources, sudi as from a drug Company, fnends
or farnily, magazine or newspaper artfdes, or m e r h e m pmftssionals you had çontad wiVi?
19.
v
20.
From which other source(s) did you obtain information about your wndiionlillness? (PLEASE WRlTE iN AU
OTHER SOURCES USED)
BT SOURCE:
2ND SOURCE:
3RD SOURCE:
21.
-
WhPt information did you obtain frorn thisRhese source(s)? (PLEASE @XW
AS MANY BOXES AS APPLY)
.
.
........
0
General dcsaipüon of the condMon/illness.............
Dewfption of syrnptoms nomally experienced by patients .....0
Information about different dnig trements ..............+.....,.......
Information about non-drug tnatments such as
homeopaîhy, aarpundure. etc. ..............................................[3
Changes in diet, exercise, etc. to be made ............................. [3
M e r (PLEASE DESCRIBE)
..O
22
What m e r infomaüon, if any, would you I i b to receive about your condition/inneSS?
23.
Now I would like you ta tell me M a t you would do if you saw information about a new prescription drug (1.e. a U n i 0
thaî you didn't know about befon) to t n a t the wndih'on/illness you are, or were. suffedng fmm. Plea~earde a
number on t :chof the scales to indicate how Iikely you would bc to ,..
Not at al1
€xtremtly
W Y
uk a dodor about üie drug
1
aska phatmacist about the dmg...................................................... 1
iind out mom irrfomation about the dnig on your own
1
ask a dodor to pmsaibe the drug for you ........................................ 1
wait for more infomatfon lo becorne availabie
about the d ~ ...............................................................................
q
1
pay no attention to iî........................................................................ 1
.............................................................
.....................
2
2
2
2
2
2
3
4
4
4
4
5
S
S
5
6
6
3
3
4
4
5
5
6
6
3
3
3
8
6
w
7
7
7
7
f
7
How satisffed were you with infomation the doctor gave you about your prescription medication? ( P M E "XW
ONE80X)
-
Qu.llb.
PhannaR
pouor
Very satisfied ..................................... O
Quite satlsfied ...,................... ............ O
a
Nat very satlsfied .............................
Not at al1 satislied
0
...............................
O
H w saWied wem you with information the pharmacist gave you about your
prsscrlption madication? (PLEASE "X" ONE BOX)
Exduâing the infonpation you wem ghren by the doclor or phamaast, dki you obtain information about your
pmsai on medicpaon fmm any mer souras. such as from the Company who makes the drug. Mends or family,
mag$Pe ar n-pr
arüdes. or orner heaith pmfessioripls you had contact with?
Fmm which ather sovcbs dM you obtain information about p u r ptes#Iption mcdication? (PLEASE W R ï E IN ALL
OTHER SOURCES USED)
13T SOURCE:
2ND SOURCE:
3
m aomcE:
What inlomiation did you obtain from WisRheso source(s)? (PLEASEUX" AS MANY BOXES AS APPLY)
How mmy Urnes par day to take or uta the rndcation..................
How much medication to take or use eadi time ............................. O
Whsn the medication shouM be taksn or useci .........-.....................
How to take or use the medicaüon ....................................-..... 0
PoHbls side effet% of the medication .......................................... a
Possible confiidt with other medications .............................-....... 13
W?m! not to eat. drink or do while taking- or wing the
msdicotion...................................................................................
a
0 t h medicafons
~
th& can be used to mat the same condition/
n
iünOthef non-dnrg trsatments that can be used ta treat the
sama candillonmlness, such as horneopathy, acupuncture. etc, 0
W h l the msdication actualiy does
O
How much the msdldon costs
a
OVisr (PLEASE DESCRIBE)
........ 0
.......................................................................................
...
............................................
.....................................................
What m e r infomation, if any, would you Iike to receive about your pe
rscp
oirtn
medication?
On P *lacd subjed. 1 woufd like you to tell me what information. ifany, you received about your conditian/illness
frum the d o d w who pricsaibed your medication. For each of the items below. please indicate whether t h e
infomiaüun was offerad to p u m u t having to ask ahut it, whether it was given ta you only aRer you asked about
it, or whether p u wem not qfven any i n f ~ r m ~ oabout
n
iî at ail. (PLEASE "XW THE APQROPRlAm BOXES
UNDER QU.1Sâ)
Told
wiaiout
pskinq
....,,.
.................................
General description of the condltion/illness
0
ûesalption of symptoms nomalfy
experlenc-nd by patients
O
Information about differsnt drug treatments......O
Information about non-drug treatments
wch as homeapathy, acupundum, etc
O
Changes in dia. axercise, etc.to be made .......U
m e r ( P W E OEsCRIBE)
...........
Giveri
aRar
ask'nq Q-
G i
aRn
as-
givm
Not
Not
apoli-
Told
wïthout
O
Ij
0
U
a
O
O
0
ff
D
O
i3
0
O
a
O
O
O
C
I would a h fike you to tell me what information, if any, you mcehred about. your
conditionAllness fmm the phatmadst who fflled your medication. For each of the Rems
above, please fndicate whether this infurmationwas offsrad to you without havïng to ask
about il, whether it was given to you only aiter you asked about it or whether you were
nOt gbm any infornation about it at all. ( P U E
THE APPROPRUSTE BOXES
UNDER QU.lôb)
(
O
n
]
Nol
appti-
G@S
n
O
o
0
Not
givcri
U
n
O
I
0
0
A
3
a
I
Q
a
186
24.
For each datement below, pleass &de one number to show how rnuch you agw or disagree with it.
disaaree
I usualh as15 the dodctr westions about the dnrg(s)
-- a b e d at the time of my medical visit ................... 1
It is helphil to ask my Men& or farnily questions
about the drug(s) I am taking.......................................
1
always provides me with infomation
about the medication he or sbe gives me .................... 1
The pharma&
The company who makes the drug should tell me
wtmt f neeû to ltnow about my medication
....,...--..,.....1
My dodor is generaRy open to questions about the
d r u ~ ( s )he w she prrsciibes
......................................1
When them is mors than m e medication to ?mat
my condition, I should be told a h u t eadi one
............. 1
I uswBy ask the phannacist questions about the
dnig(s) when I am having my prexrfption(s) flled
.......1
lt b batsr to miy l e s on dodon and mors on your
omi cornmon sensa when 1 cornes to ming for
yow body
- .
.....................................
........................ 1
f oRsn Uks to get irrhmüon about my medicaion(s)
f m book and 0th- m e n matsrials
........................ 1
I dont think dodon knaw anaugh about the
âmgs they prescribe
............................ -..-..............1
R i e n is not enough prfvay at the phamiacy
counter to ask quesiions about the medication 1
haw been prrscribed.................................................. 1
I f d I knuw mors about rny medication than do
ather people who toks the same medication................1
I feei M Mabut taWng dnigs when I am
knowledgeable about them...-.-....-..-.......-..................
1
Informationabout my medication b tao hard for
me to undentand
.............-..~....~.............~..............
1
Rsmernbefingto taka my medication is oRen
dimaitt for me
.....................................................1
Them is no need to ask questions about
@
-oni
dnigs if you tmst the dodor ......................1
I feef I am m o n ccincerned about my health than
am other peopie my age
............................................. 1
The doctor ahvays provides me with information
about the medication he or she ptoscribes for me ....... 1
D N companies
~
ought to inform consumers about
Malth issues and medications...................................
1
1 wuit infornation abut rny -ption
dmg(s) so
1 can ddmine if the rndcaüon is worlong or not ...... 1
I feel I dont know enough about rny medication
to make infomed choices about which
medication I should take...........................................
1..
it's always M e r to seek pmfessional help than
to t f yto treat younen
...............*..........'...-.-....--.......
1
1 like to know as much as I GUI about the
medication Vie dodor prwafbes for me ......................1
l feui I know mors about my condition Vian
do mer people experiendng the same heaHh
condition as me
............................................................1
I fwl I know where to find al1 the infornation
.........................................
I need on my m e d i ~ o n
1
1 want tnfonnation about my prssaiption drug(s)
so I a n decici8 if I should take the mecfication............ 1
rather have the dodor make the dedfon
Cornpletefy
I WOU#
about my treatment Vian for him or her to give
me a whole lot of cltoices............................................
1
2
3
4
5
6
7
When them is more than one medication to treat
my andiion. I shouM be ôtlowed to c h o s e
wbich madication 1 want to take ................................... 1
2
3
4
5
6
7
The dodor uses wrds I dont understand wtien
tclng me about the dmg helshe is preseribing............ 1
2
3
4
5
6
7
It wuld be hcipful if the dactor pmvided written
infomaüon about the dnrg(s) halshe presatbcs
.......... 1
2
3
4
5
'6
7
fhe pitmacU b tbo for away behind the caunter for
me to ask queafaru about the drug(s) presuibed........ 1
2
3
4
5
8
7
it woulj be easler to ask the doctor questions if
I though! of s o m quedons and wrote them
domi Wria rny appointment
2
3
4
5
6
7
.........-.....-..................
...1
25,
Cornpletely
4QSS
dlsaaree
Have yau cwr asked a dodor about a pmsdption drug you heard or read about?
...............
................ O --b
Yes
No
SKlP 10 QU.29
26.
What wu the name o f the medication you aslred the dodor ahut?
27.
What condition was thb medidon intended ta treat?
28.
Did the dodor preuri'bethis medidon for you?
29.
Have you evef asked a phanaart if there is a %enerkquivalent, or Qieaper substituts. avalable for a dnig a
dodor prescrlbed for you?
Yes............... O
No ................U
-
30.
Has a phamacid ever suggested a generic equivalent. or deaper suMlute. 10 you insîead of the brand a d0ct0r
pnscribed for you?
Yes...............a
N o ................Cl
31.
Dfd you buy the generic, or dieaper substitute. suggested by your pharmacist?
32
About how many tirnes in the past year have you visited a dodot?
34.
Ars you coverod by a drug plan that pays for your presaipüon merlkation?
SKlP TO QU.32
-
..............a
No ........................... 4 SKlP TO QU.37a
Yes in part
Yes in full ................D
-
35.
1s this a gwernment plan or a prfvate hem plan (kg. from your employer)?
.............. +SKlP 10 QU.37a
80th........................O
Govemment
Private plan
...............
36.
Whidi form of payment dues your private plan use?
-
Card at pharmacy........... .............~............................. O
Pay and reimbursed.................................................... CI
Other (PLEASEDESCRISE)
.U
37a)
each of the sources listecl bclow in cornmunicating
dnigs. On a a l e of 1 to 7, with '1' being 'not at aû klievabtenand T being
infomiation to you about p-püon
'ex!mmdy bellevable'. plertse rate sach of the following sources in temis of ils believability in cornrnunicating
informationto you about prescription medicaüons. (PLEASE ClRCLE ONE NUMBER UNDER QU.37r FOR EACH
SOURCE)
Now I would Iike p u to tell me how believable you find
6
7
7
7
Television prograrns
Talking to a dodor
Talking to a phamaast
6
7
AGovemment Heatth Department
6
6
1
2
3
4
5
1
1
1
2
2
2
3
3
3
11
2
3
4
4
4
5
5
5
6
8
6
7
7
7
4
S
6
71
A
37b)
Using the same scak, please rate each of the sources in t e m of its bdievabiüty in
communicaüng information to you about conditionslillncses. (PLEASE ClRCLE ONE
NUMBER UNDER QU.37ü FOR EACH SOURCE)
38.
Whkh do you think W the best source of information about pnrcription dmgs?
39.
Wch do yau think is the b.st s o u m of information about heaith conditions or illnesses?
4û.
Am you
... (PLEASE 'X"
THE APPROPRIATE BOX)
a
The male head of howehoM ..............
The female head of household...........
THANK YOU VUW MUCH FOR TAKlNG THE TIME TO F i L i IN THIS QUESflONNAlRE!
YOUR CONTRlBUnON T0 THIS PROJECT IS GREATLY APPRECIATED.
IMAGE EVALUATION
TEST TARGET (QA-3)