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MODUL PERKULIAHAN
Metode
Penelitian IKuantitatif
Population and sampling
Fakultas
Program Studi
Psikologi
Psikologi
Tatap Muka
10
Kode MK
Disusun Oleh
MK61021
Ainul Mardiah, M.Sc
Abstract
Kompetensi
Dalam perkuliah ini dibahas population
and sampling
Mahasiswa mampu memahami
population and sampling
Population and Sampling
Survey Research
Survey research is based on the simple idea that if you want to find out what people think
about some topic, just ask them. That is, a survey is a structured set of questions or
statements given to a group of people to measure their attitudes, beliefs, values, or
tendencies to act. Over the years, people have responded to surveys assessing everything
from political preferences to favorite sexual activities. The method has been around for some
time, as you can tell from Box 12.2.
Unlike most of the methods described in this text, surveying usually requires careful attention
to sampling procedures. This point requires some elaboration. For most of the research
methods you’ve read about, the emphasis has been on establishing relationships between
and among variables. From some of the research you have read about in this text, for
instance, you know that researchers have been interested in such topics as the effects of (a)
the movement of a room on children’sbalance, (b) closing time on perceived attractiveness,
(c) coach effectiveness training on children’s self-esteem, and (d) looming and self-efficacy
on fear of spiders. For studies such as these, researchers naturally hope that the results will
generalize beyond the people participating in them, but they assume that if the relationship
studied is a powerful one, it will occur for most individuals, regardless of how they are
chosen to participate in the study. Of course, whether this assumption turns out to be true
depends on the normal processes of replication and extension discussed in several places in
this book. So, in a study on the capacity limits of short-term memory in adults, it is not
necessary to select a random sample—virtually any group of reasonably fluent adults will do.
That is, for most research in psychology, it is sufficient to choose, as participants, what is
known as a convenience sample.
This is a group of individuals who meet the general requirements of the study and are
recruited in a variety of ways. Often they are from the ‘‘subject pool’’—general psychology
students being asked to participate in a study or two. You learned about some of the ethical
issues related to these pools in Box 5.3. Sometimes a specific type of person is recruited for
the study, a convenience strategy that is called ‘‘purposive’’ sampling. For instance, when
Stanley Milgram first recruited participants for his obedience studies, he placed ads in the
local newspaper asking for volunteers. He deliberately (i.e., purposively) avoided using
college students because he was concerned that they might be ‘‘too homogeneous a
group.... [He] wanted a wide range of individuals drawn from a broad spectrum of class
backgrounds’’ (Milgram, 1974, p. 14).
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The Milgram study also illustrates a practical reason for using convenience samples.
Recruiting participants for a study can sometimes be time-consuming and frustrating.
Milgram could have tried a more sophisticated sampling approach (like the ones you are
about to learn), but even if he had, and selected a sample from some larger group, there
was no guarantee that the people he selected would be interested in participating in the
study. It was simply more efficient to advertise widely and hope to get as many volunteers as
possible.
Although convenience samples, combined with the normal processes of replication, are
adequate for most research in psychology, they are generally inappropriate for survey
research. This is because the goal of most survey research is different—the focus is not on
examining relationships among variables; it is on developing anaccurate description of the
attitudes, beliefs, behavior tendencies, or values of a specifically defined group of people.
Good surveys require a form of sampling called probability sampling.
Probability Sampling
This general strategy is used whenever the goal is to learn something specific about an
identifiable group of individuals. As a group, those individuals are referred to as a population,
and any subgroup of them is a sample.In probability sampling, each member of the
population has some definable probability of being selected for the sample. Sometimes it is
possible to study all members of a population.
For example, if you wanted to learn the attitudes of all of the people in your experimental
psychology class about the issue of animal experimentation and did not wish to generalize
beyond that class, you could survey everyone in the class. In this case, the size of the
population would be the size of your class. As you might guess, however, the population of
interest to a researcher is usually much too large for every member in it to be tested. Hence,
a subset of that population, a sample, must be selected.
Even though an entire population is seldom tested in a study, the researcher hopes to draw
conclusions about this broader group, not just about the sample. Thus, in survey research, it
is important for the sample to reflect the attributes of the target population as a whole. When
this happens, the sample is said to be representative; if it doesn’t happen, the sample is said
to be biased in some fashion. For instance, if you wanted to investigate student perceptions
of college life, it would be a serious mistake to select people from a list that included only
those students living in college residence halls. Because off-campus residents and
commuter students might have very different attitudes from on-campus residents, the results
of your survey would be biased in favor of the latter.
Perhaps the most famous historical example of biased sampling occurred during political
polling in the presidential election of 1936. As it had been doing with reasonable success for
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several previous elections, the magazine Literary Digest tried to predict the election outcome
by sending out about 10 million simulated ballots to subscribers, to others selected from a
sample of phone books from around the country, and to others from motor vehicle
registration information (Sprinthall, 2000). Close to 25% (almost 2.5 million) of the ballots
were returned to the magazine; of these respondents, 57% preferred the Republican
candidate, Alf Landon, and 40% chose the incumbent president, Franklin Roosevelt. In the
actual election, Roosevelt won in a landslide with more than 60% of the vote. Can you guess
why the sample was biased?
Although the editors of Literary Digest were aware that their own subscribers tended to be
upper middle class and Republican, they thought they were broadening the sample and
making it more representative by adding people chosen from phone books and car
registration data. In fact, they were selecting more Republicans. In the midst of the Great
Depression, practically the only people who could afford phones and cars were members of
the upper middle and upper classes, and these were more likely to be Republicans than
Democrats. So, in the survey the magazine actually was asking Republicans how they were
going to cast their votes.
You might have noticed another flaw in the Literary Digest survey. A large number of ballots
was returned, and the magazine was quite confident in its prediction of a Landon victory
because the data reflected the views of a substantial number of people—about 2.5 million.
Note, however, that not only does the total represent only one-fourth of the ballots originally
sent out, but also the returns were from those who chose to send them back. So, those
responding to the survey tended to be not just Republicans, but Republicans who wished to
make their views known (in light of which, the 57% preferring Landon actually looks rather
small, don’t you think?).
This self-selection bias is typical in surveys that appear in popular magazines and in
newspapers. A survey will appear, with an appeal to readers to send in a response. Then, a
month or so later, the results of those who returned the survey will be reported, usually in a
way implying that the results are valid. The person reporting the survey will try to impress
you with the total number of returns rather than the representativeness of the sample. An
example of this ploy is a well-known report on female sexuality (Hite, 1987). It claimed,
among other things, that more than 90% of married women felt emotionally abused in their
relationships and a substantial majority reported dissatisfaction in marriage. When criticized
because the survey was sent only to a select group of women’s organizations and that only
4.5% of 100,000 people returned the survey, the author simply pointed out that 4,500 people
were enough for her (just as 2.5 million people were enough for Literary Digest). But
research that uses appropriate probability sampling techniques generally shows that
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Pusat Bahan Ajar dan eLearning
http://www.mercubuana.ac.id
satisfaction/happiness in marriage is actually quite high. The Hite data were misleading, to
say the least.
As a scientific thinker, you should be very skeptical about claims made on the basis of
biased samples. The lesson, of course, is that if you want to make an accurate statement
about a specific population, you must use a sample that represents that population and you
must select the sample directly, not rely simply on who decides to return the survey. If you
have no choice but to use data from a self-selected sample, and this happens sometimes,
you should at least try to determine if the attributes of the sample (e.g., average age,
income) match the attributes of the population you have in mind. Even then you need to be
cautious in the conclusions you draw.
Random Sampling
The simplest form of probability sampling is to take a simple random sample. In essence, all
this means is that each member of the population has an equal chance of being selected as
a member of the sample. To select a random sample of 100 students from your school, for
instance, you could place all of their names in a large hat and pick out 100. In actual
practice, the procedure is a bit more sophisticated than this, however, usually involving
software2 that uses a random number table. To learn the essence of the procedure, work
through the example in Table 12.1, which shows you how to use random numbers to select
a sample of 5 individuals from a population of 20.
Simple random sampling is often an effective, practical way to create a representative
sample. It is sometimes the method of choice for ethical reasons as well. In situations in
which only a small group can receive some benefit or must incur some cost, and there is no
other reasonable basis for decision-making, random sampling is the fairest method to use. A
famous example occurred in 1969 in the midst of the Vietnam War, when a lottery system
was established to see who would be drafted in the army. For obvious reasons of fairness,
birthdays for each of the 365 days of the year were to have an equal probability of being
selected first, second, third, and so on. Unfortunately, the actual procedure had some bias
(Kolata, 1986). Capsules, one for every day of the year, were placed in a large drum one
month at a time. The January capsules went in first, then the February ones, and so on. The
drum was rotated to mix the capsules, but apparently this did not succeed
TABLE 12.1 Selecting a Random Sample Using a Table of Random Numbers
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completely because when the dates were drawn, those capsules entering the drum last
tended to be the first to be picked. This was not a good time to have a birthday in December.
There are two problems with simple random sampling. First, there may be some systematic
features of the population that you might like to have reflected in your sample. Second, the
procedure may be impossible if the population is extremely large. How could you get a list of
everyone in the United States in order to select a simple random sample of Americans? The
first problem is solved by using stratified sampling, and cluster sampling solves the second
difficulty.
Stratified Sampling
Suppose you wanted to measure attitudes about abortion on your campus, and the school’s
population is 5,000 students, of whom 3,000 are women. You decide to sample 100
students. If you take a simple random sample, there are probably going to be more women
than men in your sample, but the proportions in the sample won’t match those in the
population precisely. Your goal is to make the sample truly representative of the population
and, on a question like abortion, there might be important differences of opinion between
males and females. Therefore, if your sample happens to be overrepresented with males, it
might not truly portray campus attitudes. In a situation like this, it would be a good idea to
decide ahead of time that if 60% of the population is female, then exactly 60% of the sample
will also be female. That is, just as the population has these two layers (or ‘‘strata’’), so
should the sample.
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In a stratified sample, then, the proportions of important subgroups in the population are
represented precisely in the sample. In the previous example, with a goal of a sample of
100, 60 females would be randomly sampled from the list of females, and 40 males would be
selected from the list of males. Note that some judgment is required here: The researcher
has to decide just how many layers to use. In the case of the abortion survey, males and
females were sampled in proportion to their overall numbers. Should each of the four
undergraduate classes be proportionately represented also? What about Protestants and
Catholics? What about left- and right-handers? Obviously, the researcher has to draw the
line somewhere. Some characteristics (religion) may be more critical than others
(handedness) in deciding how to stratify the sample. Based on what has occurred in prior
research or the goals of the current study, it’s up to the researcher to use some good sense.
The intent of a stratified sample, to represent subgroups proportionately, can also be met in
a nonprobability convenience sample. When using a subject pool, for instance, if you wished
to have 60% females and 40% males in your study, and you hoped to test 100 participants,
you could simply sign people up in your study untilthe 60 females and 40 males had
participated. This type of nonprobability sample iscalled a quota sample. Cluster Sampling
Stratified sampling is an effective procedure, but it still doesn’t solve the problem of trying to
sample from a huge population, when it is often impossible to acquire a complete list of
individuals. Cluster sampling, a procedure frequently used by national polling organizations,
solves the problem. With this approach, the researcher randomly selects a cluster of people
all having some feature in common. A campus survey at a large university might be done
this way. Ifa researcher wanted a cross section of students and stratified sampling was not
feasible, an alternative would be to get a list of required ‘‘core’’ classes. Each class would be
a cluster and would include students from a variety of majors. If 40 core classes were being
offered, the researcher might randomly select 10 of them and then administer the survey to
all students in each of the selected classes.
If the selected clusters are too large, the researcher can sample a smaller cluster within the
larger one. Suppose you wanted to find out how students liked living in the high-rise dorms
on your campus, which you’ve defined operationally as any dorm with eight floors or more.
Further suppose that fifteen of these buildings exist on your campus, housing a total of 9,000
students. Using cluster sampling, you could first select six of the buildings (each building = a
cluster), and then, for each building, randomly select three floors and sample all of the
residents (about forty per floor, let’s say) of the selected floors in the selected dorms. This
would give you an overall sample size of 720 (40 × 3 × 6). Notice that you also could
combine some elements of stratified sampling here. If ten of the dorms house women, and
men live in the remaining five, you might select your first clusters to reflect these proportions:
four female dorms and two male dorms.
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Varieties of Survey Methods
Most surveying today occurs in the form of written questionnaires that are sent through the
mail or administered in some more direct fashion (e.g., to a class of students). There are
other techniques for collecting survey data, however, and each survey type has its strengths
and weaknesses. In addition to written questionnaires, survey data are sometimes collected
through face-to-face interviews, sometimes through telephone interviews, and more recently,
via the Internet. Researchers sometimes combine methods, a ‘‘mixed-mode’’ approach
(Dillman, Smyth, & Christian, 2009).
Interviews
You have undoubtedly heard of the Kinsey Report, perhaps the most famous sex survey of
all time. Completed in the years just following World War II, it resulted from detailed, face-toface interviews with thousands of men and women, and it yielded two large books on sexual
behavior in America, one for men (Kinsey, Pomeroy, & Martin, 1948) and one for women
(Kinsey, Pomeroy, Martin, & Gebhard, 1953). Although you might think that Kinsey’s
interview survey format might have prevented people from describing the intimate details of
their sexual attitudes and behaviors, especially considering the historical era in which the
studies were done, this apparently did not occur. In fact, conservative postwar America was
shocked by the frequency of the reported levels of premarital sex, masturbation, and
adultery. The books, although written in dry academic prose and loaded with tables and bar
graphs, nonetheless reached best-seller status and made Kinsey a celebrated yet
controversial figure. Accused by some of contributing to a moral decline and even of being a
Communist, he was regarded by others as a pioneer in the scientific study of an important
aspect of human behavior (Christenson, 1971).
The interview format for surveying has the advantages of being comprehensive and yielding
highly detailed information. Even though the interviewer typically asks a standard set of
questions, the skilled interviewer is able to elicit considerable information through follow-up
questions or probes. Having an interviewer present also reduces the problem of unclear
questions—the interviewer can clarify on the spot. Sampling is sometimes a problem
because, in many cases, sizable segments of the population may not be included if they
refuse to be interviewed, cannot be located, or live in an area the interviewer would prefer to
avoid. For example, the poor and homeless are usually underrepresented in national
surveys using the interview format. Interviews can occur in a group format—the focus group
procedure described in Chapter 10 is an example.
Besides sampling issues, other major problems with the interview approach arecost,
logistics, and interviewer bias. Interviewers need to be hired and trained, travel expenses
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Pusat Bahan Ajar dan eLearning
http://www.mercubuana.ac.id
can be substantial, and interviews might be restricted to a fairly small geographic area
because of the logistical problems of sending interviewers long distances. And despite
training, there is always the possibility that interviewer bias can affect the responses given in
the face-to-face setting. For example, cross-race bias may exist, resulting in systematic
differences between interviews with members of one’s own race and members of other
races.
The careful researcher using interviews will develop a training program to standardize the
interview process as much as possible, and sometimes certain types of interviewers may be
trained for specific purposes. For example, middle-aged female interviewers may elicit more
cooperation in an interview survey of retired women than young male interviewers, who may
not get past the door (van Kammen & Stouthamer-Loeber, 1998)
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Pusat Bahan Ajar dan eLearning
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