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Social Work Dissertation
An Introduction to
Quantitative Methods
14/10/15 Class 8 Week 4
[email protected]
Module moderator
Aims
• Make quantitative research – and results
sections of these kinds of research papers
– less scary (less confusing?)
• Support you to take a more critical view of
what’s presented within quantitative
research studies.
Plan
 Look at the ‘anatomy’ of a quantitative research article,
focusing mostly on the methods and results section;
 Discuss examples of particular kinds of quantitative
methods used – AND what makes them ‘quantitative’;
 Discuss participant selection (‘sample), along with the
importance and validity/reliability of ‘measures’;
 Compare and contrast descriptive and inferential statistics
and explain when each would be used; and
 Throughout, use an example of a quantitative research
article and see if, together, we can make sense of it.
But first… The process of enquiry
...in Social Sciences
...in Social Work
Above from: The social work process. [On-line] Available:
http://openlearn.open.ac.uk/mod/oucontent/view.php?id=
398072&section=2.6 [Accessed 29 Nov 2012]
This process is reflected in
Sections of a research article:
 Background
 Aims/Hypotheses/Research Questions
 Methods
 The Results
 Interpretations
How? The methods of enquiry
• Experiments (e.g., compare S.W.
practice/interventions/populations)
• Observation (e.g., see what happens during SW
practice/interventions)
• Participant Observation
• Interviews (that collect quantitative data about
interventions)
• Focus groups
• Questionnaires (possibly used in interviews)
• Surveys (possibly used in interviews)
• Text/image analysis
• Etc.
What method is this?
Example
I want to know if Social Work students skip the
results section more than psychology students.
- IV = Type of student
- DV = Skipping the results section
So I ask:
1
2
3
4
5
Absolutely
Never
Skip it
Most of
the time I
don’t skip
it
About half
the time I
skip it
Most of
the time I
do skip it
Absolutely
Always
Skip it
Measures – What am I asking?
• A tool or instrument used to gather data
– (e.g., survey, IQ test, depression scale,
opinion poll)
• Provides an “Operational definition”
– (e.g., MAST to define ‘alcoholism’; poverty)
• Different levels of measurement (e.g.,
discrete/continuous) require different
tests/statistical analyses
• Can be critiqued based on noise, reliability and
validity.
Reliability and validity
Reliable measures are:
 stable over time (if they should be)
 consistent in terms of administration and scoring
 ‘internally consistent’ (items on the test measure the
same construct).
Valid measures:
 Measure the content and construct they are meant to
 Are related to other constructs in expected ways
(Creswell, 2014)
Sample – Who am I asking?
• Population (N) versus sample (n)
The selection process –
Probability/random sampling
Non-probability sampling
Simple
Convenience (who is available to
ask?)
Stratified
Purposive
Systematic
- Expert (people ‘in the know’)
Cluster
- Quota (e.g., ‘enough’ women)
Multi-stage (e.g., a combination of
above
- Snowball (ask current
participants to recommend
others)
Sampling – Why is it important?
Impacts on generalizability (or ‘external validity’) –
especially when using an experimental design:
– Interaction of selection and treatment (research
participants all have particular characteristics)
– Interaction of setting and treatment (experiment
conducted in a particular setting, therefore results
may not apply in a different setting)
– Interaction of history and treatment (experiment
conducted at a particular moment in time)
(Creswell, 2014)
You’ve picked the people and asked
the questions:
Now what?
• Use statistics to describe, compare and
draw inferences
Descriptive Statistics*
The three Ms: Mean, Median & Mode
Most commonly used measures of central tendency
 The mean = the average (the sum of all the scores / n)
 The median = the value that has many scores above it
as it has below it (the number in the middle)
 The mode = the most frequently occurring value
When the data fit more-or-less within the bell curve (i.e.,
the data are symmetrical), these three values are
approximately equal.
*Also included in this category: standard
deviation (which describes variance)
What do you mean, ‘bell curve’???
 The bell curve represents ‘normal distribution’
and can represent ‘variance’ in the data
 This is important not only for description but
for drawing conclusions (e.g., probability and
being ‘confident’ about results – more soon)
 Many types of data are normally distributed:
height, IQ, weight, blood pressure, resting
heart rates, food intake, etc.
But WHY???    
 Provides a standard frame of reference
 Reflects a number of psychological and social variables
 Helps us to understand “typical”/“atypical” and
‘probability levels’
AND MAYBE MOST IMPORTANTLY:
 Certain tests of statistical significance (or inferential
statistics) are used based on the assumption that the
data from the sample are normally distributed.
Inferential statistics:
I want to draw conclusions!
(a whistle-stop tour)
 Social scientists, don’t simply want to describe
scores – they want to draw inferences
 They want to know if the independent variable has
an effect and if hypotheses/predictions are
correct.
 So they use inferential statistics to identify
confidence/probability/significance.
Do Social Work students skip the results section more
than psychology students?
I WANT TO KNOW THREE PRINCIPAL THINGS:
1. Is there a difference between Social Work
students and psychology students?
OR: is there an effect of being a certain type of
student?
2. Is the difference statistically significant?
OR: is the difference the result of the IV (being a
specific type of student)?
3. Or….is it the result of error variables e.g.
individual differences? (also referred to as
chance factors)
So I use certain statistical tests
(depending on the data)
 T-tests (generally compares MEANS of two
groups) – (usually reported as “t”)
 Anovas (generally compares MEANS of two or
more groups) – (usually reported as “F”)
 Correlation (are the two variables related?) – (‘r’)
 Chi-square (used with categorical data) – (usually
reported as “Χ2”)
 And many, MANY more!
Correlations
 Seeks to establish whether there is a relationship
between two variables and
 How strong that relationship is
 Referred to in the literature using “r”
 Can be positive OR negative
 Values range from -1 to +1
DOES NOT IMPLY CAUSATION
Does this mean that ice cream causes people to drown?!?
No! Correlation does not imply causation
Probability levels (“p”)
 Conventional probability level – as chosen by
social scientists – also called confidence interval
 0.05 or 1/20 probability that the effect was the
result of chance
 95% confident that the effects resulted from the
IV manipulation and not chance factors
Three different ways to say the same thing
Exercise
Table 1. Descriptive Statistics for Key Constructs
Mean (SD)
Range
Social Work students
38 (5.96)
21 – 52
Psych students
29 (7.32)
20 – 55
Social Work students
2.70 (1.33)
1.77 – 3.86
Psych students
3.13 (2.49)
1.29 – 3.79
Social Work students
3.20 (0.87)
2–5
Psych students
2.76 (1.14)
1–5
Age
Grade-point average (GPA)
Skips results section
What statistics are used in the table above?
What are they telling you?
Exercise (cont)
“Based on an independent t-test, Social Work students were
significantly more likely to skip the results section than
Psychology students (t(1,48) = 2.24, p < .05).”
•
Can you identify which two numbers in the previous table
are being compared in the sentence above?
•
Is this an appropriate test to answer the research
question? Why or why not?
•
Thinking about the article that you were required to read
for today’s seminar, what inferential statistics are used –
and are these appropriate for the purposes of the
research?
The value of probability & rejecting
chance
 We can only reject the chance explanation when it is
highly improbable
 Improbability measured as: p ≤ 0.05
 The difference must be less than or equal to 0.05 in
order to reject the chance explanation
 So in journal articles when p < 0.05 you can assume a
significant effect/difference/association was found
Exercise
Based on the article you read to prepare for today’s
seminar, answer the following questions in small groups:
Looking at Table 2 (on page 73), what were the significant
results?
• What results were p < 0.05?
• What results were p < 0.01?
What is another way of explaining the difference between
the significant results “at the 0.01 level” and “at the 0.05
level”?
Recap
 It’s a lot to take in, so you will have to do some additional reading.
 As with most things, Practice, Practice, Practice.
 Look at the descriptive statistics and think about what they mean.
 Try and determine what they are comparing and then look at the
“p” value.
 Put it into your own words – and try to do this without using
numbers.
 Remember, if p < 0.05 then I am 95% confident that the
independent variable is having an effect on the dependent
variable.
And finally....
Pressing questions?
If you are unsure, Ask, Ask, Ask!
 Module co-ordinator
 Module moderator (bookable consultation
dates)
 Personal tutor
Sources
All of today’s topics can be found in:
• Alston, M. and Bowles, W. (2003) Research for Social
Workers: An introduction to methods (3rd ed). Abingdon:
Routledge (chapter 14 has some good info in it about some of
the inferential statistics we talked about)
• Jaccard, J. and Jacoby, J. (2010) Theory construction and
model-building skills. New York: The Guilford Press. (has some
good info about different kinds of statistical tests)
• Katzer, J., Cook, K.H., and Crouch, W.W. (1997). Evaluating
information: A guide for users of social science research.
Boston: McGraw-Hill. (Yes, I like this book!)
References
• Creswell, J.W. (2014) Research Design:
Qualitative, Quantitative and Mixed Methods
Approaches (4th ed.). Los Angeles: Sage.
• ANY research methods/statistics book ever
written….