1. Statistics for the Social Sciences
Psychology 340
Spring 2009
Introductions & review of some basics

2. This course How is this course different from PSY 138?
Format: Longer lectures, but no separate lab times
Content: review of PSY 138 and beyond (remember the “beyond 138”)
Dealing with more complex situations (more than 2 variables)
Hypothesis testing with:
Correlation and regression
Multiple regression
Tests for differences with more than two groups

3. PSY 138

4. This course

5. Course Format Lectures - will take up most of class time
Homework - practice items, ARE collected for credit
Labs - practice items, NOT collected for credit
Two parts:
By hand step-by-step example(s)
Large SPSS dataset practice question(s)
Exams - three cumulative exams
Quizzes - on-line Blackboard quizzes (about 10 of them), based primarily on textbook readings

6. Variability is key
A the heart of research methodology and statistics is variability
Variables - characteristics with values that aren’t constant (across individuals, time, place, etc.)
We’re interested in explaining (predicting) the variability of variables
We use experimental control to try to constrain variability to make it easier to see how different variables affect each other
We use statistical procedures to examine which variables vary together (and which don’t)

7. Statistical analysis follows design
Statistical analysis follows from the design of a study
Our decision tree helps us ask the right design questions which will lead us to the appropriate statistical test

8. Statistical analysis follows design
Decide if
there is a
difference
Experimental &
Quasi-experimental studies
Testing for differences between groups (conditions)
Vs.
Decide if
there is a
relationship
between variables
Observational studies
Testing for similarities between variables
This is a generality, there are exceptions. Towards the end of the course we’ll see that the two may be considered essentially the same kinds of analyses

9. Basic Research Methods
Observational study
Researcher observes and measures variables of interest to find relationships between the variables
No attempt is made to manipulate or influence responses
Experimental methodology
One (or more) independent variable(s) is manipulated while changes are observed in another variable (dependent)
Used to establish cause-and-effect relationships between variables
Uses extensive methods of control to minimize extraneous sources of variability
Quasi-experimental methodology
One (or more) of the independent variables is a pre-existing characteristic (e.g., sex, age, etc.)

10. Different basic methods
Experimental versus Observational methods
Experiments involve manipulation of variables
Observational methods involve examining things as they already are

11. Example Issue: What’s the best way to study for a test? Observational
Randomly select individuals
Randomly assign to groups
Crammed study group
Distributed study group
See how they do on a test
Experimental
Randomly select individuals
Watch their study habits
See how they do on a test

12. Experimental Control Our goal:
to test the possibility of a relationship between the variability in our IV and how that affects our DV.
Control is used to minimize excessive variability.
To reduce the potential of confounds.

13. Logic of experimental control
Sources of Total (T) Variability:
T = NonRandomexp + NonRandomother Random
Variability in Test Performance
Imprecision in
manipulation (IV)
& measurement (DV)
& random varying
extraneous variables
Manipulated
independent
variables (IV)
variables which
covary with IV
(condfounds)
Study method:
Crammed
Distributed
Distributed studiers never get to practice problmems
Different study times, different study methods, etc.

14. Logic of experimental control
Sources of Total (T) Variability:
T = NonRandomexp + NonRandomother Random
Constrain variability by carefully levels of IV
Eliminate counfounds
Use good measures
Experimental procedures are used to reduce R and NRother so that we can detect NRexp.
That is, so we can see the changes in the DV that are due to the changes in the independent variable(s).

15. Weight analogy Imagine the different sources of variability as weightsNR
exp
other R NR
other
Treatment group
control group

16. Weight analogy
If NRother and R are large relative to NRexp then detecting a difference may be difficult R NR
other R NR
exp
other

17. Weight analogy
But if we reduce the size of NRother and R relative to NRexp then detecting gets easier NR
other R R NR
exp
other

18. Logic of observational approaches
Suppose that you wish to predict exam performance using an observational method
Sources of Total (T) Variability:
T = NonRandomother Random
Variables that do
covary with test
performance
Variables that don’t
covary with test
performance
Observe and record variables, but don’t know which group they’ll fit into
Hours of sleep
Total study time
Test time
That’s what we’ll use statistics to find out
Study topic
Breakfast food

19. Logic of observational approaches
Total variability it test performance
Total study time
r = .6
Unexplained variance
64%
Some co-variance between the two variables
If we know the total study time, we can predict 36% of the variance in test performance

20. Logic of observational approaches
Total variability it test performance
Total study time
r = .6
Test time
r = .1
Unexplained variance
51%
A little co-variance between these test performance and test time
If we add it to study time, then we can explain more the of variance in test performance

21. Logic of observational approaches
Total variability it test performance
breakfast
r = .0
Total study time
r = .6
Test time
r = .1
Unexplained variance
51%
No co-variance between these test performance and breakfast food
If we add it to the other two, then we can NOT explain more the of variance in test performance

22. Logic of observational approaches
Total variability it test performance
breakfast
r = .0
Total study time
r = .6
Hrs of sleep
r = .45
Test time
r = .1
Unexplained variance
40%
Some co-variance between these test performance and hours of sleep
If we add it to study time, then we can explain more the of variance in test performance (but notice what happens with the overlap)

23. Statistical analysis follows design
Statistical analysis follows from the design of a study
The next question in the tree

24. Statistical analysis follows design
Testing for a difference between a sample and a known population value
Or within-groups designs 1
How many
separate
samples?
Decide if
there is a
difference
Testing for a difference between two samples
Various “t-tests” 2
>2
Testing for a difference between two samples
Various “ANOVA” designs

25. What are samples? Who do we test? Sample
Population
The set of all individuals of interest
Typically we don’t have access to all of the population
Sample
A subset of the population from whom data is collected
We test these folks and then generalize the results to the population as a whole

26. What are samples? condition C condition B condition A “Sample”
condition D
condition A
“Sample”
may also be used to refer to the participants (randomly) assigned to a particular condition of the experiment

27. Statistical analysis follows design
Statistical analysis follows from the design of a study
Next question in the tree

28. Statistical analysis follows design
There is a pre-existing relationship between the groups
“non-independent groups”
“matched samples”
Or the same subjects participate in multiple conditions
“within-groups”
“repeated-measures”
related
Are the samples
related
or independent? …
independent
There is no pre-existing relationship between the groups
“between-groups”

29. Example Dr. Charles investigated the impact of three types of
video taped teaching programs
for two types of subjects (math
and Spanish). 12 participants
were randomly assigned to one
type of teaching program and
one subject. After two weeks
of training Dr. Charles assessed
their learning. What test should
he use to analyze his data (which
program works best for which
subject)?

30. Example Dr. Charles investigated the impact of three types of
video taped teaching programs
for two types of subjects (math
and Spanish). 12 participants
were randomly assigned to one
type of teaching program and
one subject. After two weeks
of training Dr. Charles assessed
their learning. What test should
he use to analyze his data (which
program works best for which
subject)?

31. Example Dr. Charles investigated the impact of three types of
video taped teaching programs
for two types of subjects (math
and Spanish). 12 participants
were randomly assigned to one
type of teaching program and
one subject. After two weeks
of training Dr. Charles assessed
their learning. What test should
he use to analyze his data (which
program works best for which
subject)?

32. Example Dr. Charles investigated the impact of three types of
video taped teaching programs
for two types of subjects (math
and Spanish). 12 participants
were randomly assigned to one
type of teaching program and
one subject. After two weeks
of training Dr. Charles assessed
their learning. What test should
he use to analyze his data (which
program works best for which
subject)?

33. Example Dr. Charles investigated the impact of three types of
video taped teaching programs
for two types of subjects (math
and Spanish). 12 participants
were randomly assigned to one
type of teaching program and
one subject. After two weeks
of training Dr. Charles assessed
their learning. What test should
he use to analyze his data (which
program works best for which
subject)?

34. Using SPSS
The design of a study also has an impact on how you need to set up your SPSS data file

35. Brief review of SPSS Two view windows: Data view
This is where you type in all of the data
To switch
between the
views click
on the tabs

36. Brief review of SPSS Two view windows: Variable view
This is where you specify the details about the variables

37. Variable view Type of variable: numeric, text, monetary, date, etc.
Name of the variable, limited to 8 characters
Type of variable: numeric, text, monetary, date, etc.

38. The Data View Each row corresponds to an experimental unit
(called “cases” in SPSS lingo)
Each column corresponds to a variable
So each column in the data view corresponds to a row in the
variable view

39. In-class lab
With the remaining time, go ahead and work through the lab
A few study descriptions, using the decision tree try to determine the appropriate statistical test
Download the “majors.sav” datafile and open it up in SPSS.