1. Research Approaches and Methods of Data Collection
Chapter 2
Research Approaches and Methods of Data Collection
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2. 1) Descriptive: No Hypothesis
Types of Research
1) Descriptive: No Hypothesis
2) Relational Studies: (Circumstantial Evidence)
3) Experiments - evidence of cause and effect.
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3. a difference between conditions is produced by researcher
True Experiments
objective measures
a difference between conditions is produced by researcher
all other variables held constant
can make conclusions about cause and
effect
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4. Independent Variable (IV): a variable that is presumed to cause changes to occur in another variable.
In a true experiment we manipulate the IV to determine its effect on a Dependent Variable (DV).
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5. Confound Variables: anything other than the manipulated variable that is different between two conditions.
Serves as an alternative explanation of the cause of differences between conditions.
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6. Mediating and Moderating Variables
A Mediating Variable occurs between two other variables in a causal chain (e.g., anxiety causes distraction (mediating variable) which affects memory).
Moderating Variables qualify a causal relationship as dependent on another variable - e.g., the impact of anxiety on memory depends on level of fatigue (moderating variable).

7. Criteria for Identifying a Causal Relation
cause (IV) must be related to the effect (DV) (relationship condition)
changes in IV must precede changes in DV (temporal order condition)
no other explanation must exist for the effect
(eliminate confounds)

8. Definition of an Experiment:
objective observation
of phenomenon that are made to occur
in a strictly controlled situation in which one or more factors are varied and all others are kept constant.

9. Limitations of the Experimental Approach
Can not test the effects of non-manipulated variables (many potential independent variables cannot be directly manipulated)
Artificiality - refers to potential problems in generalizing findings from laboratory settings to the “real world”.
Inadequate method of scientific inquiry

10. Experimental Research Settings
Field experiments
Advantage: may be easier to generalize findings.
Disadvantage: less control of extraneous variables
Laboratory experiments
Advantage: more control than field experiments;
Disadvantage: possibly more artificiality

11. Example: Eyewitness Testimony
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12. Quantitative Research
Nonexperimental
Quantitative Research
Types
correlational study
cross-sectional and longitudinal studies
natural manipulation research

13. Two (or more) variables measured on same person.
Relation Studies
Two (or more) variables measured on same person.
Measures how well we can predict one
variable if we know the value of the other variable.
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14. The magnitude of the correlation defines the degree of relationship.
Pearson Correlation Statistic ( r ) measures the degree and the direction of the relationship.
Ranges from -1 to +1
The magnitude of the correlation defines the degree of relationship.
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15. r = 1.0 - one variable is perfectly predicted from the other variable.
r = no relationship.
r = one variable is perfectly predicted
from the other variable.
The closer to +/- 1 the stronger the relationship. The closer to 0 the weaker the relationship.
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16. The sign (- or +) indicates the direction of the relationship.
Positive - change in same direction.
High on one variable predicts high on the other.
Negative - change in opposite directions
Low on one variable predicts high on the other.
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17. Correlations Do young hockey players take more penalties than
old hockey players?
r - statistical relationship
twixt 2 variables (age &
penalties)
Penalty Minutes
Age *
(-) r

18. * * * * * * * * * * * * * Scatter Diagrams Data point.
- point on diagram representing the
co-occurrence of
the two variables for
each subject.
Variable Y
* * *
* * * *
* * * *
* *
Variable X
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19. * * * * * * * * * * * Line of Best Fit Straight line that
fits the data so the summed distance between the line and each data point
is lower than for
any other possible
line.
* *
* * * *
* * * * *
Variable Y
Variable X
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20. Line of best fit applet
Guessing Game
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21. Predict the correlation between these variables
Concept Check
Predict the correlation between these variables
(High, Medium or Low? Negative or Positive)
Weight and Height
IQ and shoe size
SAT scores and Grades in College
Miles you have drive since a fill-up and amount of gas
in your tank.
Number of Storks and Birthrate in a town
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22. Correlation  Causation Directional Problem A could cause B
B could cause A
Third Variable ( C) Problem
C could be causing both A and B
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23. Warning: People often try to use correlations as evidence of cause.
This is bad science.
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24. Correlational Studies Two variables measured on same person.
Relationship between 2 variables measured.
Results indicate level and direction of prediction – does not indicate cause.
Why do Correlational Research?
- preliminary studies (e.g., epidemiology)
ethical concerns (e.g., smoking and cancer)
non-manipulatable variables (e.g.,sex)
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25. Path Analysis
Correlational method of testing relationships among variables by seeing how well they fit some theoretical model
Direct effects – when a variable directly impacts another
Indirect effects –effect occurs through mediating variable

26. Hypothesized causal ordering for how Socio-economic status (SES), Intelligence (IQ) and Achievement Motivation (AM) Are related to GPA
SES AM
GPA IQ
SES
GPA AM IQ

27. Natural Manipulation Research
- looks like experiments, but they are confounded.
Variable is not manipulated by experimenter.
e.g., comparisons of pre-existing or self-selecting groups.
Males vs. Females
Arts vs. Science Students
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28. If Males score better on Math tests than Females can I say being Male causes better math scores?
Other explanations?
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29. EI (2)

30. Developmental Change Studies
Longitudinal Studies - Measure Same Subjects repeatedly at different Ages.
Cross-Sectional Studies - measures different subjects at different ages.
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31. Age-Cohort - group of people of the same
age, who share similar cultural and historic
experiences.
Cross-sectional - confounds Age and
Age Cohort effects.
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32. Longitudinal - follow one Age cohort. - no Age X Cohort Confounds
But …
Costly, time consuming and do not
necessarily generalize to other cohorts.
Cohort-Sequential Design
- incorporate both Longitudinal and
Cross Sectional.
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33. Reading Readiness Scores in 1970 and 1985
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35. Qualitative Research
Interpretive
Multimethod (triangulation)
- interviews, introspective analysis,
personal experience, observations, photographs ect. . .
Conducted in a natural setting (field)

36. Methods of Data Collection
Tests
Questionaires
Interviews
Focus Groups

37. Descriptive Quantitative Studies Concerns - defining variables
- sampling (who, where, and when)
- Experimenter Bias
- Reactive effect Observation can change behavior
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38. Naturalistic Observation Studies e.g., Ethnology
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39. Complete Participant Observer Studies
e.g., Schouten & McAlexander, 1995

40. E.g., Gloria Steinem
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41. Steve Garasky and Brenda Lohman, pictured here in the observation room of Iowa State University’s Child Development Lab School, are authors of a new study finding increased levels of stress in adolescents are associated with a greater likelihood of them being overweight or obese. (Credit: Bob Elbert/ISU News Service)
Marshmellow Study

42. Sampling Who, When and Where. Time Interval sampling
Event Interval Sampling

43. Case Studies Detailed description of one person’s behavior
Useful for Rare Cases
e.g., Serial Killers, Rare Disorders
Concerns
1) Experimenter Bias
2) Subject Bias (Reactivity)
3) Generalizability
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44. Profiling

45. Existing or Secondary Sources
documents
archived data bases
physical data