Research Approaches

1. Research Approaches • Internal Validity • External Validity “A study that is fetchingly realistic might bring us no closer to the truth than one that seems painfully contrived” (Myers & Hansen, 2006, p. 63).

2. Dimensions of Research • Antecedent Manipulation • Treatments • Independent variable (IV) • Imposition of Units • Behavioral measures • Dependent variable (DV)

3. Dimensions of Research High/ Low High/ Medium High/ High High Imposition of Units Medium/ Low Medium/ Medium Medium/ High Medium Low/ Low Low/ Medium Low/ High Low Low Medium High Antecedent Manipulation

4. True Experiments High/ High High Imposition of Units Medium Low Low Medium High Antecedent Manipulation

5. Nonexperimental Approaches • Phenomenology – attending to and describing one’s own experience • Case studies – outside observer records an individual’s experiences & behaviors • Field studies – research method conducted in the field using a variety of techniques • Archival studies – reexamine existing data for a new reason • Qualitative studies – data are verbal descriptions rather than numbers

6. Nonexperimental Approaches • Phenomenology - Description of one’s own immediate experience Examples: pain in my C5 vertebrae the Purkinje effect

7. Phenomenology High Imposition of Units Medium Low/ Low Low Low Medium High Antecedent Manipulation

8. Nonexperimental Approaches • Case studies - Descriptive records of another individual’s experiences or behavior. Evaluative case studies– case compared to hypothetical “normal” psychological diagnosis – DSM-IV Deviant case analysis– deviant case compared to “normal” for significant differences. e.g. Mednick, 1969 – ANS of schizophrenic children functions different compared to normal controls.

9. Case studies High/ Low High Imposition of Units Medium/ Low Medium Low/ Low Low Low Medium High Antecedent Manipulation

10. Nonexperimental Approaches • Field studies - Studies done in situ, in real-life settings as opposed to the laboratory. e.g. A field Experiment in Chicago (p. 86).

11. Field studies High/ Low High Imposition of Units Medium/ Low Medium Low/ Low Low Low Medium High Antecedent Manipulation

12. Nonexperimental Approaches • Naturalistic observation - a technique of observing behaviors as they occur spontaneously in the natural setting. e.g. dominance hierarchies in social groups.

13. Naturalistic Observation High Imposition of Units Medium Low/ Low Low Low Medium High Antecedent Manipulation

14. Nonexperimental Approaches • Systematic observation - a technique of using specific rules in a pre-arranged way to objectively record observations. Female sexual receptivity (rodents only) Lordosis- 1. darting, 2. ear wiggling 3. inverted back and 4. tail diversion

15. Nonexperimental Approaches • Participant-observer studies - the researcher becomes part of the group being studied. Undercover roid guy… just what baseball needed!

16. Nonexperimental Approaches • Archival study - already existing records are reexamined for a new purpose. E.g. data on crime, death rates, education levels, salaries housing patterns and disease rates are accessible to researchers. Bioinformatics Gene database

17. Nonexperimental Approaches Self-reports personal narratives expression of ideas, memories, feelings and thoughts • Qualitative research relies on words rather than numbers Is there a paradigm shift occurring?

18. Phenomenology is used as part of qualitative research Contemporary or Empirical Phenomenology • Researcher self-reflects on experiences related to the phenomenon • Others provide verbal or written descriptions of experiences • Accounts of the phenomenon are gathered from literature, art, television, the internet and other sources

19. Correlational and Quasi-Experimental Designs Chapter 5

20. Correlational Designs Determine the degree of relationship between two traits, behaviors or events; predict one set from another. • Antecedents are preexisting • Degree of imposition of units - high • Tend to be higher in external validity

21. Correlational Designs Low/ High High Imposition of Units Medium Low Low Medium High Antecedent Manipulation

22. Quasi-experimental Designs Can seem like an experiment, but subjects are not randomly assigned to treatment conditions. • Antecedent control varies • Degree of imposition of units - high • Tend to be higher in external validity

23. Quasiexperimental Designs Low/ High meduim/ High High Imposition of Units Medium Low Low Medium High Antecedent Manipulation

24. Example of a Quasiexperiment Lighting condition – fluorescent vs incandescent. Subjects – from company A (fluorescent lights) or B (incandescent). Performance measure – productivity. Can cause-effect be established with confidence?

25. Pearson Product-Moment Correlation Coefficient (r ) Most common procedure for calculating simple correlations – relationship between pairs of scores for each subject. Three outcomes are possible: • Positive relationship • Negative relationship • No relationship

26. Scatterplots Visual representations of the scores belonging to each subject in a study. Each dot = two scores (x,y) from one subject. • One score places the dot along the horizontal axis (x) and the other score places it along the vertical (y) axis. • Regression lines (of best fit) represent the mathematical equation that best represents the relationship between the two measured scores.

27. Hypothetical Relationships A. B. Positive r = +.69 Negative r = -.72 Variable Y Variable Y Variable X Variable X C. No correlation r = -.02 Variable Y Variable X

28. Four possible causal directions of a correlation • Given a strong positive relationship between childhood aggressiveness and watching violent TV (r = +.70). • Watching violent TV  aggressiveness • Aggressiveness  watching violent TV • Aggressiveness  watching violent TV • Both are caused by a third variable (unknown or not measured, e.g., parental supervision)

29. Coefficient of determination • Estimates the amount of variability in scores on one variable that can be explained by the other variable. • E.g., if r = .56, then r 2 = .31. • 31% of the variability in scores on variable X can be accounted for by variable Y. • An r 2 ≥ .25 can be considered a strong association.

30. Regression equation Positive r = +.56 Y slope Variable Y: calculate mean and S Y intercept X Variable X: calculate mean and S

31. Y = Y + r [Sy / Sx] (X – X) Regression Equation • Given the score on one variable you can predict the score on the other if you know: • The value of r • Average scores of X and Y (the means) • Standard deviation (S) of X and Y

32. Multiple Regression • Used to predict the score on one behavior from the scores on others included in the analysis. • The regression equation provides beta weights for each predictor (indicating their importance) • Beta weights can simply be reported or used in an advanced correlational analysis to construct causal sequences for the behaviors.

33. Multiple Correlation • Intercorrelations among 3 or more behaviors (R) • Can not explain why the 3 measures are related but it may suggest that a “third variable” is important. • Influence of one variable is held constant while measuring the correlation between the other two – partial correlation

34. Causal Modeling • Advanced correlational techniques • provide information about the direction of the cause and effect sequences among variables. Two techniques: • Path analysis • Cross-lagged panel designs

35. Path Analysis • Creates models of possible causal sequences when several related behaviors are measured • Beta weights from multiple regression analysis are used to evaluate the direction of cause and effect from correlated variables. • Internal validity is low (correlational data), consequently causal statements can not be made.

36. Path Analysis Perceived Risk .25** .20* .30** Monitoring Intrusive Thoughts .37** Psychological Distress Internal validity? Third variables? * p < .05, ** p < .01 From Schwartz, Lerman, Miller, Daly, and Masny (1995)

37. Cross-Lagged Panel Design • Uses relationships measured over time to suggest causal models. • The same pair of related behaviors or characteristics are measured at two separate time points for each subject. • Can only suggest the direction of causal relationships (not conclusive). • Bidirectional causation and the third variable problem cannot be ruled out.

38. Age 3 Age 8 r = .14 Time watching TV Time watching TV r = .05 r = .20 r = .07 r = -.59 Size of Vocabulary Size of Vocabulary r = .41 Cross-Lagged Panel Design Hypothetical Cross-Lagged Panel design

39. Quasiexperimental Designs • Subjects cannot be randomly assigned to different treatments • Quasi-treatments are formed based on a particular event, characteristic or behavior of interest. • E.g., gender differences in sleep patterns. • Low internal validity.

40. Quasiexperimental Designs • Subjects may be exposed to different treatments, but without random assignment (e.g. the lighting-productivity study) • There is a lack of control over other potential confounds (i.e., an inability to hold all else constant except for the treatment condition).

41. Ex Post Facto Studies • Ex Post Facto – systematic examination of the effects of subject variables (characteristics) without manipulation. • Low Antecedent Manipulation • High Imposition of Units • Greater external validity

42. Nonequivalent Groups • A manipulation is carried out but subjects are not randomly assigned to groups • E.g. the lighting experiment yet again • Internal validity can be increased by controlling extraneous variables after careful consideration of potential confounds.

43. Longitudinal Designs • Measure the behavior of the same group of subjects across time. • A form of within-subject design • Important for studying growth and development and aging • Retaining subjects may be difficult

44. Cross-sectional Studies • Investigates changes across time by comparing groups of subjects already at different stages at a single point in time. • Typically requires more subjects than the longitudinal study. • Subjects may differ in ways other than those being studied (similar to Ex post facto).

45. Pretest/Posttest Design • Investigates the effects of a treatment by comparing behavior before and after the treatment. • Practice effects (pretest sensitization) • Outside influences cannot be ruled out • Low internal validity e.g., exposure to cocoa on cognitive performance