Group Quantitative Designs

1. Group Quantitative Designs • First, let us consider how one chooses a design. There is no easy formula for choice of design. The choice of a design should be based on overall considerations of the study, that is, the theoretical framework, the problem, the hypotheses, the treatments, measures, settings, costs, feasibility, and time, to name a few.

2. Correlational research • Assesses strength of a relationship between two or more variables. • Cannot imply causality • However it can: • Help with prediction of future events • Provide data that is consistent or inconsistent with a particular scientific theory. • Correlational research can’t prove a theory, but it can disprove/negate a theory.

3. Differential Research Methods • Differential research compares 2 or more groups that are differentiated by some preexisting variable. No manipulation – only measurement. • Group differences existed before the study was conducted. • IV: Classification • DV: Behavior measured • Differential design is not the same as experimental: • Differential design groups individuals by pre-existing conditions (e.g., race or gender). • Experimental design groups are determined by random assignment. • Causality cannot be inferred from a differential design

4. Cross-sectional vs. Longitudinal • Cross-sectional • Eg: groups of individuals at different ages are examined on a particular variable • Cohort Effect (e.g. living through Great Depression) • Longitudinal • Follow same people over time to observe developmental changes (controls for cohort effects) • Artifact: an apparent effect of an independent variable that is actually the result of something else – thus a confound.

5. Correlation Analysis • Pearson product-movement correlation • Used if both variables are at least on an interval scale • Spearman rank-order correlation • Used if one variable is ordinal and the other is at least ordinal. • Range is from –1.00 (perfect negative relationship) to +1.00 (perfect positive relationship). Correlation of .00 means no relationship whatsoever.

6. Interpretation • Need to know whether the correlation is significant: • p-values • If the correlation is low (close to zero) then it is likely that you will not have a significant correlation. • Coefficient of determination: • Computed by squaring the correlation • Eg: if r = .50 then r2 = .25. • So a correlation of .50 indicates that 25% of the variability of the first variable can be accounted for (or explained by, predicted by) by knowing the scores on the second variable. • Sample size must be large enough for this to be meaningful.

7. Differential Analyses • Type of statistical test used depends on # of groups and the scale of measurement • If DV is at least interval and there are 2 groups a t-test for independent groups is usually used. • If more than 2 groups…ANOVA is generally used. • If the data are ordinal or nominal: non-parametrics are used (such as Mann-Whitney U test for ordinal and chi-square for nominal)

8. Validity • Validity of procedures & conclusions • “appropriateness or soundness” • Validity problems can occur at any level of research. • Researchers must anticipate these threats to validity AS WELL AS • Create procedures to eliminate or reduce them

9. Types of Validity • Statistical Validity • Accuracy of p-value • Construct Validity • Degree to which the theory or theories behind research provide best explanation for results observed. • External Validity • Generalizability to other people, places or conditions. • Internal Validity • How confident we are that the observed changes in the DV were due to the effects of the IV and not extraneous variables.

10. Confounding Variables • Maturation • History • Testing • Instrumentation • Regression to the Mean • Selection • Attrition • Diffusion of Treatment • Sequence effects

11. Pre-Experimental Designs • One-Shot Case Study (barely research) X O • One Group Pretest-Posttest Design O1 X O2 • Intact (static) Group Comparison X O1 -------------- O2

12. Experimental Designs • Posttest-Only Control Group Design R X O1 R O2 • Pretest-Posttest Control Group Design R O1 X O2 R O3 O4

13. Solomon Four-Group Design This design is used to control for the effects of the pretest on the intervention and postteest.

14. Quasi-Experimental Designs • Nonequivalent Control Group Design O1 X O2 -------------------------- O3 O4 • Separate Sample Pretest-Posttest Designs O1 X O2 -------------------------------- O3 O4 X O5

15. Quasi-Experimental Designs • Interrupted Time-Series Design O1 O2 O3 O4 X O5 O6 O7 O8 • Recurrent Institutional Cycle Design (institutional cohort design) X O1 ------------------------ O2 X O3

16. Ex Post Facto Designs • One-Shot Case Study • One-Group Pretest-Posttest Design • Co-Relational Study O1 O2 • Static Group Design X O --------- O

17. Recap • What is main purpose of Correlational research? • What information can be obtained from correlational research? • Can correlational research determine causality? • How can correlational research help to validate or invalidate a theory?

18. Recap • What is the main purpose of differential research? • What type of independent variable is used in differential research? • What are artifacts? How do the affect differential research? • How is differential research similar to experimental research?