Selecting a Research Design

1. Selecting a Research Design

2. Research Design • Refers to the outline, plan, or strategy specifying the procedure to be used in answering research questions • Determines the when (a procedural sequence) but not the how of:

3. 1. Sampling Techniques and representativeness of data sources 2. Data Collection • Time frame of measurement • Methods of measurement 3. Data Analysis

4. Three Major Approaches to Research Designs 1. Descriptive Approach 2. Experimental Approach 3. Quasi-Experimental Approach

5. 1. Descriptive Approach • Represent or provide accurate characterization of phenomenon under investigation • Provide a “picture” of a phenomenon as it naturally occurs

6. Key Features: • Not designed to provide information on cause-effect relationships, therefore internal validity is not a concern • Because focus is on providing information regarding some population or phenomena, external validity is major concern

7. Variations: • Exploratory • Goal: to generate ideas in field of inquiry that is relatively unknown • Least structured to gather more descriptive information • Frequently used as first in series of studies on a specific topic

8. Process evaluation • Goal: to identify the extent to which a program (or policy) has been implemented, how that has occurred, and what barriers have emerged • Program as implemented vs. program as intended • Designed to guide development of new program (normative), summarize the structure of program prior to studying its effects, or assess feasance of pre-existing program

9. Strengths (Descriptive): • Generally lower costs (depend upon sample size, number of data sources, and complexity of data collection methods) • Relative ease of implementation • Ability to yield results in relatively short amount of time • Data analysis straight-forward • Results easy to communicate to non-technical population

10. Limitations (Descriptive): • Cannot answer questions of causal nature

11. 2. Experimental Approach • Primary purpose is to empirically test the existence of causal relationship among two or more variables • Systematic variation of independent variable (IV) and measure its effects on dependent variable (DV)

12. Kerlinger (1973) MAX-MIN-CONApproach • MAXimize systematic variance (exp cond as different as possible) • MINimize error variance (accuracy of assessment) • CONtrol extraneous systematic variance (homogeneity of conditions)

13. Key Features: • Random assignment of individuals or entities to the levels or conditions of the study • Control biases at time of assignment • Ensure only independent variable(s) differs between conditions

14. Key Features (cont): • Emphasis placed on maximizing internal validity by controlling possibly confounding variables • Creation of highly controlled conditions may reduce external validity

15. Variations: 1. Between Group Designs A. Post-only design • Subjects randomly assigned to experimental\control groups • Introduction of IV in experimental condition • Measurement of DV (single or multiple instances)

16. Post-only design Randomized IV DV Group 1 R X O Group 2 R O

17. B. Pre and post design: • Subjects randomly assigned to experimental\control groups • Preliminary measurement of DV before before treatment (check of random assgn) • Introduction of IV in experimental condition • Measurement of DV (single or multiple instances)

18. Pre- and Post- design Randomized DV IV DV Group 1 R O1 X O2 Group 2 R O1 O2

19. C. Multiple Levels of single IV: • Subjects randomly assigned to experimental\control groups • Introduction of multiple levels of IV in experimental condition • Measurement of DV across different conditions

20. Multiple Levels design Randomized IV DV Group 1 R X1 O Group 2 R X2 O Group 3 R X3 O Group 4 R O

21. D. Multiple Experimental and Control Groups (Solomon Four-Group design): • Subjects randomly assigned to experimental\control groups • Preliminary measurement of DV in one exp\control pair • Introduction of IV in both experimental conditions • Measurement of DV (assess effects of pretest)

22. Multiple Levels design Randomized DV IV DV Group 1 R O1 X O2 Group 2 R O1 O2 Group 3 R X O2 Group 4 R O2

23. E. Multiple IVs (Factorial Design): • Subjects randomly assigned to experimental\control groups • Introduction of multiple levels of IVs in experimental condition • Measurement of DV across different conditions (cells)

24. Multiple IVs design Randomized IV IV DV Group 1 R X1 Y1 O Group 2 R X2 Y2 O Group 3 R X1 Y2 O Group 4 R X2 Y1 O

25. 2. Within-Group Designs • Repeated Measures • Each Subject is presented with two or more experimental conditions • Comparisons are made between conditions within the same group of subjects

26. Randomized IV DV IV DV Subject 1 R X1 O1 X2 O2 Subject 2 R X1 O1 X2 O2 Subject 3 R X1 O1 X2 O2 Subject 4 R X1 O1 X2 O2 Within SS design

27. Strengths (Expl): • Experimental control over threats to internal validity • Ability to rule out possible alternative explanations of effects

28. Limitations (Expl): • More resemble controlled study, less resembles usual real-world intervention (decrease external validity) • Experimental Realism = engagement of subject in experimental situation • Mundane Realism = correspondence of experimental situation to ‘real-world’ or common experience

29. Limitations (Expl): • Randomized experiments difficult to implement with integrity(practical or ethical reasons) • Attrition over time = non-equivalent designs

30. 3. Quasi-Experimental Approach • Primary purpose is to empirically test the existence of causal relationship among two or more variables • Employed when random assignment and experimental control over IV is impossible or impractical

31. Key Features: • Other design features are substituted for randomization process • Quasi-experimental comparison base: • Addition of non-equivalent comparison groups • Addition of pre- and post-treatment observations

32. Variations: A. Non-equivalent Comparison Group • Post Only, Pre-Post, Multiple Treatments • No random assignment into exp\con • “Create” comparison groups • Selection criteria or eligibility protocol • Partial out confounding variance • (statistical control)

33. B. Interrupted Time Series • Multiple observations before and after treatment or intervention is introduced • Examine changes in data trends (slope and intercept) • Investigate effects of both onset and offset of interventions

34. 16 14 12 10 8 6 4 2 0 Intervention Interrupted Time Series

35. C. Regression Discontinuity • Separate sample based on some criterion (pre-test) • One group administered treatment, other is control group • Examine trends in both groups; hypotheisze equivalent

36. Regression Discontinuity No Treatment Treatment Post Test Scores Pre-test Cut-off Point

37. Strengths (Quasi): • Approximation of experimental design, thereby allowing causal inference • Garner internal validity through statisticalcontrol, not experimental • Use where experimental designs are impractical or unethical

38. Limitations (Quasi): • Uncertainty about comparison base: Is it biased? • Statistical control based on known factors. If unknown or unmeasureable, threat to validity • Data collection schedule and measures very important