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Non-Experimental Research Designs

Sep 12, 2014

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Non-Experimental Research Designs. Farzin Madjidi, Ed.D. Pepperdine University G.S.E.P. Non-Experimental Designs. Descriptive Relationships Surveys Causal Comparative Qualitative. 2. Research Design looks tough!. But its really easy!. Descriptive Studies.

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causal comparative
cross sectional
relationship studies
variable predictor variable
variable criterion variable based

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Non-Experimental Research Designs Farzin Madjidi, Ed.D. Pepperdine University G.S.E.P.

Non-Experimental Designs • Descriptive • Relationships • Surveys • Causal Comparative • Qualitative 2

Research Design looks tough!

But its really easy!

Descriptive Studies • Intended to examine and describe an issue • Especially effective when the area has been previously studied • Ex: a study of organizational climate • Ex: a study of salaries in an industry • Typical analysis • Descriptive statistics (graphs, charts, tables, etc.) • Textual analyses (content analysis) 3

Descriptive Studies Important Issues: • Cannot make conclusions about relationships studied • Subjects (AUs) and instrumentation MUST be clearly identified • Watch for “Graphic Distortions” 4

Relationship Studies • Investigate the degree to which differences or variation in one variable are related to differences or variation in another variable • They allow us to examine/discover characteristics that can predict other characteristics • Can lead to further investigation of other variables 5

Relationship Studies • Ex: What is the relationship between leadership styles and tenure as a leader? • Ex: Is there a difference in job satisfaction based on Work Locus of Control? • Typical Analysis • Correlation • Bivariate Regression • Inferential Techniques 6

Predictive Studies • Correlation coefficients only show associations • Predictive studies allow you to calculate the value of one variable (Criterion variable) based on the values of another variable (Predictor variable) • They allow you to make estimates • They allow you to devise forecasting models 7

Predictive Studies • Ex: Determining factors that lead to attrition at high level management positions • Ex: Forecasting property values based on a number of property variables • Typical Analysis includes: • Regression • Multiple Regression • Discriminant Analysis 8

Predictive Studies Important Issues: • Cannot infer Cause-and-Effect • Practical Significance vs. Statistical Significance (low, yet significant correlation; r vs. r-squared) • Reliability and Validity of instruments used • Assumptions/Limitations of statistical techniques used 9

Survey Research • A very popular methodology • Although mostly used in descriptive studies, can be used as part of both descriptive and predictive/relationship studies • Two different types: • Cross-Sectional • Longitudinal 10

Survey ResearchCross-Sectional Surveys • Studies a phenomenon as it occurs at one point in time (e.g., political surveys, attitude surveys, etc.) • Studies a phenomenon through subjects that are along different timelines of the phenomenon (e.g., attitudes of managers in different age groups, surveyed at the same time) 11

7-98 Cross-Sectional (Mgrs w/ 3 to 5 yrs of exp) Longitudinal 7-96 7-97 7-98 (mgrs w/ 1 yr of exp) (mgrs w/ 2 yr of exp) (mgrs w/ 3 yr of exp) Survey ResearchLongitudinal Surveys • The same group of subjects are studied over a period of time • Avoids some of the limitations of cross-sectional designs Convenience vs. Resources 12

Causal-Comparative Studies Go beyond relationships/associations to examine cause-and-effects. Two types of these studies: • Ex Post Facto • Correlational 13

Causal-Comparative StudiesEx Post Facto • Applied when seeking cause-and-effect relationships, but cannot do experiments • One or more independent variables are used to study their effects on one dependent variable • Ex: What is the impact of a particular training on job performance 14

Causal-Comparative StudiesEx Post Facto (cont’d) Ex. Continued: • Approach 1: Select two groups, one group is trained, the other is not. Next their job performance is measured (experimental design) • Approach 2: Look for groups with pre-existing conditions and compare their job performance. e.g., select a group that is already trained and one that is not trained and compare their job performance. • The groups selected must be as close as possible except for the independent variable 15

Causal-Comparative StudiesCorrelational These studies use more sophisticated versions of correlation analysis to investigate cause-and-effects • Path Analysis:A causal model is developed from theory which shows by arrows the causal sequence that is expected. Correlation between these variables is used as empirical evidence of the proposed links. • Newer, more sophisticated methods: • Structural Equation Modeling • Latent Variable Causal Modeling 16

Causal-Comparative Studies Important Issues: • Primary purpose should be developing cause-and-effect relationships when experimentation is not possible • The “intervention” must have already occurred • Must identify and consider extraneous variables • Differences between the groups not due to the independent variable should be controlled • Be careful with causal conclusions 17

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Non-Experimental designs: Surveys & Correlational

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