1. Non-Experimental Quantitative Research Designs Chapter 8 Germaine Harley Bart Miller

3. How information is obtained

4. The plan and structure of research to provide a credible answer to a research question

5. Purpose of Non-Experimental Research

6. Describes existing phenomena without changing some condition to affect subjects’ responses.

7. A report of the way things are or the way things were.

10. Describes a phenomenon – in the form of stats, percentages, averages, frequencies, etc.

11. Graphs and Visuals

12. Book Example – Classroom Climate/Student’s Attitudes & Learning

13. Describe what is meant by “classroom climate” – Once established – variables can be related.

14. Consumer Tips

15. Conclusions about relationships should be made with caution

17. WHY ARE RELATIONSHIPS IMPORTANT?

18. Non-Experimental Quantitative Research Designs Q #2: Why are relationships important in nonexperimental designs? Mary: Because relationships describe differences or similarities between variables, they are important to help the researcher identify the variables to study. Also, studying relationships can help researchers to identify causes of, or to predict behaviors or outcomes. Kristen: There are many reasons relationships are important in non-experimental designs. 1. they allow us to make a preliminary identification of possible causes for the outcomes in the research. 2. they help us indentify variables to be studied. 3. they allow us to predict the value of one variable based on other variables. 4. the research is dominated by the term and, therefore, it is important to know what is meant by it. Pan: Relationships allow us to identify causes of educational phenomena and can identify variables for research. And they can help us predict the importance of one variable over another. Relationships are very important in research. Research is to examine all sorts of relationships between different things.

19. Non-Experimental Quantitative Research Designs Comparative Studies Compares two or more groups on a variable. Example: If math scores for males are significantly higher than those for females, a relationship exists between gender and math achievement Consumer Tips Subjects instrumentation, and procedures should be well described Identify the criteria for establishing different groups Do not infer causation from comparative research designs Graphic presentations should not distort the results

20. Non-Experimental Quantitative Research Designs Correlational Studies Two or more variables are related with the use of one or more correlations coefficients. Coefficient expresses the nature of the relationship between the variables. Reliability of instrument is important – without it, it is difficult to obtain a significant correlation coefficient. Lack of variability in scores (e.g., everyone scoring very, very low; everyone scoring very, very high; etc.) makes it difficult to identify relationships. Restriction in range - when the variability of scores is small, the correlation will be low.

21. Non-Experimental Quantitative Research Designs Prediction Studies - Show how one variable can predict another. Predictor Variable – H.S. GPA precedes college GPA Criterion Variable – The predicted dependent variable Multiple Regression Analysis Provides a single index of the predictive power of all the predictor variables together in studies with several predictor variables. Combines several predictor variables Coefficient of Multiple Correlation The correlation of all the independent variables (predictor variables) to the dependent variable (criterion variable) Symbol = R

22. Non-Experimental Quantitative Research Designs Consumer Tips Causation should not be inferred from correlation A relationship between two variables (e.g., achievement and attitude) does not mean one causes the other (i.e., positive attitudes do not cause high levels of achievement. The reported correlation should not be higher or lower than the actual relationship Restriction in range Attenuation – lowering of correlation because of unreliable measures. Practical significance should not be confused with statistical significance (Coefficient of Determination) – the square of the correlation coefficient The size of the correlation should be sufficient for the use of the results. Prediction studies should report accuracy of prediction for new subjects. Procedures for collecting data should be clearly indicated.

25. Longitudinal Survey

26. Internet-Based Surveys

28. Non-Experimental Quantitative Research Designs Q #1: Describe the difference between cross-sectional and longitudinal surveys. Explain when it would be appropriate/best to use each. Pan: Cross-sectional surveys are when you collect information from sample populations at one time. Longitudinal surveys are when similar subjects are interviewed over long periods of time.It is best to use longitudinal surveys when you are researching trends over a long period of time.Cross sectional surveys are convenient and provide immediate information.

29. JEOPARDY!