1. EDIT 6900: Research Methods in Instructional Technology UGA, Instructional Technology Spring, 2008 If you can hear audio, click If you cannot hear audio, click If you have a question, click Lloyd Rieber Instructor Eunjung Oh Graduate Assistant

2. Two Topics for Today Continue Introduction to Quantitative Research Methods Overview of a class activity on how to compute a t statistic to determine if two means (pretest and posttest) are significantly different.

3. Not This Week

4. Informal Activity SDC Systematic Data Collection An informal, (hopefully) enjoyable activity designed to give you first-hand experience collecting research data Your Task: Go and research something of interest to you! Report on it informally in writing Give 5 minute oral report 10%, Due: April 9

5. March 26Quantitative Research (con’t) April 2Quantitative Research April 9Preparing a Research Report SDC Reports (in class) April 16Finish SDC Reports (if needed) Research Project Presentations? April 23Research Project Presentations Remaining Course Calendar

6. Notes About the Next RDA

8. Final Project Rubric Look for Email with this.

9. Dr. Lloyd Rieber The University of Georgia Department of Educational Psychology & Instructional Technology Athens, Georgia USA EDIT 6900 Research in Instructional Technology Part IV. Quantitative Research Methodologies Chapters 9-11

10. Running an Olympic Marathon: No Significant Difference? 26 miles, 385 yards Times of top 2 runners at 2004 Olympics in Athens, Greece: –1. Stefano Baldini ITA 2:10:55 –2. Meb Keflezighi USA 2:11:29 Is a difference of 34 seconds statistically significant?

11. Total votes cast for Bush or Gore in 2000: No Significant Difference?

12. Experimental Designs  Experimental design is used to identify cause-and-effect relationships.  The researcher considers many possible factors that might cause or influence a particular condition/phenomenon.  The researcher controls for all influential factors except those having possible effects.

13. Independent and Dependent Variables  Variable: any quality or characteristic in a research investigation that has two or more possible values.  Independent variable: a possible cause of something else (one that is manipulated)  Dependent variable: a variable that is potentially influenced by the independent variable.

14. The Importance of Control  Control the confounding variables  Keep some things constant.  Include a control group.  Randomly assign people to groups.  Assess equivalence before the treatment with one ore more pretests.  Expose participants to both or all experimental conditions.  Statistically control for confounding variables.

15. Types of Experimental Designs (1)  Pre-experimental designs  True experimental designs  Quasi-experimental designs

16. Overview of Experimental Designs (2) GroupTime Group 1 Group 2 Tx: indicates that a treatment (reflecting independent variable) is presented. Obs: Indicates that an observation (reflecting the dependent variable) is made. : Indicates that nothing occurs during a particular time period. Exp: Indicates a previous experience ( an independent variable) that some participants have had and others have not; the experience has not been one that the researcher could control.

17. Pre-Experimental Designs  Design 1: One-shot experimental case study GroupTime Group1TxObs  Design 2: One-group pretest-posttest design GroupTime Group1ObsTxObs

18. GroupTime Random assignment Group1ObsTxObs Group2Obs True Experimental Designs (1)  Design 4: Pretest-posttest control group design  Design 5: Solomon focus-group design GroupTime Random assignment Group1ObsTxObs Group2Obs Group3TxObs Group4Obs

19. GroupTime Random assignment Group1TxObs Group2Obs True Experimental Designs (2)  Design 6: Posttest-only control group design

20. Quasi-Experimental Designs GroupTime Group1ObsTxObs Group2Obs  Design 8: Nonrandomized control group pretest-posttest design

21. Factorial Designs  Design 15: Randomized two-factor design GroupTime Treatment related to the two variables may occur simultaneously or sequentially Treatment related to Variable 2 Group1Tx 1 Tx 2 Obs Group2Tx 1 Obs Group3Tx 2 Obs Group4Obs

22. Inferential Statistics (1)  Estimating population parameters(1)  Inferential statistics can show how closely the sample statistics approximate parameters of the overall population.  The sample is randomly chosen and representative of the total population.  The means we might obtain from an infinite number of samples form a normal distribution.  The mean of the distribution of the sample means is equal to the mean of the population from which the sample shave been drawn.  The standard deviation of the distribution of sample means is directly related to the standard deviation of the characteristic in question for the overall population.

23. Inferential Statistics (2)  Testing Hypotheses (1)  Research hypothesis vs. statistical hypothesis  Statistical hypothesis testing: comparing the distribution of data collected by a researcher with an ideal, or hypothetical distribution - significance level/alpha (α): e.g.,.05,.01 - statistically significant - reject the null hypothesis

24. Inferential Statistics (3)  Testing Hypotheses (2)  Making errors in hypothesis testing - Type 1 error: alpha error - Type 2 error: beta error

25. Inferential Statistics (4)  Testing Hypotheses (3)  Making errors in hypothesis testing -Increase the power of a statistical test 1) Use as large a sample size as is reasonably possible 2) Maximize the validity and reliability of your measures. 3) Use parametric rather than non parametric statistics whenever possible. - Whenever we test more than one statistical hypothesis, we increase the probability of making at least one Type 1 error.

26. Inferential Statistics (5)  Examples of inferential statistical procedures Parametric statisticsNonparametric statistics Students’ t testSign test Analysis of variance (ANOVA) Mann-Whitney U RegressionKruskal-Wallis U Factor analysisWilcoxon matched-pair signed rank test Structural equation modeling (SEM) Chi-square goodness- of-fit test Odds ratio Fisher’s exact test

27. Inferential Statistics (6)  Example of reporting a test of a statistical hypothesis: Percentage means and standard deviations are contained in Table 1. A significant main effect was found on the test of learning outcomes, F(1, 97) = 9.88, p <.05, MS error = 190.51. Participants given the educational game scored significantly higher (mean =91.5%) than participants who were not given the game (mean=71.2%).

28. Your Task (This has already been emailed to you.) 1.Finish watching my pre-recorded presentation introducing quantitative research methods first. 2.Launch your Excel from last week. “Save as” with a new title. 3.Compute a t statistic from the data set emailed to you. Follow my video tutorial. 4.Email your spreadsheet to me as an attachment. (You do not have to finish this evening, but I expect most will.)

29. This is meant as a class activity. It is not a graded activity. If you get stuck and become totally frustrated, stop and send me what you have.

30. To do list Follow the Course Learning Plan!