1. EDCI 696 Dr. D. Brown Presented by: Kim Bassa

2. Targeted Topics Analysis of dependent variables and different types of data Selecting the appropriate statistic for experimental design and data Interpreting experimental analysis Displaying experimental design data Discussing experimental results

3. The “Best” Fit It is important to choose the best design to… Fit your research question Address validity issues Select ways to measure your dependent variable

4. Experimental Research Experiments that have treatments but do not use random assignment to make comparisons are called quasi-experiments. Experimental research and Quasi-experimental are alike in that they attempt to determine if an independent variable had a direct impact on a dependent variable. Throughout the text they are both referred to as experimental research

5. Experimental Data Data from experimental research are quantitative or qualitative. You will collect numerical data on your variables in order to conduct your analysis. You will use inferential statistics to make comparisons between conditions in an experimental intervention or across/between experimental groups. Types of Data: Ordinal Nominal Interval Ratio (Scale)

6. Statistic Selection Statistical Tests rely on certain assumptions in order to provide accurate information. When assumptions for a statistical test are violated (or not tested), the results of the analyses will be invalid. In each experiment prior to the analysis, the researcher will set the level of significance, which is the probability.

7. Statistic Selection Level of Significance is represented by the small letter… p Level of Significance is also represented by the Greek letter for… alpha 5% probability statistical differences in an analysis would be due to chance or measurement error is represented by… p<.05 Level of Significance (probability) P<.05 means there is 95% statistical probability that differences are due to the intervention.

8. Statistic Selection Selecting or matching the right statistic depends on the type of data you and whether you want to compare mean scores for the dependent variable across groups or conditions.

9. Statistic Selection Putting It All Together (Table 9.2) Can students find main idea after learning a new reading strategy? Design: Post –only Data: correct/incorrect or percentage correct (nominal or interval) Statistic: descriptives Do Students in Algebra I classes who engage in the XYZ curriculum perform significantly different on the state tests than students who do not? Design: Comparison group Data: correct/incorrect or percentage correct (nominal or interval) Statistic: chi-square or ANOVA

10. Statistic Selection To compare pre/post nominal data Use chi- square To compare percentage correct interval data Use ANOVA The t test only test means between 2 groups The ANOVA tests scores for multiple groups at a time.

11. Statistic Selection Two-way ANOVA: two independent variables with one rating to analyze ANCOVA: another variable accounts for difference in or covary with the dependent variable MANCOVA: several ratings to analyze as separate dependent variables or any suspected covariance Main Effects: yielded results after analyzing multiple variables, independent or dependent Interaction Effects: the interaction of the effects of two or more independent variables on a dependent variable Post hoc: follow ups to the original statistical test (i.e. Bonforonni or Tukey)

12. Interpretation of Results Data Entry Software: Statistical Package for the Social Sciences (SPSS) or Statistical Analysis Software (SAS) Use a new computer file Enter data collection accurately Know how to use software menus and commands Understanding Output Know the statistic for the test that you run (i.e. F statistic for ANOVA ) Know the level of significance (i.e. p<.05) Know the degrees of freedom (df)- approximately equal the number of participants for your data and used in the statistical calculation of the level of significance

13. Interpretation of Results Reporting Experimental Results The results section is where statistical out- comes are reported and only includes factual information from the data-base outcomes. The effect size (degree of difference between groups or conditions) is also reported in the results section.

14. Interpretation of Results Data displays provide the advantage of visually analyzing data. frequency tables and histograms are two useful displays and are commonly use to report data. A frequency table is used to display nominal or categorical data. A histogram display the relationship between two variables whose measures yield continuous scores.

15. Discussing Experimental Results The researcher has the opportunity to interpret the results in the discussion section. This section should also include limitations, implications for practice, and future research needs. Limitations Shortcomings Implications for Practice How results can be used in the classroom or other use Future Research Needs New/Improved ideas for research or an extension to the current research

16. Statistical Conclusion Validity Statistical Conclusion Validity: is based on reliable implementation of independent variable as well as appropriate and correctly used measures and statistics, in order draw conclusions regarding the effect of the independent variable on the dependent variable. Fidelity of Treatment Implementation: is based on the treatment being implemented reliably enough to know it was the cause of effects

17. Statistical Conclusion Validity A small sample may contribute to a low statistical power. Low Statistical power means that it is less likely that the statistical test could find statistical difference Type I and Type II Error

18. Summary Select the appropriate statistic Enter the data Interpret the results Enhance your analysis through data displays Remember that inferential analyses of experimental research lead to statistical levels of significance, not necessarily practical levels of significance (McMillian, 2004).