Course Description
Experimental design and the analysis of data from experiments, including orthogonal analysis of variance for single and multifactor designs, randomized blocks, repeated measures, and mixed models.
Athena Title
App Analysis of Vari Mthds Edu
Equivalent Courses
Not open to students with credit in ERSH 8310
Non-Traditional Format
This course will be taught 95% or more online.
Prerequisite
ERSH 4300/6300 or ERSH 4300E/6300E
Grading System
A - F (Traditional)
Course Objectives
UNIT I 1. Given a small data set and summary calculations, determine the standard error of the statistic of interest (sample mean, the difference between means). 2. Define and interpret the meaning of a Type I error, Type II error, and statistical power. 3. Describe the interrelationship between Type I errors, Type II errors, and statistical power. 4. Given a research context and summary data, identify the appropriate critical value for the test statistic for a specific hypothesis and confidence interval. 5. Given a computed t-statistic, estimate the p-value. UNIT II 1. Given summary calculations (i.e., SS, MS F), complete an ANOVA summary table and interpret the results in terms of the hypothesis tested. 2. Given descriptive statistics (i.e., sample means, standard deviations, variances), compute the ANOVA summary table and F-ratio. 3. Given the results reported in an ANOVA summary table or descriptive statistics, identify specific differences using the Bonferroni procedure. 4. Develop confidence intervals for specific contrasts of interest. 5. Describe the effect on the Type I error rate when data assumptions are violated. 6. Given a research problem, identify appropriate procedures for increasing statistical power and reducing Type II errors. 7. Calculate statistical power for a specific research study. 8. Determine the necessary sample size needed to answer a specific research question with reasonable statistical power. 9. Given a description of a research study and a computer printout or facsimile, interpret the results of the analysis and draw appropriate conclusions. UNIT III 1. Given a research problem and summary calculations for a study involving two independent variables, complete the analysis and interpret the results. 2. Given the results of a research study involving two independent variables, provide an appropriate interpretation of the results. 3. Given summary calculations, test hypotheses for specific contrasts and provide interval estimates for differences between specific populations. 4. Estimate the minimum sample size needed to test the main effects or interaction for a factorial ANOVA. 5. Given a specific research context, estimate the statistical power for detecting main effects in a factorial ANOVA. 6. Given a description of a research study and a computer printout or facsimile, interpret the results of the analysis and draw appropriate conclusions. 7. Interpret the results for an interaction effect as well as suggest appropriate follow-up analyses. 8. Illustrate the meaning of an interaction, simple effect, and main effect by numerical examples or graphs. 9. Given a description of a research study and a computer printout or facsimile, interpret the results of the analysis and draw appropriate conclusions. UNIT IV 1. Given a description of a research problem involving a continuous independent variable and the regression equation, interpret the following: intercept, slope, R2. 2. Given the results of an ANOVA summary for a regression analysis, interpret the findings in terms of the hypothesis of interest. 3. Given a description of a research study and the data analysis results for a simple linear regression/correlation as reported on a computer output or facsimile, interpret the results completely and draw conclusions supported by the data. 4. Given descriptive statistics including means, standard deviations, and correlation, compute the regression equation and ANOVA summary table. Unit V 1. Given a description of a research study and the data analysis results as reported on a computer output or facsimile, interpret the results completely and draw conclusions supported by the data. Data analysis strategies that might be reported include ANOVA for gain scores and analysis of covariance. 2. Given the results of the analysis of covariance, correctly interpret the meaning and significance for the following tests: equality of regression slopes, the relationship between the covariance and dependent variable, and treatment effect including confidence intervals. Unit VI 1. Given descriptive data, complete the ANOVA summary table. 2. Describe the effect of violating the assumption of sphericity. 3. Determine the necessary sample size for a repeated measures design. 4. Determine appropriate tests for specific contrasts of interest to a researcher. 5. Given a computer printout or facsimile, interpret the results of the analysis. Unit VII 1. Given a description of a research study and summary calculations for a mixed model design, complete an ANOVA summary table. 2. Given the research question, determine which F-ratio is relevant for the question and test specific hypotheses of interest through contrast analyses. 3. Given a computer printout or facsimile, interpret the results of the analysis.
Topical Outline
Unit I. Review Statistical Hypothesis Testing A. Comparing two populations a. t-test statistic b. errors in inference c. statistical power d. confidence interval e. assumptions Unit II. Analysis of Variance A. Single factor a. ANOVA summary b. identifying specific differences c. assumptions d. statistical sample size/power e. structural model Unit III. Analysis of Variance A. Two factors a. structural model b. sample size/power c. ANOVA summary table d. assumptions e. effect size f. contrasts g. interaction h. simple effects i. identifying specific differences Unit IV. Simple Linear Regression A. Quantitative independent variable a. correlation b. linear functions c. ANOVA summary table Unit V. Analysis of Covariance A. Overview B. Structural model C. ANCOVA summary table a. equality of regression slopes b. relationship between the covariate and dependent measure c. treatment effects d. identifying specific differences D. Compared with gain scores Unit VI. Repeated Measures Design A. related samples t-test B. ANOVA summary table C. Assumptions D. Sample size/power E. Identifying specific differences Unit. VII. Mixed Model Design A. ANOVA summary table B. Assumptions C. Identifying specific differences
Syllabus