What are the indicators for using a regression analysis? Create a research scenario in which it would be correct to use a regression analysis, including the research question, and dependent and independent variables.© BrainMass Inc. brainmass.com October 25, 2018, 9:57 am ad1c9bdddf
ANOVA compares the difference in means between multiple groups.
Dependent variable: Must be interval or ratio
Independent variable: Categorical (this is what gives us groups to compare)
Example: Does BMI score differ between youth from low-, middle-, and high-income households?
Dependent variable: BMI score
Independent variable: Level of household income
Regression, on the other hand, does not compare groups and is not trying to determine if group A is different from group B. Instead, regression looks at the participants all together as one group, examining how the their characteristics predict the dependent variable. For example, if a participant is female and older, they are more likely to have a higher BMI than a younger male. In this case, the regression analysis is looking at ...
This answer describes the differences between regression and ANOVA, with examples, and discusses the basic features of regression.
Logistic Multiple Regression Analysis Using SPSS
Thank you for your hard work on this - here is a note from a friend who is working on the same issue - he is referring to the SPSS input:
The correct analysis was to run a hierarchical logistic regression entering perceive, safety and gender in the first block and previous, selfcon and sexexp in a second. I used forced entry on both blocks, but you could choose to run a forward stepwise method on block 2 (either strategy is justified). For the variable previous I used an indicator contrast with 'No condom' as the base category.
While he wouldn't help me further, he did note the following: Therefore, there is a 91% chance that she will use a condom on her next encounter. Hope this helps.
Statistics Application Evaluation Criteria
Use this rubric for the following Applications: Factorial ANOVA (Week 3), ANOVA with Repeated Measures (Week 4), ANCOVA (Week 5), Multiple Regression (Week 6), Linear Multiple Regression (Week 7), Odds Ratio (Week 8), Logistic Regression (Week 9), Cronbach's Alpha (Week 11).
During several weeks of the term, you will be required to run statistics problems using SPSS. The website of the primary textbook author provides answers to most questions to help guide you as you work. However, you will be required to document your work and submit it as a package to your instructor. Please use this document as guidance for submitting your PASW weekly applications. Note that each assignment is worth 50 points; point assignments are indicated next to each indicator.
Create a master MS Word document (you should only be submitting ONE document each week). Label it in accordance with the convention described in your syllabus. Inside of this document, include the following information.
1. (5 pts) State the underlying assumptions for the statistical test.
2. (5 pts) State whether the assumptions have been met. If the assumptions were not met (either in actuality or hypothetically), state what alternatives you have available to you.
3. (5 pts) State the null and alternative (research) hypotheses.
4. (5 pts) Copy your syntax file and paste it into your MS Word Document.
5. (5 pts) For your output file: Select all Copy all objects Paste into your MS word document. This will ensure that your output is in a form that your instructor can read.
6. (10 pts) Create a results table consistent with requirements from the APA style manual.
7. (10 pts) Report the results using correct APA format.
a. For ANOVA, ANCOVA, and Repeated Measures ANOVA models, ensure that you provide interpretations for the main effects and interactions as well as any post-hoc tests.
b. For Multiple and Logistic Regression models, ensure that you include appropriate measures of model fit as well as the specific procedure used (e.g., Hierarchical, Enter, Stepwise, Forward, Backward).
8. (5 pts) Describe how you would compute the sample size to achieve 80% power, alpha = .05, and the appropriate effect size.