A researcher is analyzing a dataset to determine whether the independent variables predict students’ reading level. The dependent variable is reading level (1=good reader, 0=poor reader). The four independent variables are age, SES (1=upper, 2=middle, 3=lower), teaching approach (1=computer, 2=traditional), and amount of reading (scaled data from 1-10).

*Please refer to SPSS Commands and the Logistic Regression Example.

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Your task:

Test the assumption of collinearity.
Analyze the data using logistic regression analysis. Your analysis should include discussion and interpretation of the following: comparison of crude vs. adjusted odds ratios (and corresponding 95% CI), log likelihood, R Square, Wald test and p-values, logistic regression equation, goodness-of-fit tests, and predictive accuracy of the model. Include summary tables.
Assignment Expectations

Length: Case assignments should be at least 3 pages (750 words) in length excluding tables.

References: Any references used should be from academic sources and cited using APA format.

Organization: Subheadings should be used to organize your paper according to question.

Format: This assignment should be written in a scientific format (as in the “Results” section of a peer-reviewed study). APA format is required for all assignments at the PhD level. Refer to the following guidelines in presenting tables and results in APA format: 1. APA Table Guidelines, 2. Reporting Results. See Syllabus page for more information on APA format.

Grammar and Spelling: While no points are deducted for minor errors, assignments are expected to adhere to standard guidelines of grammar, spelling, punctuation, and sentence syntax. Points may be deducted if grammar and spelling impact clarity.

The following items will be assessed in particular:

Relevance—All content is connected to the question.
Precision—Specific questions are addressed. Calculations, statements, facts, and statistics are specific and accurate.
Depth of discussion—Points are presented and integrated.
Evidence—Statements are well supported with facts, statistics or references.
Logic—Presented discussion makes sense; conclusions are logically supported by premises, statements, or factual information.
Clarity—Writing is concise and understandable, and subjects are sufficiently described.
Objectivity—The use of first person and subjective bias are avoided.

xplain about the logistic regression

Princeton University (N/A). Logit Models for Binary Data. Retrieved May 2016 from http://data.princeton.edu/wws509/notes/c3.pdf

Terms and concepts in logistic regression

Sperandei, S. (2014). Understanding logistic regression analysis. Retrieved June 2018 from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3936971/pdf/biochem-24-1-12-4.pdf

Park, H. (2013). An Introduction to Logistic Regression. From Basic Concepts to Interpretation with Particular Attention to the Nursing Domain. Retrieved June 2018 from https://pdfs.semanticscholar.org/3305/2b1d2363aee3ad290612109dcea0aed2a89e.pdf

Logistic regression in SPSS

Laerd Statistics. (2018). Binomial Logistic Regression Using SPSS Statistics. Retrieved June 2018 from https://statistics.laerd.com/spss-tutorials/binomial-logistic-regression-using-spss-statistics.php

Additional resources for SPSS

for a “How to do logistic regression with SPSS” follow these steps:

Access elluminate
Go to “Recordings”
Go to Room DEL618
Go to the date of the live session
Open your speakers

Write-up results of tables and figures in APA format:

Quick, D. (N/A) Making Tables and Figures. Retrieved July, 2018 from: http://web.cortland.edu/hendrick/APA%20Making%20Tables%20and%20Figures.pdf
Kirk, S. How to write the results section. Retrieved from July, 2018 from: https://www.youtube.com/watch?v=pKAJz3eNxbg

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