Walden University Week 4 Binary Logistic Regression in SPSS Discussion
Binary Logistic Regression in SPSS
This week you will build on the simple logistic regression analysis did last week. You will use the same two variables (one independent variable and one dependent variable) you used in your SPSS analysis last week and add a second independent variable to the analysis. How has your statistical significance and odds ratio changed by the introduction of the second independent variable?
To prepare
- Use the one independent variable and one dependent variable you used to conduct your simple logistic regression analysis in Week 4.
- Add a second independent variable to your analysis (multiple logistic regression).
- Remember that your dependent variable must be dichotomous/binary.
- Think about how you might use the odds ratio in your analysis to simplify the interpretation of your results.
- How has your statistical significance and odds ratio changed by the introduction of the second independent variable?
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The Assignment
Use SPSS to answer the research question you constructed. Write an analysis in APA format, including title page, references, and an appendix, that includes your data output and addresses each of the tasks listed above. The content should be 2–3 pages, including setup of the assignment and a discussion of whether the predictive relationship is statistically significant as well as the odds ratio and what it means. Your SPSS output should be included as an appendix.
Early in your Assignment, when you relate which dataset you analyzed, please include the mean of the following variables. If you are using the Afrobarometer Dataset, report the mean of Q1 (Age).
Instructor Notes:
In the text, report in APA form, these statistics, in this order:
– The overall test of model coefficients (Chi-square – use the Greek symbol, degrees of freedom, p)
– An estimate of the effect size for the overall model (Cox & Snell and/or Nagelkerke chi-square)
– For each predictor: The regression coefficient with Wald test, degrees of freedom, p value, odds ratio (Exp b) and confidence interval.
– Be sure to interpret the statistics. When interpreting the odds ratio, use the word “odds” (not “likelihood”).
Use proper APA format, citations, and referencing for your analysis, research question, and display of output.
- Title page (NO RUNNING HEAD, NO ABSTRACT)
- Text reporting the research question, which data set was used, how the analysis was done, results of any tests of assumptions, the results reported in APA format, brief interpretation of findings. Professor is a stickler for APA format. There are examples of what the results should look like for each assigned analysis in the Warner textbook. This does not have to be longer than 2 pages.
- Reference page
- Appendix which includes SPSS syntax and SPSS output
- Latin statistical symbols (e.g., N, M, SD, t, p) should be in italics.
- There should be a space on each side of equals (and inequality) signs.
- Never say Sig in the writeup. The correct symbol is p.
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Simple Logistic Regression
Walden University
12/27/20
Simple Logistic Regression
Simple logistic regression is predictive analysis, just like regression analysis. It is utilized to describe data and explain the relationship between a dependent variable and the independent variable. Independent variable can be nominal, ratio, or ordinal variable. Logistic regression is suitable when the dependent variable is dichotomous or binary. An afrobarometer data set was used for the analysis and some of the variables stated in the data set are country by region, respondent number, urban or rural, age, your present living conditions, importance of religion, age category, employment status, education category and internet usage.
The two variables selected for conducting simple logistic regression are the education category and employment status. The education category is an independent variable. The employment status is the dependent variable and it is a dichotomous variable having unemployed and employed. The research question is: can the education category predict the employment status? The null hypothesis is the education category cannot predict the employment status and the alternate hypothesis is the education category predicts the employment status.
Table 1 show that 10,319 cases were included in the analysis out of which 54 cases are missing. Table 2 depict that two categories of the dependent variable are unemployed and employed. Table 3 shows that the p-value is 0.001, which is less than the alpha value (0.05). This means the model is statistically significant. Hence, the education category predicts employment status. The odds ratio is given as 1.696, which means if the education category increases by 1 then there are chances that the employment will increase by 1.696 times. Table 4 depicts the mean of the education category is 1.40 and employment status is 0.3315 (Warner, 2013).
References
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: SAGE Publications.
Appendix
Table 1
Case Processing Summary Unweighted Casesa N Percent Selected Cases Included in Analysis 10265 99.5 Missing Cases 54 .5 Total 10319 100.0 Unselected Cases 0 .0 Total 10319 100.0 a. If weight is in effect, see classification table for the total number of cases. Table 2
Dependent Variable Encoding Original Value Internal Value Unemployed 0 Employed 1 Table 3
Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Step 1a EDUC_COND .528 .024 503.988 1 .000 1.696 1.619 1.776 Constant -1.483 .042 1223.371 1 .000 .227 a. Variable(s) entered on step 1: EDUC_COND. Table 4
Descriptive Statistics N Minimum Maximum Mean Std. Deviation Education Category 10301 -1 3 1.40 .949 Employment Status 10279 .00 1.00 .3315 .47079 Valid N (listwise) 10265 
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