28 Jun U05D2 – Types of Logistic Regression
In this discussion, a ficticious researcher plans to do a logistic regression using the default (enter) method. Using the enter method, Warner (2013) and George and Mallery (2013) suggested that all independent variables are entered into the equation in all at once. This method allows the researcher to handle a block of variables at one time. The advantage of this approach is that all variables are simultaneously calculated. The effects of variables can be seen as they exist in total.
Next, the researcher’s friend Barbara suggests that the researcher should do a sequential logistic regression instead. The sequential logistic regression approach represents a model in which terms are added sequentially until the model fit can no longer be improved (Sawtelle, Brewe, & Kramer, 2011; Stockburger, n.d.). An advantage of this approach is that the sequential method introduces a means by which missing values could be imputed into the researcher’s data (Raghunathan, Lepkowski, Van Hoewyk, & Solenberger, 2001). The obvious disadvantage is that the researcher might be tempted to use this approach if cases within the data have missing values and should be discarded as invalid entries.
Lastly, the researcher’s friend, Linda, tells the researcher that a stepwise logistic regression is the way to go. The stepwise method in SPSS (George & Mallery, 2013) removes one variable at a time given those variables with the weakest correlations first (School of Geography, n.d.). This approach has the advantage of helping the researcher focus on only the variables that display the best-fit for the distribution, however a disadvantage of the stepwise approach is that a number of variables representative of what might be a relatively valid variables might be discarded due to a bad model fit and was not recommended for use (Smith, 2012).
In order to determine the criteria the researcher should sue to decide the best approach for the logistic regression model, the researhcer would need to perform an initial review of the data. If the data has missing data and the researcher deems it neccesary to impute values for missing data, he could consider the stepwise approach. This approach would also assume that a large enough sample existed by which missing values could be estimated. If, however, the researcher wanted to determine which variables were the best representatives of the data set and research question, he might choose the stepwize approach. Lastly, if the researcher had a relatively perfect data set and that cases within the data set could be discarded without severe impact on the reliability of the research, he might consider the enter method of logistic regression.
References
George, D., & Mallery, P. (2013). IBM statistics 21 step by step: A simple guide and reference (13th ed.). Pearson. Retrieved from http://online.vitalsource.com/books/9781269627795
Raghunathan, T. E., Lepkowski, J., Van Hoewyk, J., & Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27(1), 85-95.
Sawtelle, V., Brewe, E., & Kramer, L. H. (2011). Sequential logistic regression: A method to reveal subtlety in self-efficacy. In Proceedings of the tenth annual college of education & GSN research conference (pp. 216-225). Miami, FL: M. S. Plakhotnik, S. M. Nielsen; D. M. Pane.
School of Geography. (n.d.). Stepwise liear regression. Retrieved Feb 07, 2017, from University of Leeds: http://www.geog.leeds.ac.uk/courses/other/statistics/spss/stepwise/
Smith, M. K. (2012). Problems with stepwise model selection procedures. Retrieved from Common mistakes in using statistics: Spotting and avoiding them: https://www.ma.utexas.edu/users/mks/statmistakes/stepwise.html
Stockburger, D. W. (n.d.). Multiple regression with many predictor variables. Retrieved from psychstat.missouristate.edu: http://www.psychstat.missouristate.edu/multibook/mlt07.htm
Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage. Retrieved from http://online.vitalsource.com/books/9781452268705
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