NotesFAQContact Us
Collection
Advanced
Search Tips
ERIC Number: ED416213
Record Type: Non-Journal
Publication Date: 1998-Jan-23
Pages: 22
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
A Primer on Logistic Regression.
Woldbeck, Tanya
This paper introduces logistic regression as a viable alternative when the researcher is faced with variables that are not continuous. If one is to use simple regression, the dependent variable must be measured on a continuous scale. In the behavioral sciences, it may not always be appropriate or possible to have a measured dependent variable on a continuous scale. Logistic regression, a technique derived from logit modeling or logit analysis, has been the analysis of choice in many areas of research in this situation. Logistic regression, like the other regression analyses still looks at the relationship between the variables of interest as the core focus of the analysis, but it uses the concept of the odds ratio as its measure of association. An example illustrates the interpretation of coefficients using the odds ratio. Traditionally the chi-square statistic has been used to test for independence between variables. In logistic regression, the likelihood-ratio-chi-squared statistic (G-squared) is important in comparing the observed and expected frequencies of the variable of interest to assess the goodness of fit of the model. Logistic regression has been used in many areas of research in the behavioral sciences, and can be used in detecting differential item functioning. It is a part of statistical software packages, and an example is included of use of this viable and efficient tool for statistical analysis. (Contains 5 tables, 1 figure, and 18 references.) (SLD)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A