Difference between revisions of "Correlation and regression analysis"
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| − | The [[Media:Correlation_and_regression_analysis_-_Lecture.pdf|lecture]] begins with a basic introduction (what are typical research questions, what kind of variables are used, what assumptions and requirements need to be met to apply the method), continues with an overview over fundamental equations (incl. some do-it-yourself in Excel), introduces then major regression types (standard, sequential/hierarchical, statistical/stepwise) and ends with some important issues (limitations, aspects to be attentive of, etc.). | + | TBA |
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| + | #The [[Media:Correlation_and_regression_analysis_-_Lecture.pdf|lecture]] begins with a basic introduction (what are typical research questions, what kind of variables are used, what assumptions and requirements need to be met to apply the method), continues with an overview over fundamental equations (incl. some do-it-yourself in Excel), introduces then major regression types (standard, sequential/hierarchical, statistical/stepwise) and ends with some important issues (limitations, aspects to be attentive of, etc.). | ||
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=Using SPSS= | =Using SPSS= | ||
Revision as of 12:40, 22 March 2021
Using jamovi
TBA
Using SPSS
The lecture begins with a basic introduction (what are typical research questions, what kind of variables are used, what assumptions and requirements need to be met to apply the method), continues with an overview over fundamental equations (incl. some do-it-yourself in Excel), introduces then major regression types (standard, sequential/hierarchical, statistical/stepwise) and ends with some important issues (limitations, aspects to be attentive of, etc.).
The PC exercise deals with the practical aspects of carrying out regression analyses in SPSS. There are two major parts dealing with linear regression and logistic regression. The part on Linear regression analysis begins with an assignment (on how to check requirements for calculating a linear regression), then demonstrates the equivalency of correlation to Linear regression (if there is only one predictor), how multiple predictors can be included in the regression model (incl. different methods for adding predictors) and end with how to assess the quality of your model. This is followed by an assignment to test the acquired knowledge practically. The logistic (binary) regression part consists of a basic introduction on the method (focussing on how a logistic function can be used to convert from a continuous to a binary outcome), followed by an assignment to use the method practically.
In addition, there is a file with an additional assignment and the solutions to it.
Finally, there are two ZIP-files: One with the data files required in the exercise and the assignments, another containing SPSS syntax (with comments) and SPSS output files for the analyses described in the main slides as well as for the additional assignments.
More in-depth on the theoretical background
Both lecture slides includes practical examples for calculation: A LibreOffice/Excel-file with a demonstration how a correlation (i.e., a very simple regression with one variable; example from Field (2018), Ch. 8) is calculated by hand.
In addition, there is a syntax file to run a regression analysis in MATLAB / Octave (from Tabachnik & Fidell (2013), Ch. 5.4; the data can also be found in a ZIP-file to replicate the analyses in SPSS) which is described in the lecture slides when using SPSS.
