View source for Correlation and regression analysis

=Using jamovi=
Correlation is covered as the [[Media:CrashCourseRefresher_jamovi_Analyses.pdf|third part of an introductory lecture on uni- and bivariate statistics]]. Building upon that, [[Media:Regression_jamovi_Lecture.pdf|another lecture]] covers multivariate analyses. It 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 lecture also introduces how those analyses are carried out in [https://www.jamovi.org/ jamovi].

For an introduction from another perspective, you can head to chapter 12 of [https://lsj.readthedocs.io/en/latest/Ch12/Ch12_Regression.html “learning statistics with jamovi”] or to a [https://www.youtube.com/watch?list=PLkk92zzyru5OAtc_ItUubaSSq6S_TGfRn&v=gRhVjKNWiUs video introduction] by Barton Poulson from datalab.cc.

=Using SPSS=
The [[Media:Regression_SPSS_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.).

The [[Media:Regression_SPSS_Exercise.pdf|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 [[Media:Regression_SPSS_Assignment.pdf|additional assignment]].

Finally, there are two ZIP-files: [[:File:DataFiles_Field_TabachnikFidell.zip|One]] with the data files required in the exercise and the assignments, [[Media:Regression_SPSS_SyntaxOutput.zip|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 include practical examples for calculation: A [[Media:Regression_Step-by-step.ods|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.<br>
In addition, there is a [[Media:Regression_Step-by-step.txt|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 [[Media:Regression_Step-by-step.zip|ZIP-file]] to replicate the analyses in SPSS), which is described in the [[Media:Regression_SPSS_Lecture.pdf|lecture slides]] when using SPSS.