Correlation and regression analysis - Revision history

Sje025 at 11:16, 7 February 2023

2023-02-07T11:16:00Z

← Older revision		Revision as of 11:16, 7 February 2023
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	=Using jamovi=		=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].		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.		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.

Sje025 at 10:53, 7 February 2023

2023-02-07T10:53:34Z

← Older revision		Revision as of 10:53, 7 February 2023
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	Correlation is covered as the [[Media:CrashCourseRefresher_jamovi_Analyses.pdf\|third part of an introductory lecture on uni- and bivariate statistics]]. [[Media:Regression_jamovi_Lecture.pdf\|~~Another~~ lecture ]] ~~builds upon that by covering~~ 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].		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.		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.

Sje025: /* Using jamovi */

2023-02-07T10:52:25Z

Using jamovi

← Older revision		Revision as of 10:52, 7 February 2023
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	~~The~~ [[Media:Regression_jamovi_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 lecture also introduces how those analyses are carried out in [https://www.jamovi.org/ jamovi].		Correlation is covered as the [[Media:CrashCourseRefresher_jamovi_Analyses.pdf\|third part of an introductory lecture on uni- and bivariate statistics]]. [[Media:Regression_jamovi_Lecture.pdf\|Another lecture ]] builds upon that by covering 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.		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.

Sje025 at 17:36, 6 February 2023

2023-02-06T17:36:17Z

← Older revision		Revision as of 17:36, 6 February 2023
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	~~TBA~~		The [[Media:Regression_jamovi_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 lecture also introduces how those analyses are carried out in [https://www.jamovi.org/ jamovi].
	The [[Media:~~Correlation_and_regression_analysis_-_Lecture_jamovi~~.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 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.		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=		=Using SPSS=
	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.).		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_analysis_-_PC-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.		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_analyses_-_Assignment~~.pdf\|additional assignment]] ~~and the [[Media:Regression_analyses_-_Assignment_-_Solution.pdf\|solutions]] to it~~.		In addition, there is a file with an [[Media:Regression_SPSS_Assignment.pdf\|additional assignment]].

	Finally, there are two ZIP-files: [[~~Media~~:~~DataFiles~~.zip\|One]] with the data files required in the exercise and the assignments, [[Media:~~Regression_OutputSyntax~~.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.		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=		=More in-depth on the theoretical background=
	Both lecture slides ~~includes~~ practical examples for calculation: A [[Media:~~Regression_analysis_-_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>		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_analysis_-_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_analysis_-_Step~~-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS) which is described in the [[Media:~~Correlation_and_regression_analysis_-_Lecture~~.pdf\|lecture slides]] when using SPSS.		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.

Sje025: /* Using jamovi */

2023-02-06T16:12:11Z

Using jamovi

← Older revision		Revision as of 16:12, 6 February 2023
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	TBA		TBA
	~~<!--~~		The [[Media:Correlation_and_regression_analysis_-_Lecture_jamovi.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 lecture also introduces how those analyses are carried out in [https://www.jamovi.org/ jamovi].
	#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.).
	~~-->~~		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=		=Using SPSS=

Sje025 at 10:41, 22 March 2021

2021-03-22T10:41:25Z

← Older revision		Revision as of 10:41, 22 March 2021
Line 16:		Line 16:
	=More in-depth on the theoretical background=		=More in-depth on the theoretical background=
	Both lecture slides includes practical examples for calculation: A [[Media:Regression_analysis_-_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>		Both lecture slides includes practical examples for calculation: A [[Media:Regression_analysis_-_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_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS) which is described in the [[Media:Correlation_and_regression_analysis_-_Lecture.pdf\|lecture slides]] when using SPSS.		In addition, there is a [[Media:Regression_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS) which is described in the [[Media:Correlation_and_regression_analysis_-_Lecture.pdf\|lecture slides]] when using SPSS.

Sje025 at 10:40, 22 March 2021

2021-03-22T10:40:20Z

← Older revision		Revision as of 10:40, 22 March 2021
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	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
			<!--
			#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.).
			-->

	=Using SPSS=		=Using SPSS=

Sje025 at 10:38, 22 March 2021

2021-03-22T10:38:49Z

← Older revision		Revision as of 10:38, 22 March 2021
Line 1:		Line 1:
	=~~Theoretical introduction~~=		=Using jamovi=
	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 ~~and Octave~~), 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: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.).

	The lecture includes practical examples for calculation: A [[Media:Regression_analysis_-_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, as well as a [[Media:Regression_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS).

	=~~Practical exercises using~~ SPSS=		=Using SPSS=
			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.).

	The [[Media:Regression_analysis_-_PC-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.		The [[Media:Regression_analysis_-_PC-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.

Line 10:		Line 11:

	Finally, there are two ZIP-files: [[Media:DataFiles.zip\|One]] with the data files required in the exercise and the assignments, [[Media:Regression_OutputSyntax.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.		Finally, there are two ZIP-files: [[Media:DataFiles.zip\|One]] with the data files required in the exercise and the assignments, [[Media:Regression_OutputSyntax.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 includes practical examples for calculation: A [[Media:Regression_analysis_-_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_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS) which is described in the [[Media:Correlation_and_regression_analysis_-_Lecture.pdf\|lecture slides]] when using SPSS.

Sje025 at 17:08, 4 June 2019

2019-06-04T17:08:16Z

← Older revision		Revision as of 17:08, 4 June 2019
Line 9:		Line 9:
	In addition, there is a file with an [[Media:Regression_analyses_-_Assignment.pdf\|additional assignment]] and the [[Media:Regression_analyses_-_Assignment_-_Solution.pdf\|solutions]] to it.		In addition, there is a file with an [[Media:Regression_analyses_-_Assignment.pdf\|additional assignment]] and the [[Media:Regression_analyses_-_Assignment_-_Solution.pdf\|solutions]] to it.

	Finally, there are two ZIP-files: [[Media:DataFiles.zip\|One]] with the data files required in the exercise and the ~~assignment~~, ~~another~~ [[Media:Regression_OutputSyntax.zip\|another]] containing SPSS syntax (with comments) and SPSS output files for the ~~analysis~~ described in the main slides as well as for the additional ~~assignment~~.		Finally, there are two ZIP-files: [[Media:DataFiles.zip\|One]] with the data files required in the exercise and the assignments, [[Media:Regression_OutputSyntax.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.

Sje025 at 17:07, 4 June 2019

2019-06-04T17:07:30Z

← Older revision		Revision as of 17:07, 4 June 2019
Line 1:		Line 1:
	=Theoretical introduction=		=Theoretical introduction=
	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 and Octave), 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: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 and Octave), 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 includes practical examples for calculation: A [[Media:Regression_analysis_-_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, as well as a [[Media:Regression_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS).		The lecture includes practical examples for calculation: A [[Media:Regression_analysis_-_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, as well as a [[Media:Regression_analysis_-_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_analysis_-_Step-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS).

← Older revision		Revision as of 17:36, 6 February 2023
Line 1:		Line 1:
	=Using jamovi=		=Using jamovi=
	~~TBA~~		The [[Media:Regression_jamovi_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 lecture also introduces how those analyses are carried out in [https://www.jamovi.org/ jamovi].
	The [[Media:~~Correlation_and_regression_analysis_-_Lecture_jamovi~~.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 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.		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=		=Using SPSS=
	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.).		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_analysis_-_PC-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.		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_analyses_-_Assignment~~.pdf\|additional assignment]] ~~and the [[Media:Regression_analyses_-_Assignment_-_Solution.pdf\|solutions]] to it~~.		In addition, there is a file with an [[Media:Regression_SPSS_Assignment.pdf\|additional assignment]].

	Finally, there are two ZIP-files: [[~~Media~~:~~DataFiles~~.zip\|One]] with the data files required in the exercise and the assignments, [[Media:~~Regression_OutputSyntax~~.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.		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=		=More in-depth on the theoretical background=
	Both lecture slides ~~includes~~ practical examples for calculation: A [[Media:~~Regression_analysis_-_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>		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_analysis_-_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_analysis_-_Step~~-by-step.zip\|ZIP-file]] to replicate the analyses in SPSS) which is described in the [[Media:~~Correlation_and_regression_analysis_-_Lecture~~.pdf\|lecture slides]] when using SPSS.		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.