Exam #2 Laboratory #2 Correlations and Regression on SPSS



Psyc451 Exam#1 Lab#2Name:_________________________ Bivariate & Multivariate Regression Dataset pack1mod.savWalk-Through -- The criterion variable will be graduate GPA & the predictors will be prog, gender, GREA, GREQ, and GREVObtain the bivariate model using each predictor, in turn.Fill in the followingPredictorb βpaDoes the predictor “work”?Interpretation of b weight (be sure to use the proper wording for quantitative vs. binary predictors)ProgGREAGREQGREVgenderGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?ProgGREAGREQGREVgenderConstant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” ProgGREAGREQGREVGenderConstant (a)Write it up, following the example in the handout (use a single table – don’t worry about the means and std).Your Turn #1 Dataset pack2mod.savThe criterion variable will be depression & the predictors will be stress, SES, salary satisfaction, financial independence, and marital status.Obtain the bivariate model using each predictor, in turn.Fill in the followingPredictorb βpaDoes the predictor “work”?Interpretation of b weightstressSESsalary satisfactionfinancial independencemarital statusGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?stressSESsalarysatisfactionfinancialindependencemaritalstatusConstant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” stressSESsalarysatisfactionfinancialindependencemarital statusConstant (a) Write it up, following the example in the handout (use a single table – don’t worry about the means and std).About the “Your Turn,” your Laboratory Project and the ConferenceThis is the second week to work on your project. If you do a decent job this week of finding a criterion and some predictors, the rest of the project will go very smoothly (on the other hand, if you dog this assignment you’ll have tons to do to catch up).You may use the same criterion/predictors you used last week, change some, or change all of them -- your choice. Remember, a “good story” isn’t just “a set of all significant” predictors. Rather, it is a combination of things that do and don’t correlate/have multivariate contributions, so we can tell an interesting story about how these variables relate to the criterion.So, use this assignment to find a criterion and set of predictorsHave a mix of “kinds of predictors” (demographics, etc.)Have a mix of significant and nonsignificant multivariate contributors (6-8 predictors is plenty!!!)You may try multiple criterion variables in any data set, but you must present below data from at least 2 different data sets!When you are done with the four Your Turns, pick 2 of them to write up like the Walk Through above and the example on the handouts.Your Turn #2 Note: criterion must be quantitative – predictors must be quantitative or binaryData set ________________________ criterion variable ________________________________Obtain the bivariate model using each predictor, in turn.Fill in the following Predictorb βpaDoes the predictor “work”?Interpretation of b weightGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?Constant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:1. Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” Constant (a)Your Turn #3 Note: criterion must be quantitative – predictors must be quantitative or binaryData set ________________________ criterion variable ________________________________Obtain the bivariate model using each predictor, in turn.Fill in the following Predictorb βpaDoes the predictor “work”?Interpretation of b weightGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?Constant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:1. Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” Constant (a)Your Turn #4 Note: criterion must be quantitative – predictors must be quantitative or binaryData set ________________________ criterion variable ________________________________Obtain the bivariate model using each predictor, in turn.Fill in the following Predictorb βpaDoes the predictor “work”?Interpretation of b weightGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?Constant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:1. Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” Constant (a)Your Turn #5 Note: criterion must be quantitative – predictors must be quantitative or binaryData set ________________________ criterion variable ________________________________Obtain the bivariate model using each predictor, in turn.Fill in the following Predictorb βpaDoes the predictor “work”?Interpretation of b weightGet the correlation of each of the predictors with the criterion and the multiple regression using all the predictorsFill in the followingR? _________ F ___________ df ____, ___________ p __________ Predictorcorrelationsmultiple regressionrpIs this a viable bivariate predictor?bβpDoes the predictor contribute to the multivariate model?Constant (a)Does the multiple regression model work? How well? What variables contribute to the model? What contributors are “most important”?Interpret each multiple regression b weight (be sure to use the proper wording for quantitative vs. binary predictors). Also, for each predictor, compare its bivariate relationship with the criterion (from the simple regression) and its contribution to the multivariate model (from the multiple regression weight). Identify each predictor as one of the following:1. Predictor is neither correlated nor makes a multivariate contributionCorrelation and multivariate contribution have the same (non-zero) signPredictor is correlated, but does not make a multivariate contribution (probably is collinear with other predictors)Suppressor that is not correlated, but makes a multivariate contributionSuppressor that is correlated and makes a multivariate contribution, but with opposite (non-zero) signsVariableInterpretation“Type of Predictor” Constant (a)When you are done with the four Your Turns, pick 2 of them to write up like the Walk Through above and the example on the handouts. ................
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