EDF 7405 Advanced Quantitative Methods in Educational Research DISC.SAS. The data:

Transcription

1 1 Starting SPSS: EDF 7405 Advanced Quantitative Methods in Educational Research DISC.SAS The data: Maternal Age Intelligence

2 2 Entering the data: Please note that almost all screen shots in the directions for using SPSS are from SPSS 14. The appearance of these screen shots will be slightly different than the appearance of screen shots created from earlier or later versions of SPSS. An occasional a screen shot is from SPSS16.

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5 Saving the data as an SPSS SAV file 5

6 6 Listing the data: I will not list my data in every program, but you should list yours in every program and double check for data entry errors.

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9 9 Summarize Case Processing Summary a Cases Included Excluded Total N Percent N Percent N Percent MAGE % 0.0% % IQ % 0.0% % a. Limited to first 100 cases. Case Summaries a MAGE IQ Total N a. Limited to first 100 cases. Analyzing the data:

10 Calculating descriptive statistics 10

11 11 Descriptives Descriptive Statistics MAGE IQ Valid N (listwise) Std. N Minimum Maximum Mean Deviation Constructing a scatterplot

12 This screen shot is from an older version of SPSS. With SPSS 15 or 16, select legacy diaglogs. 12

13 13 Graph Plot of Intelligence Versus Maternal Agae IQ MAGE

14 Calculating covariances and correlations: 14

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16 16 Correlations Correlations MAGE IQ MAGE Pearson Correlation * Sig. (2-tailed)..033 Sum of Squares and Cross-products Covariance N IQ Pearson Correlation.642* Sig. (2-tailed).033. Sum of Squares and Cross-products Covariance N *. Correlation is significant at the 0.05 level (2-tailed).

17 17 EDF 7405 Advanced Quantitative Methods in Educational Research SIMPR.SAS See pages for directions to calculate descriptive statistics. Descriptives Descriptive Statistics MAGE IQ Valid N (listwise) Std. N Minimum Maximum Mean Deviation See pages for directions to construct a scatterplot. Graph 160 Scatterplot of IQ versus Mage IQ MAGE

18 Conducting a regression analysis and computing predicted values and residuals: 18

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21 21 Note that the predicted values (PRE_1) and residuals (RES_1) have been added to the data set: Regression Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 MAGE a. Enter a. All requested variables entered. b. Dependent Variable: IQ Model Summary Std. Error Model R R Square Adjusted R Square of the Estimate a a. Predictors: (Constant), MAGE is SY X, a quantity I call the standard error of estimate.

22 22 Model 1 Regression Residual Total a. Predictors: (Constant), MAGE b. Dependent Variable: IQ ANOVA b Sum of Mean Squares df Square F Sig a In the table above Sig. refers to the p value: Prob F Unstandardized Coefficients Coefficients a Standardi zed Coefficien ts Model B Std. Error Beta t Sig. 1 (Constant) MAGE a. Dependent Variable: IQ SPSS uses beta (.135 above) to refer to the standardized slope for the sample. Beta is not in Y X. The estimate of in this equation is b.516. SPSS uses Constant to refer to the intercept. The estimate of the intercept is a In the table above Sig. refers to the p value: Pr ob t Drawing the regression line on a scatterplot 1. Run the program to produce a scatterplot (see pages for directions to construct a scatterplot). 2. Click once in the center of the plot. (A box will be displayed around the plot). 3. Click two times. (The plot is displayed in the SPSS for Windows Chart Editor). 4. Click twice outside the plot area. The following is displayed:

23 23 The plot will be displayed, but you will be in the SPSS for Windows Chart Editor. Close the SPSS for Windows Chart Editor to get back to the window in which the output is displayed (SPSS for Windows Viewer). Graph 160 Scatterplot of IQ versus Mage IQ MAGE

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25 25 EDF 7405 Advanced Quantitative Methods in Educational Research This shows how to use SPSS to do a basic logistic regression. After importing the data into the SPSS Data Editor, click Analyze, Regression (see page 18). However click Binary Logistic in place of Linear. For my the data the result is Move PROMATH into the dependent slot because it is the 0-1 indicating failing and passing the 8 th grade test. Move MATMATH4 into the Covariates slot. Here covariate is being used as a synonym for independent variable. The results are Logistic Regression Unweighted Cases a Selected Cases Unselected Cases Total Case Processing Summary Included in Analysis Missing Cases Total N Percent a. If weight is in effect, see classification table for the total number of cases.

26 26 Dependent Variable Encoding Original Value 0 1 Internal Value 0 Block 0: Beginning Block 1 Classification Table a,b Predicted Observed Step 0 PROMATH 0 1 Overall Percentage a. Constant is included in the model. b. The cut value is.500 PROMATH Percentage 0 1 Correct Step 0 Constant Variables in the Equation B S.E. Wald df Sig. Exp(B) Variables not in the Equation Step 0 Variables MATMATH4 Overall Statistics Block 1: Method = Enter Score df Sig Omnibus Tests of Model Coefficients Step 1 Step Block Model Chi-square df Sig Model Summary Step 1-2 Log Cox & Snell Nagelkerke likelihood R Square R Square a a. Estimation terminated at iteration number 5 because parameter estimates changed by less than.001.

27 27 Classification Table a Predicted Observed Step 1 PROMATH Overall Percentage a. The cut value is PROMATH Percentage 0 1 Correct Variables in the Equation B S.E. Wald df Sig. Exp(B) Step MATMATH a Constant a. Variable(s) entered on step 1: MATMATH4.

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29 29 EDF 7405 Advanced Quantitative Methods in Educational Research HETERO.SAS The variables in this example are the number of workers supervised 27 industrial companies and the number of supervisors in the same companies. In the analysis number of supervisors is the dependent variable. The new feature of this program is a residual plot. Residual plots are used to detect violations of assumptions, The Data Workers Supervisors

30 30 See pages for directions to calculate descriptive statistics. Descriptives Descriptive Statistics WORKERS SUPERS Valid N (listwise) Std. N Minimum Maximum Mean Deviation See pages for directions to construct a scatterplot. Graph 300 Plot of # of Supervisors vs. # of Workers 200 SUPERS WORKERS

31 31 Conducting the regression analysis and the Saving Studentized residuals We want to save the Studentized residuals. A studentized residual is a residual ( e Y Yˆ ) divided by its standard error (denoted as Se ). Follow the steps to produce a regression analysis (see pages 18-19) until you produce the following screen:

32 32 The following shows that the Studentized residuals have been added to the data set. These are now available for plotting. If you want to, these can be saved for future work.

33 33 Regression Variables Entered/Removed b Model 1 Variables Entered Variables Removed Method WORKERS a. Enter a. All requested variables entered. b. Dependent Variable: SUPERS Model 1 Model Summary b Std. Error Adjusted of the R R Square R Square Estimate.881 a a. Predictors: (Constant), WORKERS b. Dependent Variable: SUPERS Model 1 Regression Residual Total ANOVA b Sum of Mean Squares df Square F Sig a a. Predictors: (Constant), WORKERS b. Dependent Variable: SUPERS Model 1 (Constant) WORKERS Coefficients a Unstandardized Coefficients a. Dependent Variable: SUPERS Standardi zed Coefficien ts B Std. Error Beta t Sig

34 34 Predicted Value Residual Std. Predicted Value Std. Residual a. Dependent Variable: SUPERS Constructing a residual plot Residuals Statistics a Std. Minimum Maximum Mean Deviation N E Follow the steps for constructing a scatterplot (see pages for directions to construct a scatterplot). The Studentized residuals will be available for plotting:

35 35 Graph Residual Plot: Studentized Residuals vs. Worke 2 1 Studentized Residual WORKERS

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37 37 EDF 7405 Advanced Quantitative Methods in Educational Research HETERO1.SAS This program uses the worker data to illustrate weighted least squares analysis, a procedure used when the homogeneity of variance assumption is violated. This is accomplished by including a weight in the data. Typically one starts with 1 X 2 where X is the independent variable. In our case this is workers. Calculating the weight

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40 40 Conducting the weighted least squares regression analysis Follow the steps to produce a regression analysis (see page 18) until you get to the following screen:

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42 42 Regression Variables Entered/Removed b,c Model 1 Variables Variables Entered Removed Method WORKERS a. Enter a. All requested variables entered. b. Dependent Variable: SUPERS c. Weighted Least Squares Regression - Weighted by WEIGHT Model Summary b,c Std. Error Model R R Square Adjusted R Square of the Estimate a E-02 a. Predictors: (Constant), WORKERS b. Dependent Variable: SUPERS c. Weighted Least Squares Regression - Weighted by WEIGHT ANOVA b,c Model Sum of Squares df Mean Square F Sig. 1 Regression 9.286E E a Residual 1.284E E-04 Total a. Predictors: (Constant), WORKERS b. Dependent Variable: SUPERS c. Weighted Least Squares Regression - Weighted by WEIGHT Unstandardized Coefficients Coefficients a,b Standardi zed Coefficien ts Model B Std. Error Beta t Sig. 1 (Constant) WORKERS a. Dependent Variable: SUPERS b. Weighted Least Squares Regression - Weighted by WEIGHT

43 43 Predicted Value Std. Predicted Value a Standard Error of Predicted Value Residuals Statistics b,c Std. Minimum Maximum Mean Deviation N Adjusted Predicted Value Residual Std. Residual a Stud. Residual Deleted Residual Stud. Deleted Residual Mahal. Distance Cook's Distance Centered Leverage Value a. Not computed for Weighted Least Squares regression. b. Dependent Variable: SUPERS c. Weighted Least Squares Regression - Weighted by WEIGHT See pages for directions to construct this scatterplot. Graph 2.0 Resid. Plot from Weight. Least Squares 1.5 Studentized Residual WORKERS

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45 45 EDF 7405 Advanced Quantitative Methods in Educational Research HETERO2.SAS This program uses the workers data to illustrate the use of transformations in regression analysis. Here a logarithmic transformation is used in an attempt to remove heteroscedasticity of variance. In this approach the logarithm of the dependent variable is used as the dependent variable. In our example Number of supervisors is the dependent variable. Calculating the logarithm

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47 47 Constructing the scatterplot Follow the usual steps to construct a scatterplot (see pages for directions to construct a scatterplot), but use logs as the dependent variable.

48 48 Graph LOGS WORKERS Conducting the regression analysis Follow the usual steps to conduct the regression analysis but use logs as the dependent variable. As usual save the Studentized residuals.

49 49 Regression Variables Entered/Removed b Model 1 Variables Entered Variables Removed Method WORKERS a. Enter a. All requested variables entered. b. Dependent Variable: LOGS Model Summary b Std. Error Model R R Square Adjusted R Square of the Estimate a a. Predictors: (Constant), WORKERS b. Dependent Variable: LOGS

50 50 Model 1 Regression Residual Total ANOVA b Sum of Mean Squares df Square F Sig a E a. Predictors: (Constant), WORKERS b. Dependent Variable: LOGS Model 1 (Constant) WORKERS a. Dependent Variable: LOGS Coefficients a Unstandardized Coefficients Standardi zed Coefficien ts B Std. Error Beta t Sig E Residuals Statistics a Minimum Maximum Mean Std. Deviation N Predicted Value Std. Predicted Value Standard Error of Predicted Value 4.902E E E Adjusted Predicted Value Residual E Std. Residual Stud. Residual Deleted Residual E Stud. Deleted Residual Mahal. Distance Cook's Distance Centered Leverage Value a. Dependent Variable: LOGS

51 51 Graph 2 1 Studentized Residual WORKERS