Correlations. Notes. Output Created Comments 04-OCT :34:52

Size: px
Start display at page:

Download "Correlations. Notes. Output Created Comments 04-OCT :34:52"

Transcription

1 Correlations Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time 0-OCT-01 1:: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> 11 User-defined missing values are treated as missing. Statistics for each pair of variables are based on all the cases with valid data for that pair. CORRELATIONS /VARIABLES= /PRINT=TWOTAIL NOSIG /STATISTICS DESCRIPTIVES /MISSING=PAIRWISE. 00:00:0. 00:00:0. Descriptive Statistics Mean Std. Deviation N Correlations Pearson Correlation Sig. (-tailed) N Pearson Correlation Sig. (-tailed) N SurvivalPerce ntage 1. ** ** **. Correlation is significant at the 0.01 level (-tailed). Page 1

2 General Linear Model Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time 0-OCT-01 1::01 C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> 11 User-defined missing values are treated as missing. Statistics are based on all cases with valid data for all variables in the model. GLM BY ShipType /METHOD=SSTYPE() /INTERCEPT=INCLUDE /POSTHOC= ShipType (TUKEY) /EMMEANS=TABLES(OVERALL) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.0) /DESIGN= ShipType... 00:00:0. 00:00:0.1 Page

3 Between-Subjects Factors ShipType 1 1 Value Label N Battleship 0 Cruiser Aircraft Carrier Descriptive Statistics ShipType Mean Std. Deviation N Battleship Total Cruiser Page

4 Descriptive Statistics ShipType Mean Std. Deviation N Total Total Aircraft Carrier Total Total Total Battleship Page

5 Descriptive Statistics ShipType Mean Std. Deviation N Total Cruiser Total Total Aircraft Carrier Total..1 1 Page

6 Descriptive Statistics ShipType Mean Std. Deviation N Total Total.. 11 Multivariate Tests a Effect Value F Hypothesis df Error df Sig. Intercept Pillai's Trace b Wilks' Lambda b Hotelling's Trace b Roy's Largest Root b ShipType Pillai's Trace Wilks' Lambda.. b Hotelling's Trace Roy's Largest Root.1. c Pillai's Trace Wilks' Lambda b Hotelling's Trace Roy's Largest Root.0.0 c ShipType * Pillai's Trace Wilks' Lambda.. b Hotelling's Trace Roy's Largest Root.0. c a. Design: Intercept + ShipType + + ShipType * b. Exact statistic c. The statistic is an upper bound on F that yields a lower bound on the significance level. Page

7 Tests of Between-Subjects Effects Source Corrected Model Intercept ShipType ShipType * Error Total Corrected Total Dependent Variable Type III Sum of Squares df Mean Square F Sig.. a b a. R Squared =.00 (Adjusted R Squared =.00) b. R Squared =.1 (Adjusted R Squared =.1) Estimated Marginal Means Grand Mean % Confidence Interval Dependent Variable Mean Std. Error Lower Bound Upper Bound.0 a a a. Based on modified population marginal mean. Post Hoc Tests Page

8 Tukey HSD Dependent Variable (I) (J) Multiple Comparisons Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound.00 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Page

9 Tukey HSD Dependent Variable (I) (J) Multiple Comparisons Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound -.01 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Page

10 Tukey HSD Dependent Variable (I) (J) Multiple Comparisons Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound.01 * * * * * * * * * * * * * * * * * * * * * * * * * * * Page

11 Tukey HSD Dependent Variable (I) (J) Multiple Comparisons Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound -.0 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Page 11

12 Tukey HSD Dependent Variable (I) (J) Multiple Comparisons Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound -.0 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Page 1

13 Multiple Comparisons Tukey HSD Dependent Variable (I) (J) 1 Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound.001 * * * * * * * * * * * * * * * Based on observed means. The error term is Mean Square(Error) =.01. *. The mean difference is significant at the.0 level. Homogeneous Subsets Page 1

14 Tukey HSD a,b 1 Sig. N Subset Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) =.00. a. Uses Harmonic Mean Sample Size = 1.1. b. Alpha =.0. Page 1

15 Tukey HSD a,b 1 Sig. N 1. Subset Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) =.01. a. Uses Harmonic Mean Sample Size = 1.1. b. Alpha =.0. ShipType Page 1

16 Multiple Comparisons Tukey HSD Dependent Variable (I) ShipType (J) ShipType Battleship Cruiser Aircraft Carrier Cruiser Battleship Aircraft Carrier Battleship Cruiser Aircraft Carrier Aircraft Carrier Battleship Cruiser Battleship Cruiser Aircraft Carrier Cruiser Battleship Aircraft Carrier Battleship Cruiser Aircraft Carrier Aircraft Carrier Battleship Cruiser Mean Difference (I- J) Std. Error Sig. % Confidence Interval Lower Bound Upper Bound.00 * * * * * * * * * * * * * * * * * * * * * * Based on observed means. The error term is Mean Square(Error) =.01. *. The mean difference is significant at the.0 level. Homogeneous Subsets Page 1

17 Tukey HSD a,b ShipType Aircraft Carrier Cruiser Battleship Sig. Subset N Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) =.00. a. Uses Harmonic Mean Sample Size = b. Alpha =.0. Tukey HSD a,b ShipType Cruiser Battleship Aircraft Carrier Sig. Subset N Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) =.01. a. Uses Harmonic Mean Sample Size = b. Alpha =.0. Partial Corr Page 1

18 Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time -OCT-01 0:: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> User defined missing values are treated as missing. 11 Statistics are based on cases with no missing data for any variable listed. PARTIAL CORR /VARIABLES= BY /SIGNIFICANCE=TWOTAIL /STATISTICS=DESCRIPTIVES /MISSING=LISTWISE. 00:00:0. 00:00:0. Descriptive Statistics Mean Std. Deviation N Correlations Control Variables Correlation Significance (-tailed) df Correlation Significance (-tailed) df Partial Corr SurvivalPerce ntage Page 1

19 Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time -OCT-01 0:: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> User defined missing values are treated as missing. 11 Statistics are based on cases with no missing data for any variable listed. PARTIAL CORR /VARIABLES= BY ShipType /SIGNIFICANCE=TWOTAIL /STATISTICS=DESCRIPTIVES /MISSING=LISTWISE. 00:00:0. 00:00:0.1 Descriptive Statistics ShipType Mean Std. Deviation N Correlations Control Variables & ShipType Correlation Significance (-tailed) df Correlation Significance (-tailed) df SurvivalPerce ntage Page 1

20 Partial Corr Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time -OCT-01 0:: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> User defined missing values are treated as missing. 11 Statistics are based on cases with no missing data for any variable listed. PARTIAL CORR /VARIABLES= BY ShipType /SIGNIFICANCE=TWOTAIL /STATISTICS=DESCRIPTIVES /MISSING=LISTWISE. 00:00:0.1 00:00:0. Descriptive Statistics ShipType Mean Std. Deviation N Correlations Control Variables ShipType Correlation Significance (-tailed) df Correlation Significance (-tailed) df SurvivalPerce ntage Page 0

21 Univariate Analysis of Variance Notes Output Created Comments Input Missing Value Handling Syntax Resources Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time 1-OCT-01 1:0:1 C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> 11 User-defined missing values are treated as missing. Statistics are based on all cases with valid data for all variables in the model. UNIANOVA BY ShipType WITH /METHOD=SSTYPE() /INTERCEPT=INCLUDE /PLOT=PROFILE( ShipType *ShipType) /EMMEANS=TABLES(OVERALL) WITH(=MEAN) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.0) /DESIGN= ShipType *ShipType. 00:00:0.1 00:00:0. Page 1

22 Between-Subjects Factors 1 ShipType 1 Value Label N Battleship 0 Cruiser Aircraft Carrier 1 Descriptive Statistics Dependent Variable: ShipType Mean Std. Deviation N 1 Cruiser..0 Total..0 Battleship.1.0 Cruiser Total Battleship..0 1 Cruiser Total Battleship..0 0 Cruiser Aircraft Carrier..00 Total Battleship Cruiser Page

23 Descriptive Statistics Dependent Variable: ShipType Mean Std. Deviation N Aircraft Carrier Total Battleship.11.0 Cruiser Aircraft Carrier Total Battleship.0.01 Cruiser Aircraft Carrier Total.00.0 Battleship.0.0 Cruiser Aircraft Carrier Total Battleship Cruiser Aircraft Carrier Total.01.0 Battleship Cruiser Aircraft Carrier..1 0 Total.0.01 Total Battleship Cruiser Aircraft Carrier Total Page

24 Tests of Between-Subjects Effects Dependent Variable: Source Corrected Model Intercept ShipType * ShipType Error Total Corrected Total Type III Sum of Squares df Mean Square F Sig. 11. a a. R Squared =.1 (Adjusted R Squared =.1) Estimated Marginal Means Grand Mean Dependent Variable: % Confidence Interval Mean Std. Error Lower Bound Upper Bound.0 a,b a. Covariates appearing in the model are evaluated at the following values: =.. b. Based on modified population marginal mean. Profile Plots Page

25 Estimated Marginal Means of. Estimated Marginal Means Covariates appearing in the model are evaluated at the following values: =. Page

26 Estimated Marginal Means of. Estimated Marginal Means Battleship Cruiser Aircraft Carrier ShipType Covariates appearing in the model are evaluated at the following values: =. Page

27 Estimated Marginal Means of Estimated Marginal Means ShipType Battleship Cruiser Aircraft Carrier. 1 Covariates appearing in the model are evaluated at the following values: =. Non-estimable means are not plotted Regression Page

28 Output Created Comments Input Missing Value Handling Syntax Resources Data Active Dataset Filter Weight Split File Notes N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time Memory Required Additional Memory Required for Residual Plots 1-OCT-01 1:1: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> User-defined missing values are treated as missing. 11 Statistics are based on cases with no missing values for any variable used. REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI() BCOV R ANOVA /CRITERIA=PIN(.0) POUT(.) /NOORIGIN /DEPENDENT /METHOD=ENTER ShipType /METHOD=ENTER. bytes 0 bytes 00:00:0.0 00:00:0. Descriptive Statistics ShipType Mean Std. Deviation N Page

29 Correlations Pearson Correlation Sig. (1-tailed) N ShipType ShipType ShipType ShipType SurvivalPerce ntage Variables Entered/Removed a Variables Entered Variables Removed Method Model 1 SurvivalPerce ntage, ShipType,. Enter b a. Dependent Variable: b. All requested variables entered. Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1. a a. Predictors: (Constant),, ShipType, Page

30 ANOVA a Model 1 Regression Residual Total Sum of Squares df Mean Square F Sig b a. Dependent Variable: b. Predictors: (Constant),, ShipType, Coefficients a Model 1 (Constant) ShipType Unstandardized Coefficients Standardized Coefficients.0% Confidence Interval for B B Std. Error Beta t Sig. Lower Bound Upper Bound E a. Dependent Variable: Coefficient Correlations a Model 1 Correlations ShipType Covariances ShipType SurvivalPerce ntage ShipType E-00.0E-0 -.0E-00.0E-0.E-00.E-0 -.0E-00.E-0 1.0E-00 a. Dependent Variable: Summarize Page 0

31 Output Created Comments Input Missing Value Handling Syntax Resources Data Active Dataset Filter Weight Split File Notes N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time 1-OCT-01 1::11 C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> 11 For each dependent variable in a table, user-defined missing values for the dependent and all grouping variables are treated as missing. Cases used for each table have no missing values in any independent variable, and not all dependent variables have missing values. SUMMARIZE /TABLES= BY ShipType BY /FORMAT=NOLIST TOTAL /TITLE='Case Summaries' /MISSING=VARIABLE /CELLS=MEAN STDDEV COUNT. 00:00:0. 00:00:0. Case Processing Summary * ShipType * Cases Included Excluded Total N Percent N Percent N Percent % 0 0.0% % Page 1

32 Case Summaries ShipType Mean Std. Deviation N Battleship Total Cruiser Total Total Aircraft Carrier Page

33 Case Summaries ShipType Mean Std. Deviation N Total..1 1 Total Total.. 11 GLM BY ShipType /METHOD=SSTYPE() /INTERCEPT=INCLUDE /CRITERIA=ALPHA(.0) /DESIGN= ShipType *ShipType. General Linear Model Page

34 Output Created Comments Input Missing Value Handling Syntax Resources Data Active Dataset Filter Weight Split File Notes N of Rows in Working Data File Definition of Missing Cases Used Processor Time Elapsed Time 1-OCT-01 1:: C:\Users\Aaron\Documents\Player- Ships v.sav DataSet1 Battles > (FILTER) <none> <none> 11 User-defined missing values are treated as missing. Statistics are based on all cases with valid data for all variables in the model. GLM BY ShipType /METHOD=SSTYPE() /INTERCEPT=INCLUDE /CRITERIA=ALPHA(.0) /DESIGN= ShipType *ShipType. 00:00:0.1 00:00:0. Page

35 Between-Subjects Factors 1 ShipType 1 Value Label N Battleship 0 Cruiser Aircraft Carrier 1 Multivariate Tests a Effect Value F Hypothesis df Error df Sig. Intercept Pillai's Trace b Wilks' Lambda b Hotelling's Trace b Roy's Largest Root b Pillai's Trace Wilks' Lambda b Hotelling's Trace Roy's Largest Root.0.0 c ShipType Pillai's Trace Wilks' Lambda.. b Hotelling's Trace Roy's Largest Root.1. c * ShipType Pillai's Trace Wilks' Lambda.. b Hotelling's Trace Roy's Largest Root.0. c a. Design: Intercept + + ShipType + * ShipType b. Exact statistic Page

36 c. The statistic is an upper bound on F that yields a lower bound on the significance level. Tests of Between-Subjects Effects Source Corrected Model Intercept ShipType * ShipType Error Total Corrected Total Dependent Variable Type III Sum of Squares df Mean Square F Sig.. a b a. R Squared =.00 (Adjusted R Squared =.00) b. R Squared =.1 (Adjusted R Squared =.1) Page

Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55

Regression. Notes. Page 1. Output Created Comments 25-JAN :29:55 REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) BCOV R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT favorability /METHOD=ENTER Zcontemp ZAnxious6 zallcontact. Regression Notes Output

More information

General Linear Model

General Linear Model GLM V1 V2 V3 V4 V5 V11 V12 V13 V14 V15 /WSFACTOR=placeholders 2 Polynomial target 5 Polynomial /METHOD=SSTYPE(3) /EMMEANS=TABLES(OVERALL) /EMMEANS=TABLES(placeholders) COMPARE ADJ(SIDAK) /EMMEANS=TABLES(target)

More information

*************NO YOGA!!!!!!!************************************.

*************NO YOGA!!!!!!!************************************. *************NO YOGA!!!!!!!************************************. temporary. select if human gt 1 and Q_TotalDuration gt 239 and subjectnum ne 672 and subj ectnum ne 115 and subjectnum ne 104 and subjectnum

More information

General Linear Model. Notes Output Created Comments Input. 19-Dec :09:44

General Linear Model. Notes Output Created Comments Input. 19-Dec :09:44 GET ILE='G:\lare\Data\Accuracy_Mixed.sav'. DATASET NAME DataSet WINDOW=RONT. GLM Jigsaw Decision BY CMCTools /WSACTOR= Polynomial /METHOD=SSTYPE(3) /PLOT=PROILE(CMCTools*) /EMMEANS=TABLES(CMCTools) COMPARE

More information

Univariate Analysis of Variance

Univariate Analysis of Variance Univariate Analysis of Variance Output Created Comments Input Missing Value Handling Syntax Resources Notes Data Active Dataset Filter Weight Split File N of Rows in Working Data File Definition of Missing

More information

Advanced Quantitative Data Analysis

Advanced Quantitative Data Analysis Chapter 24 Advanced Quantitative Data Analysis Daniel Muijs Doing Regression Analysis in SPSS When we want to do regression analysis in SPSS, we have to go through the following steps: 1 As usual, we choose

More information

MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA:

MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA: MULTIVARIATE ANALYSIS OF VARIANCE MANOVA is an extension of the univariate ANOVA as it involves more than one Dependent Variable (DV). The following are assumptions for using MANOVA: 1. Cell sizes : o

More information

Descriptive Statistics

Descriptive Statistics *following creates z scores for the ydacl statedp traitdp and rads vars. *specifically adding the /SAVE subcommand to descriptives will create z. *scores for whatever variables are in the command. DESCRIPTIVES

More information

Stevens 2. Aufl. S Multivariate Tests c

Stevens 2. Aufl. S Multivariate Tests c Stevens 2. Aufl. S. 200 General Linear Model Between-Subjects Factors 1,00 2,00 3,00 N 11 11 11 Effect a. Exact statistic Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root Pillai's Trace

More information

EDF 7405 Advanced Quantitative Methods in Educational Research. Data are available on IQ of the child and seven potential predictors.

EDF 7405 Advanced Quantitative Methods in Educational Research. Data are available on IQ of the child and seven potential predictors. EDF 7405 Advanced Quantitative Methods in Educational Research Data are available on IQ of the child and seven potential predictors. Four are medical variables available at the birth of the child: Birthweight

More information

SPSS LAB FILE 1

SPSS LAB FILE  1 SPSS LAB FILE www.mcdtu.wordpress.com 1 www.mcdtu.wordpress.com 2 www.mcdtu.wordpress.com 3 OBJECTIVE 1: Transporation of Data Set to SPSS Editor INPUTS: Files: group1.xlsx, group1.txt PROCEDURE FOLLOWED:

More information

Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D.

Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D. Research Design - - Topic 19 Multiple regression: Applications 2009 R.C. Gardner, Ph.D. Curve Fitting Mediation analysis Moderation Analysis 1 Curve Fitting The investigation of non-linear functions using

More information

SAVE OUTFILE='C:\Documents and Settings\ddelgad1\Desktop\FactorAnalysis.sav' /COMPRESSED.

SAVE OUTFILE='C:\Documents and Settings\ddelgad1\Desktop\FactorAnalysis.sav' /COMPRESSED. SAVE OUTFILE='C:\Documents and Settings\ddelgad\Desktop\FactorAnalysis.sav' /COMPRESSED. SAVE TRANSLATE OUTFILE='C:\Documents and Settings\ddelgad\Desktop\FactorAnaly sis.xls' /TYPE=XLS /VERSION=8 /MAP

More information

Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology

Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology Data_Analysis.calm Three Factor Completely Randomized Design with One Continuous Factor: Using SPSS GLM UNIVARIATE R. C. Gardner Department of Psychology This article considers a three factor completely

More information

Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)

Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi) Regression (, Lingkungan kerja dan ) Descriptive Statistics Mean Std. Deviation N 3.87.333 32 3.47.672 32 3.78.585 32 s Pearson Sig. (-tailed) N Kemampuan Lingkungan Individu Kerja.000.432.49.432.000.3.49.3.000..000.000.000..000.000.000.

More information

N J SS W /df W N - 1

N J SS W /df W N - 1 One-Way ANOVA Source Table ANOVA MODEL: ij = µ* + α j + ε ij H 0 : µ = µ =... = µ j or H 0 : Σα j = 0 Source Sum of Squares df Mean Squares F J Between Groups nj( j * ) J - SS B /(J ) MS B /MS W = ( N

More information

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means

One-Way ANOVA Source Table J - 1 SS B / J - 1 MS B /MS W. Pairwise Post-Hoc Comparisons of Means One-Way ANOVA Source Table ANOVA MODEL: ij = µ* + α j + ε ij H 0 : µ 1 = µ =... = µ j or H 0 : Σα j = 0 Source Sum of Squares df Mean Squares F Between Groups n j ( j - * ) J - 1 SS B / J - 1 MS B /MS

More information

Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti

Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Prepared by: Prof. Dr Bahaman Abu Samah Department of Professional Development and Continuing Education Faculty of Educational Studies Universiti Putra Malaysia Serdang Use in experiment, quasi-experiment

More information

SPSS Guide For MMI 409

SPSS Guide For MMI 409 SPSS Guide For MMI 409 by John Wong March 2012 Preface Hopefully, this document can provide some guidance to MMI 409 students on how to use SPSS to solve many of the problems covered in the D Agostino

More information

Contrasts (in general)

Contrasts (in general) 10/1/015 6-09/749 Experimental Design for Behavioral and Social Sciences Contrasts (in general) Context: An ANOVA rejects the overall null hypothesis that all k means of some factor are not equal, i.e.,

More information

M A N O V A. Multivariate ANOVA. Data

M A N O V A. Multivariate ANOVA. Data M A N O V A Multivariate ANOVA V. Čekanavičius, G. Murauskas 1 Data k groups; Each respondent has m measurements; Observations are from the multivariate normal distribution. No outliers. Covariance matrices

More information

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES

4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES 4:3 LEC - PLANNED COMPARISONS AND REGRESSION ANALYSES FOR SINGLE FACTOR BETWEEN-S DESIGNS Planned or A Priori Comparisons We previously showed various ways to test all possible pairwise comparisons for

More information

Logbook Authors: Rens van de Schoot, Joris J. Broere, Koen H. Perryck, Mariëlle Zondervan - Zwijnenburg, Nancy E.E. van Loey

Logbook Authors: Rens van de Schoot, Joris J. Broere, Koen H. Perryck, Mariëlle Zondervan - Zwijnenburg, Nancy E.E. van Loey Logbook Authors: Rens van de Schoot, Joris J. Broere, Koen H. Perryck, Mariëlle Zondervan - Zwijnenburg, Nancy E.E. van Loey Contents Data... 2 Empirical data analysis... 3 SPSS analyses repeated measure...

More information

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total 45 Lampiran 3 : Uji Validitas dan Reliabilitas Reliability Case Processing Summary N % Valid 75 00.0 Cases Excluded a 0.0 Total 75 00.0 a. Listwise deletion based on all variables in the procedure. Reliability

More information

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA:

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA: 1 Neuendorf MANOVA /MANCOVA Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y1 Y2 Y3 Y4 Like ANOVA/ANCOVA: 1. Assumes equal variance (equal covariance matrices) across cells (groups defined by

More information

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA:

Neuendorf MANOVA /MANCOVA. Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y4. Like ANOVA/ANCOVA: 1 Neuendorf MANOVA /MANCOVA Model: X1 (Factor A) X2 (Factor B) X1 x X2 (Interaction) Y1 Y2 Y3 Y4 Like ANOVA/ANCOVA: 1. Assumes equal variance (equal covariance matrices) across cells (groups defined by

More information

GLM Repeated-measures designs: One within-subjects factor

GLM Repeated-measures designs: One within-subjects factor GLM Repeated-measures designs: One within-subjects factor Reading: SPSS dvanced Models 9.0: 2. Repeated Measures Homework: Sums of Squares for Within-Subject Effects Download: glm_withn1.sav (Download

More information

T. Mark Beasley One-Way Repeated Measures ANOVA handout

T. Mark Beasley One-Way Repeated Measures ANOVA handout T. Mark Beasley One-Way Repeated Measures ANOVA handout Profile Analysis Example In the One-Way Repeated Measures ANOVA, two factors represent separate sources of variance. Their interaction presents an

More information

Simple Linear Regression: One Quantitative IV

Simple Linear Regression: One Quantitative IV Simple Linear Regression: One Quantitative IV Linear regression is frequently used to explain variation observed in a dependent variable (DV) with theoretically linked independent variables (IV). For example,

More information

Multivariate Tests. Mauchly's Test of Sphericity

Multivariate Tests. Mauchly's Test of Sphericity General Model Within-Sujects Factors Dependent Variale IDLS IDLF IDHS IDHF IDHCLS IDHCLF Descriptive Statistics IDLS IDLF IDHS IDHF IDHCLS IDHCLF Mean Std. Deviation N.0.70.0.0..8..88.8...97 Multivariate

More information

ANOVA Longitudinal Models for the Practice Effects Data: via GLM

ANOVA Longitudinal Models for the Practice Effects Data: via GLM Psyc 943 Lecture 25 page 1 ANOVA Longitudinal Models for the Practice Effects Data: via GLM Model 1. Saturated Means Model for Session, E-only Variances Model (BP) Variances Model: NO correlation, EQUAL

More information

Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology

Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology Psychology 308c Dale Berger Interactions and Centering in Regression: MRC09 Salaries for graduate faculty in psychology This example illustrates modeling an interaction with centering and transformations.

More information

Analysis of Covariance (ANCOVA) Lecture Notes

Analysis of Covariance (ANCOVA) Lecture Notes 1 Analysis of Covariance (ANCOVA) Lecture Notes Overview: In experimental methods, a central tenet of establishing significant relationships has to do with the notion of random assignment. Random assignment

More information

EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS

EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS EDF 7405 Advanced Quantitative Methods in Educational Research MULTR.SAS The data used in this example describe teacher and student behavior in 8 classrooms. The variables are: Y percentage of interventions

More information

SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients

SPSS Output. ANOVA a b Residual Coefficients a Standardized Coefficients SPSS Output Homework 1-1e ANOVA a Sum of Squares df Mean Square F Sig. 1 Regression 351.056 1 351.056 11.295.002 b Residual 932.412 30 31.080 Total 1283.469 31 a. Dependent Variable: Sexual Harassment

More information

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis ESP 178 Applied Research Methods 2/23: Quantitative Analysis Data Preparation Data coding create codebook that defines each variable, its response scale, how it was coded Data entry for mail surveys and

More information

Entering and recoding variables

Entering and recoding variables Entering and recoding variables To enter: You create a New data file Define the variables on Variable View Enter the values on Data View To create the dichotomies: Transform -> Recode into Different Variable

More information

Neuendorf MANOVA /MANCOVA. Model: MAIN EFFECTS: X1 (Factor A) X2 (Factor B) INTERACTIONS : X1 x X2 (A x B Interaction) Y4. Like ANOVA/ANCOVA:

Neuendorf MANOVA /MANCOVA. Model: MAIN EFFECTS: X1 (Factor A) X2 (Factor B) INTERACTIONS : X1 x X2 (A x B Interaction) Y4. Like ANOVA/ANCOVA: 1 Neuendorf MANOVA /MANCOVA Model: MAIN EFFECTS: X1 (Factor A) X2 (Factor B) Y1 Y2 INTERACTIONS : Y3 X1 x X2 (A x B Interaction) Y4 Like ANOVA/ANCOVA: 1. Assumes equal variance (equal covariance matrices)

More information

MANOVA MANOVA,$/,,# ANOVA ##$%'*!# 1. $!;' *$,$!;' (''

MANOVA MANOVA,$/,,# ANOVA ##$%'*!# 1. $!;' *$,$!;' ('' 14 3! "#!$%# $# $&'('$)!! (Analysis of Variance : ANOVA) *& & "#!# +, ANOVA -& $ $ (+,$ ''$) *$#'$)!!#! (Multivariate Analysis of Variance : MANOVA).*& ANOVA *+,'$)$/*! $#/#-, $(,!0'%1)!', #($!#$ # *&,

More information

Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont.

Regression: Main Ideas Setting: Quantitative outcome with a quantitative explanatory variable. Example, cont. TCELL 9/4/205 36-309/749 Experimental Design for Behavioral and Social Sciences Simple Regression Example Male black wheatear birds carry stones to the nest as a form of sexual display. Soler et al. wanted

More information

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed) Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "An Analysis of 2013 NAPLEX, P4-Comp. Exams and P3 courses The following analysis illustrates relationships

More information

Repeated-Measures ANOVA in SPSS Correct data formatting for a repeated-measures ANOVA in SPSS involves having a single line of data for each

Repeated-Measures ANOVA in SPSS Correct data formatting for a repeated-measures ANOVA in SPSS involves having a single line of data for each Repeated-Measures ANOVA in SPSS Correct data formatting for a repeated-measures ANOVA in SPSS involves having a single line of data for each participant, with the repeated measures entered as separate

More information

Dependent Variable Q83: Attended meetings of your town or city council (0=no, 1=yes)

Dependent Variable Q83: Attended meetings of your town or city council (0=no, 1=yes) Logistic Regression Kristi Andrasik COM 731 Spring 2017. MODEL all data drawn from the 2006 National Community Survey (class data set) BLOCK 1 (Stepwise) Lifestyle Values Q7: Value work Q8: Value friends

More information

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know:

Multiple Regression. More Hypothesis Testing. More Hypothesis Testing The big question: What we really want to know: What we actually know: We know: Multiple Regression Ψ320 Ainsworth More Hypothesis Testing What we really want to know: Is the relationship in the population we have selected between X & Y strong enough that we can use the relationship

More information

Sociology 593 Exam 2 March 28, 2002

Sociology 593 Exam 2 March 28, 2002 Sociology 59 Exam March 8, 00 I. True-False. (0 points) Indicate whether the following statements are true or false. If false, briefly explain why.. A variable is called CATHOLIC. This probably means that

More information

Interactions between Binary & Quantitative Predictors

Interactions between Binary & Quantitative Predictors Interactions between Binary & Quantitative Predictors The purpose of the study was to examine the possible joint effects of the difficulty of the practice task and the amount of practice, upon the performance

More information

36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression

36-309/749 Experimental Design for Behavioral and Social Sciences. Sep. 22, 2015 Lecture 4: Linear Regression 36-309/749 Experimental Design for Behavioral and Social Sciences Sep. 22, 2015 Lecture 4: Linear Regression TCELL Simple Regression Example Male black wheatear birds carry stones to the nest as a form

More information

Mediation question: Does executive functioning mediate the relation between shyness and vocabulary? Plot data, descriptives, etc. Check for outliers

Mediation question: Does executive functioning mediate the relation between shyness and vocabulary? Plot data, descriptives, etc. Check for outliers Plot data, descriptives, etc. Check for outliers A. Nayena Blankson, Ph.D. Spelman College University of Southern California GC3 Lecture Series September 6, 2013 Treat missing i data Listwise Pairwise

More information

ANOVA in SPSS. Hugo Quené. opleiding Taalwetenschap Universiteit Utrecht Trans 10, 3512 JK Utrecht.

ANOVA in SPSS. Hugo Quené. opleiding Taalwetenschap Universiteit Utrecht Trans 10, 3512 JK Utrecht. ANOVA in SPSS Hugo Quené hugo.quene@let.uu.nl opleiding Taalwetenschap Universiteit Utrecht Trans 10, 3512 JK Utrecht 7 Oct 2005 1 introduction In this example I ll use fictitious data, taken from http://www.ruf.rice.edu/~mickey/psyc339/notes/rmanova.html.

More information

Appendix 1. The result of normality with Kolmogorov-Smirnov method and descriptive

Appendix 1. The result of normality with Kolmogorov-Smirnov method and descriptive Appendix 1. The result of normality with Kolmogorov-Smirnov method and descriptive Kolmogorov-Smirnov(a) Tests of Normality Shapiro-Wilk Statistic df Sig. Statistic df Sig. VISKO.83 7.(*).969 7.76 AW.3

More information

Research Methodology: Tools

Research Methodology: Tools MSc Business Administration Research Methodology: Tools Applied Data Analysis (with SPSS) Lecture 09: Introduction to Analysis of Variance (ANOVA) April 2014 Prof. Dr. Jürg Schwarz Lic. phil. Heidi Bruderer

More information

Independent Samples ANOVA

Independent Samples ANOVA Independent Samples ANOVA In this example students were randomly assigned to one of three mnemonics (techniques for improving memory) rehearsal (the control group; simply repeat the words), visual imagery

More information

Chapter 7, continued: MANOVA

Chapter 7, continued: MANOVA Chapter 7, continued: MANOVA The Multivariate Analysis of Variance (MANOVA) technique extends Hotelling T 2 test that compares two mean vectors to the setting in which there are m 2 groups. We wish to

More information

Repeated Measures Part 2: Cartoon data

Repeated Measures Part 2: Cartoon data Repeated Measures Part 2: Cartoon data /*********************** cartoonglm.sas ******************/ options linesize=79 noovp formdlim='_'; title 'Cartoon Data: STA442/1008 F 2005'; proc format; /* value

More information

2 Prediction and Analysis of Variance

2 Prediction and Analysis of Variance 2 Prediction and Analysis of Variance Reading: Chapters and 2 of Kennedy A Guide to Econometrics Achen, Christopher H. Interpreting and Using Regression (London: Sage, 982). Chapter 4 of Andy Field, Discovering

More information

Multiple Comparisons

Multiple Comparisons Multiple Comparisons Error Rates, A Priori Tests, and Post-Hoc Tests Multiple Comparisons: A Rationale Multiple comparison tests function to tease apart differences between the groups within our IV when

More information

Sociology 593 Exam 1 February 17, 1995

Sociology 593 Exam 1 February 17, 1995 Sociology 593 Exam 1 February 17, 1995 I. True-False. (25 points) Indicate whether the following statements are true or false. If false, briefly explain why. 1. A researcher regressed Y on. When he plotted

More information

Simple Linear Regression: One Qualitative IV

Simple Linear Regression: One Qualitative IV Simple Linear Regression: One Qualitative IV Simple linear regression with one qualitative IV variable is essentially identical to linear regression with quantitative variables. The primary difference

More information

Repeated Measures ANOVA Multivariate ANOVA and Their Relationship to Linear Mixed Models

Repeated Measures ANOVA Multivariate ANOVA and Their Relationship to Linear Mixed Models Repeated Measures ANOVA Multivariate ANOVA and Their Relationship to Linear Mixed Models EPSY 905: Multivariate Analysis Spring 2016 Lecture #12 April 20, 2016 EPSY 905: RM ANOVA, MANOVA, and Mixed Models

More information

Sociology 593 Exam 2 Answer Key March 28, 2002

Sociology 593 Exam 2 Answer Key March 28, 2002 Sociology 59 Exam Answer Key March 8, 00 I. True-False. (0 points) Indicate whether the following statements are true or false. If false, briefly explain why.. A variable is called CATHOLIC. This probably

More information

Introduction to Within-Person Analysis and RM ANOVA

Introduction to Within-Person Analysis and RM ANOVA Introduction to Within-Person Analysis and RM ANOVA Today s Class: From between-person to within-person ANOVAs for longitudinal data Variance model comparisons using 2 LL CLP 944: Lecture 3 1 The Two Sides

More information

Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur

Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 29 Multivariate Linear Regression- Model

More information

CHAPTER6 LINEAR REGRESSION

CHAPTER6 LINEAR REGRESSION CHAPTER6 LINEAR REGRESSION YI-TING HWANG DEPARTMENT OF STATISTICS NATIONAL TAIPEI UNIVERSITY EXAMPLE 1 Suppose that a real-estate developer is interested in determining the relationship between family

More information

Investigating Models with Two or Three Categories

Investigating Models with Two or Three Categories Ronald H. Heck and Lynn N. Tabata 1 Investigating Models with Two or Three Categories For the past few weeks we have been working with discriminant analysis. Let s now see what the same sort of model might

More information

Multivariate analysis of variance and covariance

Multivariate analysis of variance and covariance Introduction Multivariate analysis of variance and covariance Univariate ANOVA: have observations from several groups, numerical dependent variable. Ask whether dependent variable has same mean for each

More information

TOPIC 9 SIMPLE REGRESSION & CORRELATION

TOPIC 9 SIMPLE REGRESSION & CORRELATION TOPIC 9 SIMPLE REGRESSION & CORRELATION Basic Linear Relationships Mathematical representation: Y = a + bx X is the independent variable [the variable whose value we can choose, or the input variable].

More information

Chapter Goals. To understand the methods for displaying and describing relationship among variables. Formulate Theories.

Chapter Goals. To understand the methods for displaying and describing relationship among variables. Formulate Theories. Chapter Goals To understand the methods for displaying and describing relationship among variables. Formulate Theories Interpret Results/Make Decisions Collect Data Summarize Results Chapter 7: Is There

More information

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis

STAT 3900/4950 MIDTERM TWO Name: Spring, 2015 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis STAT 3900/4950 MIDTERM TWO Name: Spring, 205 (print: first last ) Covered topics: Two-way ANOVA, ANCOVA, SLR, MLR and correlation analysis Instructions: You may use your books, notes, and SPSS/SAS. NO

More information

Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons

Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons Self-Assessment Weeks 6 and 7: Multiple Regression with a Qualitative Predictor; Multiple Comparisons 1. Suppose we wish to assess the impact of five treatments on an outcome Y. How would these five treatments

More information

Using SPSS for One Way Analysis of Variance

Using SPSS for One Way Analysis of Variance Using SPSS for One Way Analysis of Variance This tutorial will show you how to use SPSS version 12 to perform a one-way, between- subjects analysis of variance and related post-hoc tests. This tutorial

More information

4.1 Computing section Example: Bivariate measurements on plants Post hoc analysis... 7

4.1 Computing section Example: Bivariate measurements on plants Post hoc analysis... 7 Master of Applied Statistics ST116: Chemometrics and Multivariate Statistical data Analysis Per Bruun Brockhoff Module 4: Computing 4.1 Computing section.................................. 1 4.1.1 Example:

More information

1 A Review of Correlation and Regression

1 A Review of Correlation and Regression 1 A Review of Correlation and Regression SW, Chapter 12 Suppose we select n = 10 persons from the population of college seniors who plan to take the MCAT exam. Each takes the test, is coached, and then

More information

Multiple OLS Regression

Multiple OLS Regression Multiple OLS Regression Ronet Bachman, Ph.D. Presented by Justice Research and Statistics Association 12/8/2016 Justice Research and Statistics Association 720 7 th Street, NW, Third Floor Washington,

More information

Data Analyses in Multivariate Regression Chii-Dean Joey Lin, SDSU, San Diego, CA

Data Analyses in Multivariate Regression Chii-Dean Joey Lin, SDSU, San Diego, CA Data Analyses in Multivariate Regression Chii-Dean Joey Lin, SDSU, San Diego, CA ABSTRACT Regression analysis is one of the most used statistical methodologies. It can be used to describe or predict causal

More information

PSY 216. Assignment 12 Answers. Explain why the F-ratio is expected to be near 1.00 when the null hypothesis is true.

PSY 216. Assignment 12 Answers. Explain why the F-ratio is expected to be near 1.00 when the null hypothesis is true. PSY 21 Assignment 12 Answers 1. Problem 1 from the text Explain why the F-ratio is expected to be near 1.00 when the null hypothesis is true. When H0 is true, the treatment had no systematic effect. In

More information

Workshop 7.4a: Single factor ANOVA

Workshop 7.4a: Single factor ANOVA -1- Workshop 7.4a: Single factor ANOVA Murray Logan November 23, 2016 Table of contents 1 Revision 1 2 Anova Parameterization 2 3 Partitioning of variance (ANOVA) 10 4 Worked Examples 13 1. Revision 1.1.

More information

DESIGNING EXPERIMENTS AND ANALYZING DATA A Model Comparison Perspective

DESIGNING EXPERIMENTS AND ANALYZING DATA A Model Comparison Perspective DESIGNING EXPERIMENTS AND ANALYZING DATA A Model Comparison Perspective Second Edition Scott E. Maxwell Uniuersity of Notre Dame Harold D. Delaney Uniuersity of New Mexico J,t{,.?; LAWRENCE ERLBAUM ASSOCIATES,

More information

MATH ASSIGNMENT 2: SOLUTIONS

MATH ASSIGNMENT 2: SOLUTIONS MATH 204 - ASSIGNMENT 2: SOLUTIONS (a) Fitting the simple linear regression model to each of the variables in turn yields the following results: we look at t-tests for the individual coefficients, and

More information

Multiple linear regression S6

Multiple linear regression S6 Basic medical statistics for clinical and experimental research Multiple linear regression S6 Katarzyna Jóźwiak k.jozwiak@nki.nl November 15, 2017 1/42 Introduction Two main motivations for doing multiple

More information

ANCOVA. Psy 420 Andrew Ainsworth

ANCOVA. Psy 420 Andrew Ainsworth ANCOVA Psy 420 Andrew Ainsworth What is ANCOVA? Analysis of covariance an extension of ANOVA in which main effects and interactions are assessed on DV scores after the DV has been adjusted for by the DV

More information

More Accurately Analyze Complex Relationships

More Accurately Analyze Complex Relationships SPSS Advanced Statistics 17.0 Specifications More Accurately Analyze Complex Relationships Make your analysis more accurate and reach more dependable conclusions with statistics designed to fit the inherent

More information

Research Design - - Topic 17 Multiple Regression & Multiple Correlation: Two Predictors 2009 R.C. Gardner, Ph.D.

Research Design - - Topic 17 Multiple Regression & Multiple Correlation: Two Predictors 2009 R.C. Gardner, Ph.D. Reseach Design - - Topic 7 Multiple Regession & Multiple Coelation: Two Pedictos 009 R.C. Gadne, Ph.D. Geneal Rationale and Basic Aithmetic fo two pedictos Patial and semipatial coelation Regession coefficients

More information

Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS

Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS Gregory Carey, 1998 Regression & Path Analysis - 1 MULTIPLE REGRESSION AND PATH ANALYSIS Introduction Path analysis and multiple regression go hand in hand (almost). Also, it is easier to learn about multivariate

More information

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages: Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the

More information

sphericity, 5-29, 5-32 residuals, 7-1 spread and level, 2-17 t test, 1-13 transformations, 2-15 violations, 1-19

sphericity, 5-29, 5-32 residuals, 7-1 spread and level, 2-17 t test, 1-13 transformations, 2-15 violations, 1-19 additive tree structure, 10-28 ADDTREE, 10-51, 10-53 EXTREE, 10-31 four point condition, 10-29 ADDTREE, 10-28, 10-51, 10-53 adjusted R 2, 8-7 ALSCAL, 10-49 ANCOVA, 9-1 assumptions, 9-5 example, 9-7 MANOVA

More information

Sociology Research Statistics I Final Exam Answer Key December 15, 1993

Sociology Research Statistics I Final Exam Answer Key December 15, 1993 Sociology 592 - Research Statistics I Final Exam Answer Key December 15, 1993 Where appropriate, show your work - partial credit may be given. (On the other hand, don't waste a lot of time on excess verbiage.)

More information

Multivariate Regression (Chapter 10)

Multivariate Regression (Chapter 10) Multivariate Regression (Chapter 10) This week we ll cover multivariate regression and maybe a bit of canonical correlation. Today we ll mostly review univariate multivariate regression. With multivariate

More information

Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors

Chapter 4 Regression with Categorical Predictor Variables Page 1. Overview of regression with categorical predictors Chapter 4 Regression with Categorical Predictor Variables Page. Overview of regression with categorical predictors 4-. Dummy coding 4-3 4-5 A. Karpinski Regression with Categorical Predictor Variables.

More information

GLM Repeated Measures

GLM Repeated Measures GLM Repeated Measures Notation The GLM (general linear model) procedure provides analysis of variance when the same measurement or measurements are made several times on each subject or case (repeated

More information

BIOMETRICS INFORMATION

BIOMETRICS INFORMATION BIOMETRICS INFORMATION Index of Pamphlet Topics (for pamphlets #1 to #60) as of December, 2000 Adjusted R-square ANCOVA: Analysis of Covariance 13: ANCOVA: Analysis of Covariance ANOVA: Analysis of Variance

More information

In Class Review Exercises Vartanian: SW 540

In Class Review Exercises Vartanian: SW 540 In Class Review Exercises Vartanian: SW 540 1. Given the following output from an OLS model looking at income, what is the slope and intercept for those who are black and those who are not black? b SE

More information

Analyses of Variance. Block 2b

Analyses of Variance. Block 2b Analyses of Variance Block 2b Types of analyses 1 way ANOVA For more than 2 levels of a factor between subjects ANCOVA For continuous co-varying factor, between subjects ANOVA for factorial design Multiple

More information

Topic 1. Definitions

Topic 1. Definitions S Topic. Definitions. Scalar A scalar is a number. 2. Vector A vector is a column of numbers. 3. Linear combination A scalar times a vector plus a scalar times a vector, plus a scalar times a vector...

More information

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum

T-test: means of Spock's judge versus all other judges 1 12:10 Wednesday, January 5, judge1 N Mean Std Dev Std Err Minimum Maximum T-test: means of Spock's judge versus all other judges 1 The TTEST Procedure Variable: pcwomen judge1 N Mean Std Dev Std Err Minimum Maximum OTHER 37 29.4919 7.4308 1.2216 16.5000 48.9000 SPOCKS 9 14.6222

More information

Paper: ST-161. Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop UMBC, Baltimore, MD

Paper: ST-161. Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop UMBC, Baltimore, MD Paper: ST-161 Techniques for Evidence-Based Decision Making Using SAS Ian Stockwell, The Hilltop Institute @ UMBC, Baltimore, MD ABSTRACT SAS has many tools that can be used for data analysis. From Freqs

More information

McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination

McGill University. Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II. Final Examination McGill University Faculty of Science MATH 204 PRINCIPLES OF STATISTICS II Final Examination Date: 20th April 2009 Time: 9am-2pm Examiner: Dr David A Stephens Associate Examiner: Dr Russell Steele Please

More information

Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons

Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons Self-Assessment Weeks 8: Multiple Regression with Qualitative Predictors; Multiple Comparisons 1. Suppose we wish to assess the impact of five treatments while blocking for study participant race (Black,

More information

ANALYSIS OF VARIANCE OF BALANCED DAIRY SCIENCE DATA USING SAS

ANALYSIS OF VARIANCE OF BALANCED DAIRY SCIENCE DATA USING SAS ANALYSIS OF VARIANCE OF BALANCED DAIRY SCIENCE DATA USING SAS Ravinder Malhotra and Vipul Sharma National Dairy Research Institute, Karnal-132001 The most common use of statistics in dairy science is testing

More information

Multicollinearity Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 13, 2015

Multicollinearity Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 13, 2015 Multicollinearity Richard Williams, University of Notre Dame, https://www3.nd.edu/~rwilliam/ Last revised January 13, 2015 Stata Example (See appendices for full example).. use http://www.nd.edu/~rwilliam/stats2/statafiles/multicoll.dta,

More information

Using the GLM Procedure in SPSS

Using the GLM Procedure in SPSS Using the GLM Procedure in SPSS Alan Taylor, Department of Psychology Macquarie University 2002-2011 Macquarie University 2002-2011 Contents i Introduction 1 1. General 3 1.1 Factors and Covariates 3

More information