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Reliability and Validity testing using SAS/STAT Andrea Roofe Ph.D. Candidate, Florida International University SAS ®  Stud...
Reliability <ul><li>Instrument consistently measures what it should measure. </li></ul>10 lbs of feathers 10 lbs of steel =
Validity <ul><li>Accuracy </li></ul><ul><li>Reliability is a prerequisite  for validity </li></ul><ul><li>Predictive abili...
Research Questions <ul><li>Are there differences between virtual and traditional teams? </li></ul><ul><li>If yes, what are...
The Process  <ul><li>Instrument development </li></ul><ul><li>Discriminant Analysis </li></ul><ul><li>Logistic Regression ...
Instrument Development <ul><li>Scale development procedures (DeVellis,2003) </li></ul><ul><li>34 items </li></ul><ul><ul><...
Descriptives <ul><li>PROC UNIVARIATE </li></ul><ul><li>PROC MEANS </li></ul><ul><li>PROC FREQ </li></ul><ul><li>PROC UNIVA...
<ul><li>OPTIONS LINESIZE=150 PAGESIZE=60 PAGENO=1;  </li></ul><ul><li>TITLE1 'DATA ANALYSIS-VALIDATION OF SURVEY INSTRUMEN...
Reliability Analysis Code <ul><li>Performed on both constructs  </li></ul><ul><li>NOMISS Missing values ignored   </li></u...
<ul><li>PROC CORR DATA = ‘ name of file’ ; </li></ul><ul><li>ALPHA NOMISS;  </li></ul><ul><li>VAR ‘list of variables’;  </...
Reliability Analysis Results
Factor Analysis Factor 1 Factor 2 Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7
Factor Analysis options <ul><li>PROC FACTOR  </li></ul><ul><li>METHOD=PRINCIPAL </li></ul><ul><li>SCREE </li></ul><ul><li>...
Factor Analysis <ul><li>Principal Components Method </li></ul><ul><li>Scree Plot </li></ul><ul><li>Maximum Likelihood Meth...
<ul><li>PROC FACTOR DATA= ‘ name of file ’ METHOD=principal SCREE ROTATE=varimax S C;  </li></ul><ul><li>VAR ‘ list of var...
Factor Analysis Results <ul><li>Principal Components Method= 9 factors </li></ul><ul><li>Maximum Likelihood Method=11 fact...
 
Logistic Regression <ul><li>DV is categorical </li></ul><ul><li>Normality assumption violated.  </li></ul><ul><li>Press & ...
Logistic Analysis Code <ul><li>Dummy variables using IF…ELSE </li></ul><ul><li>PROC LOGISTIC </li></ul><ul><li>MODEL (opti...
<ul><li>DATA Combinedaomfiuaib;  </li></ul><ul><li>SET Combinedaomfiuaib;  </li></ul><ul><li>IF VIRTRAD=1 THEN NEWVIRTRAD=...
 
Discriminant Analysis <ul><li>Discriminates between groups </li></ul><ul><li>Predicts group membership </li></ul><ul><li>N...
Discriminant Analysis Code <ul><li>PROC DISCRIM </li></ul><ul><ul><li>PRIORS </li></ul></ul><ul><ul><li>CLASS </li></ul></...
Discriminant Analysis-Selection of Significant Variables   <ul><li>Variables that differ between groups </li></ul><ul><li>...
<ul><li>PROC STEPDISC DATA=‘ name of file’  METHOD=STEPWISE SLE=0.4 SLS=0.05;  </li></ul><ul><li>*SLE(S)=SIGNIFICANCE LEVE...
Study Conclusions <ul><li>Number of countries spanned by the team </li></ul><ul><li>Industry/Type of organization </li></u...
Methodology Summary <ul><li>2.  Reliability </li></ul><ul><li>Survey Development </li></ul>4.  Validity Task Step SAS Tool...
Thank you
 
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SAS Global Forum 2008 presentation

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This is my SAS Student Ambassador presentation to the 2008 SAS Global Forum.

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SAS Global Forum 2008 presentation

  1. 1. Reliability and Validity testing using SAS/STAT Andrea Roofe Ph.D. Candidate, Florida International University SAS ® Student Ambassador 2008
  2. 2. Reliability <ul><li>Instrument consistently measures what it should measure. </li></ul>10 lbs of feathers 10 lbs of steel =
  3. 3. Validity <ul><li>Accuracy </li></ul><ul><li>Reliability is a prerequisite for validity </li></ul><ul><li>Predictive ability </li></ul>
  4. 4. Research Questions <ul><li>Are there differences between virtual and traditional teams? </li></ul><ul><li>If yes, what are they? </li></ul><ul><li>Implications for team management and </li></ul><ul><li>leadership </li></ul>
  5. 5. The Process <ul><li>Instrument development </li></ul><ul><li>Discriminant Analysis </li></ul><ul><li>Logistic Regression </li></ul><ul><li>Factor Analysis </li></ul><ul><li>Reliability Testing </li></ul><ul><li>Descriptive Analysis </li></ul>
  6. 6. Instrument Development <ul><li>Scale development procedures (DeVellis,2003) </li></ul><ul><li>34 items </li></ul><ul><ul><li>member perception of leader behaviors </li></ul></ul><ul><ul><li>member perception of group dynamics </li></ul></ul><ul><li>Pilot survey using different groups </li></ul>
  7. 7. Descriptives <ul><li>PROC UNIVARIATE </li></ul><ul><li>PROC MEANS </li></ul><ul><li>PROC FREQ </li></ul><ul><li>PROC UNIVARIATE PLOT NORMAL (not done for categorical variables) </li></ul>
  8. 8. <ul><li>OPTIONS LINESIZE=150 PAGESIZE=60 PAGENO=1; </li></ul><ul><li>TITLE1 'DATA ANALYSIS-VALIDATION OF SURVEY INSTRUMENT'; </li></ul><ul><li>DATA COMBINEDAOM; </li></ul><ul><li>INFILE 'insert address of your input file'; </li></ul><ul><li>INPUT list of variables ; </li></ul><ul><li>IF VIRTRAD=1 THEN TEAM='VIRTUAL'; </li></ul><ul><li>ELSE TEAM='TRADITIONAL'; </li></ul><ul><li>IF TYPETEAM=1 THEN TYPE='ACADEMIC'; </li></ul><ul><li>IF TYPETEAM=2 THEN TYPE='STUDENT'; </li></ul><ul><li>IF TYPETEAM=3 THEN TYPE='MANAGEMENT' ; </li></ul><ul><li>IF TYPETEAM=4 THEN TYPE='TECHNICAL'; </li></ul><ul><li>IF TYPETEAM=5 THEN TYPE='OTHER'; </li></ul><ul><li>*COMPUTING MEANS; </li></ul><ul><li>PROC MEANS DATA = COMBINEDAOM; </li></ul><ul><li>VAR Q1--GROUP; </li></ul><ul><li>RUN; </li></ul><ul><li>*COMPUTING BASIC STATISTICAL MEASURES-MODE, STD DEVIATION etc; </li></ul><ul><li>ODS; </li></ul><ul><li>PROC UNIVARIATE DATA=COMBINEDAOM; </li></ul><ul><li>VAR Q1--GROUP; </li></ul><ul><li>RUN; </li></ul><ul><li>PROC FREQ DATA=COMBINEDAOM; </li></ul><ul><li>RUN; </li></ul>
  9. 9. Reliability Analysis Code <ul><li>Performed on both constructs </li></ul><ul><li>NOMISS Missing values ignored </li></ul><ul><li>ALPHA option in PROC CORR </li></ul>
  10. 10. <ul><li>PROC CORR DATA = ‘ name of file’ ; </li></ul><ul><li>ALPHA NOMISS; </li></ul><ul><li>VAR ‘list of variables’; </li></ul><ul><li>RUN; </li></ul><ul><li>PROC CORR DATA = ‘ name of file’ ALPHA NOMISS; </li></ul><ul><li>VAR ‘list of variables’; ; </li></ul><ul><li>RUN; </li></ul><ul><li>PROC CORR DATA = ‘ name of file’ ALPHA NOMISS; </li></ul><ul><li>VAR ‘list of variables’; </li></ul><ul><li>RUN; </li></ul>
  11. 11. Reliability Analysis Results
  12. 12. Factor Analysis Factor 1 Factor 2 Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7
  13. 13. Factor Analysis options <ul><li>PROC FACTOR </li></ul><ul><li>METHOD=PRINCIPAL </li></ul><ul><li>SCREE </li></ul><ul><li>METHOD=ML </li></ul><ul><li>HEYWOOD </li></ul><ul><li>ROTATE=VARIMAX </li></ul>
  14. 14. Factor Analysis <ul><li>Principal Components Method </li></ul><ul><li>Scree Plot </li></ul><ul><li>Maximum Likelihood Method </li></ul>
  15. 15. <ul><li>PROC FACTOR DATA= ‘ name of file ’ METHOD=principal SCREE ROTATE=varimax S C; </li></ul><ul><li>VAR ‘ list of variables ’; </li></ul><ul><li>RUN; </li></ul><ul><li>PROC FACTOR DATA= ‘ name of file ’ METHOD=ML HEYWOOD ROTATE=varimax S C; </li></ul><ul><li>VAR ‘ list of variables ’; </li></ul><ul><li>RUN; </li></ul>
  16. 16. Factor Analysis Results <ul><li>Principal Components Method= 9 factors </li></ul><ul><li>Maximum Likelihood Method=11 factors </li></ul><ul><li>Scree plot=4 factors </li></ul><ul><li>4 factors = approx. 50% of variance </li></ul>
  17. 18. Logistic Regression <ul><li>DV is categorical </li></ul><ul><li>Normality assumption violated. </li></ul><ul><li>Press & Wilson (1978) </li></ul>
  18. 19. Logistic Analysis Code <ul><li>Dummy variables using IF…ELSE </li></ul><ul><li>PROC LOGISTIC </li></ul><ul><li>MODEL (options STEPWISE, BACKWARD) </li></ul>
  19. 20. <ul><li>DATA Combinedaomfiuaib; </li></ul><ul><li>SET Combinedaomfiuaib; </li></ul><ul><li>IF VIRTRAD=1 THEN NEWVIRTRAD=1; ELSE NEWVIRTRAD=0; </li></ul><ul><li>PROC LOGISTIC DATA=Combinedaomfiuaib; </li></ul><ul><li>MODEL NEWVIRTRAD = country size lengthexist explifespan group survey teambuild comfrtgroup satwork cohesiv </li></ul><ul><li>/BACKWARD; </li></ul><ul><li>OUTPUT OUT=OUT1 PREDICTED=POSTERIOR; </li></ul><ul><li>TITLE2 'LOGISTIC REGRESSION ANALYSIS OF VIRTUAL TEAMS DATA'; </li></ul><ul><li>RUN; </li></ul>
  20. 22. Discriminant Analysis <ul><li>Discriminates between groups </li></ul><ul><li>Predicts group membership </li></ul><ul><li>NPAR option (non parametric test) </li></ul>
  21. 23. Discriminant Analysis Code <ul><li>PROC DISCRIM </li></ul><ul><ul><li>PRIORS </li></ul></ul><ul><ul><li>CLASS </li></ul></ul><ul><ul><li>CROSSVALIDATE </li></ul></ul><ul><li>LIST </li></ul><ul><li>SHORT </li></ul><ul><li>POOL = YES </li></ul><ul><li>METHOD=NORMAL or METHOD=NPAR </li></ul><ul><li>OPTIONS: </li></ul>
  22. 24. Discriminant Analysis-Selection of Significant Variables <ul><li>Variables that differ between groups </li></ul><ul><li>PROC STEPDISC </li></ul><ul><li>METHOD=STEPWISE </li></ul><ul><li>SLE = 0.4 </li></ul><ul><li>SLS = 0.05 </li></ul>
  23. 25. <ul><li>PROC STEPDISC DATA=‘ name of file’ METHOD=STEPWISE SLE=0.4 SLS=0.05; </li></ul><ul><li>*SLE(S)=SIGNIFICANCE LEVEL OF ENTRY INTO STEPWISE CANONICAL CORRELATION; </li></ul><ul><li>CLASS VIRTRAD; </li></ul><ul><li>VAR country size lengthexist explifespan group survey teambuild comfrtgroup satwork cohesiv ; </li></ul><ul><li>TITLE2 'STEPWISE DISCRIMINANT ANALYSIS OF VIRTUAL TEAMS DATA'; </li></ul><ul><li>RUN; </li></ul>
  24. 26. Study Conclusions <ul><li>Number of countries spanned by the team </li></ul><ul><li>Industry/Type of organization </li></ul><ul><li>Member satisfaction with working conditions </li></ul><ul><li>Member comfort level with group interactions </li></ul><ul><li>Group cohesiveness </li></ul>
  25. 27. Methodology Summary <ul><li>2. Reliability </li></ul><ul><li>Survey Development </li></ul>4. Validity Task Step SAS Tool PROC CORR, ALPHA 3. Data Reduction PROC FACTOR PROC DISCRIM PROC LOGISTIC
  26. 28. Thank you

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