Analysis of Covariance (ANCOVA)
MathematicsExpert
dsus4data.zipdsus4data/Album Sales.sav
dsus4data/Angry Pigs.sav
dsus4data/Angry Real.sav
dsus4data/Attitude.sav
dsus4data/Band Personality.sav
dsus4data/Beckham (1929).sav
dsus4data/BeerGogglesLighting.sav
__MACOSX/dsus4data/._BeerGogglesLighting.sav
dsus4data/Bernard et al. (2012).sav
dsus4data/Big Hairy Spider.sav
dsus4data/BigBrother.sav
__MACOSX/dsus4data/._BigBrother.sav
dsus4data/Board & Fritzon 2005.sav
dsus4data/Burnout.sav
__MACOSX/dsus4data/._Burnout.sav
dsus4data/Bushtucker.sav
__MACOSX/dsus4data/._Bushtucker.sav
dsus4data/Cat Regression.sav
dsus4data/Cats and Dogs.sav
dsus4data/Cats Weight.sav
dsus4data/Cats.sav
dsus4data/Catterplot.sav
dsus4data/Chamorro-Premuzic.sav
__MACOSX/dsus4data/._Chamorro-Premuzic.sav
dsus4data/Chat-Up Lines.sav
__MACOSX/dsus4data/._Chat-Up Lines.sav
dsus4data/Chick Flick (Mixed).sav
__MACOSX/dsus4data/._Chick Flick (Mixed).sav
dsus4data/chicken.sav
__MACOSX/dsus4data/._chicken.sav
dsus4data/ChickFlick.sav
dsus4data/Child Aggression.sav
__MACOSX/dsus4data/._Child Aggression.sav
dsus4data/CIr.sav
dsus4data/CIr.sps
************************************************************. * Author: Andy Field, University of Sussex, UK . ************************************************************. MATRIX. GET n /VARIABLES = n /MISSING=OMIT. GET r /VARIABLES = r /MISSING=OMIT. COMPUTE z = 0.5*(ln((1+r)/(1-r))). COMPUTE SEz = 1/sqrt(n-3). COMPUTE zscore = z/SEz. COMPUTE sigz = 2*(1-cdfnorm(abs(zscore))). COMPUTE zrupper = z + (1.96*SEz). COMPUTE zrlower = z - (1.96*SEz). COMPUTE rlower =(exp(zrlower/0.5)-1)/(1+exp(zrlower/0.5)). COMPUTE rupper =(exp(zrupper/0.5)-1)/(1+exp(zrupper/0.5)). COMPUTE zCI = {r, rlower, rupper, zscore, sigz}. print "*** 95% Confidence interval for r ***". print zCI /TITLE = " r 95% Lower 95% Upper z Sig". END MATRIX.
__MACOSX/dsus4data/._CIr.sps
dsus4data/Coldwell et al. (2006).sav
dsus4data/condom.sav
__MACOSX/dsus4data/._condom.sav
dsus4data/Contrast.sav
__MACOSX/dsus4data/._Contrast.sav
dsus4data/Cosmetic Surgery.sav
dsus4data/Coulrophobia.sav
__MACOSX/dsus4data/._Coulrophobia.sav
dsus4data/Çetinkaya & Domjan (2006).sav
__MACOSX/dsus4data/._Çetinkaya & Domjan (2006).sav
dsus4data/Daniels (2012).sav
dsus4data/DarkLord.sav
__MACOSX/dsus4data/._DarkLord.sav
dsus4data/Davey(2003).sav
__MACOSX/dsus4data/._Davey(2003).sav
dsus4data/Depression.sav
__MACOSX/dsus4data/._Depression.sav
dsus4data/DepressionSyntax.SPS
MANOVA before after BY treat(0 4) /WSFACTORS time (2) /CONTRAST (time)=special(1 1, 1 -1) /CONTRAST (treat)=special (1 1 1 1 1, -4 1 1 1 1, 0 -3 1 1 1, 0 0 1 1 -2, 0 0 1 -1 0) /CINTERVAL JOINT(.95) MULTIVARIATE(BONFER) /METHOD UNIQUE /ERROR WITHIN+RESIDUAL /PRINT TRANSFORM HOMOGENEITY(BARTLETT COCHRAN BOXM) SIGNIF( UNIV MULT AVERF HF GG ) PARAM( ESTIM EFSIZE).
__MACOSX/dsus4data/._DepressionSyntax.SPS
dsus4data/DFBeta.sav
dsus4data/Diet.sav
dsus4data/Differences between dependent r.sps
************************************************************. * Author: Andy Field, University of Sussex, UK . ************************************************************. MATRIX. GET rxy /VARIABLES = rxy. GET rzy /VARIABLES = rzy. GET rxz /VARIABLES = rxz. GET n /VARIABLES = n. COMPUTE diff = rxy-rzy. COMPUTE ttest = diff*(sqrt(((n-3)*(1+rxz))&/(2*(1 - rxy**2 - rxz**2 - rzy**2 + (2*rxy)*rxz*rzy)))). COMPUTE sigt = tcdf(ttest,(n-3)). COMPUTE output = {diff, ttest, sigt}. print "*** Tests of Differences between Dependent Correlation Coefficiants ***". print output /TITLE = " Difference t Sig". END MATRIX.
__MACOSX/dsus4data/._Differences between dependent r.sps
dsus4data/Display.SAV
__MACOSX/dsus4data/._Display.SAV
dsus4data/DownloadFestival.sav
dsus4data/Drug.sav
dsus4data/Dummy.sav
__MACOSX/dsus4data/._Dummy.sav
dsus4data/Eastenders.sav
__MACOSX/dsus4data/._Eastenders.sav
dsus4data/Eel.sav
dsus4data/Elephant Football.sav
dsus4data/Escape From Inside.sav
__MACOSX/dsus4data/._Escape From Inside.sav
dsus4data/EssayMarks.sav
__MACOSX/dsus4data/._EssayMarks.sav
dsus4data/Exam Anxiety.sav
dsus4data/Facebook.sav
__MACOSX/dsus4data/._Facebook.sav
dsus4data/Field (2006).sav
__MACOSX/dsus4data/._Field (2006).sav
dsus4data/Field&Hole.sav
__MACOSX/dsus4data/._Field&Hole.sav
dsus4data/fugazi.sav
__MACOSX/dsus4data/._fugazi.sav
dsus4data/Gallup et al.sav
__MACOSX/dsus4data/._Gallup et al.sav
dsus4data/Gelman & Weakliem (2009).sav
dsus4data/GlastonburyDummy.sav
dsus4data/GlastonburyFestival.sav
__MACOSX/dsus4data/._GlastonburyFestival.sav
dsus4data/GlastonburyFestivalRegression.sav
__MACOSX/dsus4data/._GlastonburyFestivalRegression.sav
dsus4data/Goat or Dog.sav
dsus4data/goggles.sav
dsus4data/GogglesRegression.sav
__MACOSX/dsus4data/._GogglesRegression.sav
dsus4data/GogglesSimpleEffects.SPS
glm Attractiveness by gender alcohol /emmeans = tables(gender*alcohol)compare(gender).
__MACOSX/dsus4data/._GogglesSimpleEffects.SPS
dsus4data/grades.sav
__MACOSX/dsus4data/._grades.sav
dsus4data/Gueguen (2012).sav
dsus4data/Handlebars.sav
__MACOSX/dsus4data/._Handlebars.sav
dsus4data/HangoverCure.sav
dsus4data/Hiccups.sav
__MACOSX/dsus4data/._Hiccups.sav
dsus4data/Hill et al. (2007).sav
__MACOSX/dsus4data/._Hill et al. (2007).sav
dsus4data/HonestyLab.sav
dsus4data/Honeymoon Period Restructured.sav
__MACOSX/dsus4data/._Honeymoon Period Restructured.sav
dsus4data/Honeymoon Period.sav
__MACOSX/dsus4data/._Honeymoon Period.sav
dsus4data/Horoscope.sav
__MACOSX/dsus4data/._Horoscope.sav
dsus4data/Independent r.sav
__MACOSX/dsus4data/._Independent r.sav
dsus4data/Independent t from means.sps
COMPUTE df = n1+n2-2. COMPUTE Diff = x1-x2. COMPUTE poolvar = (((n1-1)*(sd1 ** 2))+((n2-1)*(sd2 ** 2)))/df. COMPUTE poolsd = sqrt((((n1-1)*(sd1 ** 2))+((n2-1)*(sd2 ** 2)))/(n1+n2)). Compute SE = sqrt(poolvar*((1/n1)+(1/n2))). COMPUTE CI_Upper = Diff+(idf.t(0.975, df)*SE). Compute CI_Lower = Diff-(idf.t(0.975, df)*SE). COMPUTE d = Diff/poolsd. COMPUTE t_test = Diff/SE. COMPUTE t_sig = 2*(1-(CDF.T(abs(t_test),df))). Variable labels Diff 'Difference between Means (X1-X2)'. Variable labels SE 'Standard Error of Difference between means'. Variable labels poolsd 'Pooled SD'. Variable labels d 'Effect Size (d)'. Variable labels t_test 't statistic'. Variable labels t_sig 'Significance (2-tailed)'. Variable labels CI_Upper '95% Confidence Interval (Upper)'. Variable labels CI_Lower '95% Confidence Interval (Lower)'. Formats t_sig(F8.5). EXECUTE . SUMMARIZE /TABLES= x1 x2 Diff CI_Lower CI_Upper df t_test t_sig d /FORMAT=VALIDLIST NOCASENUM TOTAL LIMIT=100 /TITLE='T-test' /MISSING=VARIABLE /CELLS=NONE.
__MACOSX/dsus4data/._Independent t from means.sps
dsus4data/Infidelity.sav
__MACOSX/dsus4data/._Infidelity.sav
dsus4data/Invisibility Baseline.sav
dsus4data/Invisibility RM.sav
dsus4data/Invisibility.sav
dsus4data/Jiminy Cricket.sav
dsus4data/Johns et al. (2012).sav
dsus4data/Lacourse et al. (2001) Females.sav
__MACOSX/dsus4data/._Lacourse et al. (2001) Females.sav
dsus4data/Lambert et al. (2012).sav
dsus4data/LooksOrPersonality.sav
__MACOSX/dsus4data/._LooksOrPersonality.sav
dsus4data/lying.sav
dsus4data/Marzillier & Davey (2005).sav
__MACOSX/dsus4data/._Marzillier & Davey (2005).sav
dsus4data/Massar et al. (2011).sav
dsus4data/Matthews et al. (2007).sav
dsus4data/McNulty et al. (2008).sav
dsus4data/MenLikeDogs.sav
__MACOSX/dsus4data/._MenLikeDogs.sav
dsus4data/Method Of Teaching.sav
__MACOSX/dsus4data/._Method Of Teaching.sav
dsus4data/Miller et al. (2007).sav
__MACOSX/dsus4data/._Miller et al. (2007).sav
dsus4data/MixedAttitude.sav
__MACOSX/dsus4data/._MixedAttitude.sav
dsus4data/Murder.sav
dsus4data/Muris et al (2008).sav
__MACOSX/dsus4data/._Muris et al (2008).sav
dsus4data/Nichols & Nicki (2004).sav
__MACOSX/dsus4data/._Nichols & Nicki (2004).sav
dsus4data/OCD.sav
dsus4data/Ong et al. (2011).sav
dsus4data/Outliers (Percentage of Z-sc.textClipping
__MACOSX/dsus4data/._Outliers (Percentage of Z-sc.textClipping
dsus4data/Oxoby (2008) MOA.sav
dsus4data/Oxoby (2008) Offers.sav
dsus4data/PBCorr.SAV
dsus4data/Penalty.sav
dsus4data/Penis.sav
__MACOSX/dsus4data/._Penis.sav
dsus4data/Perham & Sykora (2012).sav
dsus4data/Piff et al. (2012) Pedestrian.sav
dsus4data/Piff et al. (2012) Vehicle.sav
dsus4data/ProfilePicture.sav
dsus4data/psychology.sav
__MACOSX/dsus4data/._psychology.sav
dsus4data/pubs.sav
__MACOSX/dsus4data/._pubs.sav
dsus4data/RecodeGlastonburyData.SPS
DO IF (1-SYSMIS(change)). RECODE music (3=1)(ELSE = 0) INTO Crusty. RECODE music (2=1)(ELSE = 0) INTO Metaller. RECODE music (1=1)(ELSE = 0) INTO Indie_Kid. END IF. VARIABLE LABELS Crusty 'No Affiliation vs. Crusty'. VARIABLE LABELS Metaller 'No Affiliation vs. Metaller'. VARIABLE LABELS Indie_Kid 'No Affiliation vs. Indie Kid'. VARIABLE LEVEL Crusty Metaller Indie_Kid (Nominal). FORMATS Crusty Metaller Indie_Kid (F1.0). EXECUTE.
__MACOSX/dsus4data/._RecodeGlastonburyData.SPS
dsus4data/RovingEye.sav
dsus4data/Sage Editors Can't Play Football.sav
__MACOSX/dsus4data/._Sage Editors Can't Play Football.sav
dsus4data/SAQ (Item 3 Reversed).sav
dsus4data/SAQ.sav
dsus4data/Schützwohl(2008).sav
dsus4data/Shopping Exercise.sav
dsus4data/SimpleEffectsAttitude.sps
DATASET ACTIVATE DataSet2. GLM beerpos beerneg beerneut winepos wineneg wineneut waterpos waterneg waterneut /WSFACTOR=Drink 3 Imagery 3 /EMMEANS = TABLES(Drink*Imagery) COMPARE(Imagery).
dsus4data/Sing or Guitar.sav
dsus4data/Sonnentag (2012).sav
dsus4data/Soya.sav
dsus4data/SPSSExam.sav
__MACOSX/dsus4data/._SPSSExam.sav
dsus4data/Stalker.sav
dsus4data/Superhero.sav
dsus4data/Supermodel.sav
__MACOSX/dsus4data/._Supermodel.sav
dsus4data/Tablets.sav
dsus4data/Tea Makes You Brainy 15.sav
dsus4data/Tea Makes You Brainy 716.sav
dsus4data/Teach.sav
__MACOSX/dsus4data/._Teach.sav
dsus4data/Text Messages.sav
__MACOSX/dsus4data/._Text Messages.sav
dsus4data/The Biggest Liar.sav
dsus4data/TOSSE-R.sav
__MACOSX/dsus4data/._TOSSE-R.sav
dsus4data/Transformations.SPS
COMPUTE logday1 = LG10(day1 + 1) . COMPUTE logday2 = LG10(day2 + 1) . COMPUTE logday3 = LG10(day3 + 1) . COMPUTE sqrtday1 = SQRT(day1). COMPUTE sqrtday2 = SQRT(day2). COMPUTE sqrtday3 = SQRT(day3). COMPUTE recday1 = 1/(day1+1). COMPUTE recday2 = 1/(day2+1). COMPUTE recday3 = 1/(day3+1). EXECUTE .
