| Task | R | SAS | SPSS | Stata |
Analysis of Variance
| myModel <- aov(posttest ~ workshop,
data = mydata100)
summary(myModel)
pairwise.t.test(posttest, workshop)
TukeyHSD(myModel, "workshop")
plot(TukeyHSD(myModel, "workshop"))
| PROC GLM;
CLASS workshop;
MODEL posttest =
workshop;
MEANS workshop /
TUKEY;
| UNIANOVA posttest BY workshop
/POSTHOC = workshop ( TUKEY )
/PRINT = ETASQ HOMOGENEITY
/DESIGN = workshop.
| anova posttest workshop
|
Correlate,
Pearson
| cor( mydata[3:6],
method = "pearson",
use = "pairwise")
cor.test(mydata$q1,
mydata$q2, use = "pairwise")
library("Rcmdr")
rcorr.adjust( mydata[3:6] )
| PROC CORR; VAR q1-q4; RUN;
| CORRELATIONS
/VARIABLES=q1 TO q4. | correlate q*
|
Correlate,
Spearman
| cor( mydata[3:6],
method = "spearman",
use = "pairwise")
cor.test(mydata$q1,
mydata$q2,
use = "pairwise")
library("Rcmdr")
rcorr.adjust( mydata[3:6] )
|
PROC CORR SPEARMAN; VAR q1-q4;
| NONPAR CORR
/VARIABLES=q1 to q4
/PRINT=SPEARMAN. | spearman q*
|
Crosstabulation
& Chi-squared | myWG <- table(workshop, gender)
chisq.test(myWG)
library("gmodels")
CrossTable(workshop, gender,
chisq = TRUE,
format = "SAS") | PROC FREQ;
TABLES workshop*gender /CHISQ;
| CROSSTABS
/TABLES=workshop BY
gender
/FORMAT= AVALUE TABLES
/STATISTIC=CHISQ
/CELLS= COUNT ROW
/COUNT ROUND CELL.
| tab gender workshop, row col exact
|
Descriptive Stats
| summary(mydata)
library("Hmisc")
describe(mydata)
| PROC MEANS;
VAR q1--posttest;
PROC UNIVARIATE; VAR q1--posttest; | DESCRIPTIVES VARIABLES=q1 to posttest
/STATISTICS=MEAN STDDEV VARIANCE
MIN MAX SEMEAN.
EXAMINE VARIABLES=q1 to posttest
/PLOT BOXPLOT STEMLEAF NPPLOT
/COMPARE GROUP
/STATISTICS DESCRIPTIVES EXTREME
/MISSING PAIRWISE.
| summary q*
summary q*, detail |
| Frequencies | summary(mydata)
library("Deducer")
frequencies(mydata)
| PROC FREQ;
TABLES workshop--q4; | FREQUENCIES VARIABLES=
workshop TO q4.
| tab1 workshop gender q*
|
| Kruskal-Wallis | kruskal.test(posttest ~
workshop)
pairwise.wilcox.test(posttest,
workshop) | PROC npar1way;
CLASS workshop;
VAR posttest; | NPAR TESTS
/K-W=posttest BY
workshop(1 3).
| kwallis q1, by(gender)
|
Regression, Linear
| myModel <- lm(q4 ~ q1 + q2 + q3,
data = mydata100)
summary(myModel)
plot(myModel)
| PROC REG;
MODEL q4=q1-q3; | REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT q4
/METHOD=ENTER q1 q2 q3.
| regress q4 q1-q3
lvr2plot
|
Sign Test
| library("PASWR")
SIGN.test(posttest, pretest,
conf.level = .95) | myDiff=posttest-pretest;
PROC UNIVARIATE;
VAR myDiff;
| NPTESTS
/RELATED TEST(q1 q2) SIGN
/MISSING SCOPE=ANALYSIS USERMISSING=EXCLUDE
/CRITERIA ALPHA=0.05
CILEVEL=95.
| bitest posttest > pretest
|
t-Test, Independent
| t.test(q1 ~ gender,
data = mydata100) | PROC TTEST;
CLASS gender;
VAR q1;
| T-TEST
GROUPS = gender('m' 'f')
/VARIABLES = q1.
| ttest gender=q1, unpair unequ
|
t-Test, Paired
| t.test(posttest, pretest,
paired = TRUE)
| PROC TTEST;
PAIRED pretest*
posttest;
| T-TEST
PAIRS=pretest WITH
posttest (PAIRED).
| |
Variance Test
| # Bartlett's
var.test(posttest ~ gender)
# Levene's
library("car")
levene.test(posttest, gender) | | | robvar posttest, by(gender)
* Or...
sdtest posttest = gender
|
Wilcoxon Rank Sum (Mann-Whitney)
| wilcox.test(q1 ~ gender,
data = mydata100) | PROC NPAR1WAY;
CLASS gender;
VAR q1;
| NPTESTS
/RELATED TEST(pretest
posttest) SIGN WILCOXON.
| ranksum posttest, by(gender)
|
Wilcoxon Signed Rank (Paired)
| wilcox.test(posttest, pretest,
paired = TRUE) | myDiff=posttest-pretest;
PROC UNIVARIATE;
VAR myDiff; | NPTESTS
/RELATED TEST(q1 q2) WILCOXON
/MISSING SCOPE=ANALYSIS USERMISSING=EXCLUDE
/CRITERIA ALPHA=0.05 CILEVEL=95.
| signrank q1 = gender
|