SYNTAX FOR STATA USE PDF

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Summary

This is a detailed syntax for stata commands. Table form makes it easy to use during the exam or while analysing data....

Description

Syntax ANOVA DV IVlistf

Test/analysis One-way ANOVA

Notes Includes variance of group means and total variation.

ANOVA DV Ivlist IV1#IV2

Factorial ANOVA: main effects Observe main effects & interactions & interaction

ANOVA DV IV1#IV2

Factorial Anova interaction

by BYvars: summarize VARlist codebook VARlist

Comparing outputs

descriptives

produces summary data for variable list stratified by levels of categories

additional information

gives you range min max missing values mean sd and percentiles

contrast {VARcat weights…} {VARcat weights...}, effects complex contrasts

Testing main effects and interaction effects

contrast p.VARcat

Polynomial contrasts

Tests linear, quadratic, cubic trends

Reference contrasts

Testing difference in estimated cell means, e.g. gender

contrast r.VARcat correlate VARlist correlate VARlist, covariance

estat vif

Frequency VARlist

correlation matrix

Compute correlations between variables

covariance matrix

Computes the sample covariances between variables

Descriptives for VIF collinearity

Run after regress. Shows VIF and tolerance for each IV. VIF should be < 10, tolerance > 0.1f

frequency table

Summary statistics for categorical & continuous variables

Graph bar (count), over(VAR)

Generating bar graph of variables

Usually used for categorical data

graph bar (count), over(VAR) over(VAR) asyvars

Coloured graph bar with frequencies

(count) gives frequencies rather than % Asyvars is colour

Draws vertical bar charts

Here (percept) gives you % rather than frequencies

Clustered bar chart

Create clustered bar chart to illustrate the two way ANOVA with predicted values.t

Pie Chart

Used for categorical data to see proportions of participants (percentage distribution)

graph twoway (scatter VARlist) (lfit VARlist), ytitle ("IV”)

Two-way scatterplot graph

Used to see variance!

histogram VAR, by(groupVAR), freq

Histogram of a variable

graph bar (percent), over(VAR) graph bar yhat, over(IV1) over(IV2) asyvars ytitle("Predicted Cell Means for DV”) graph pie, over(VAR)

Can be used to see central tendency

Data features

histogram, no. of pos/0/neg. intergers/nonintergers, missing data

margins VARcat

Marginal means

displays group means

margins VARcat, at(VARcont(values))

Computes the predicted values for each specified case (designated by at)

inspect VARlist

marginsplot

Plot of marginal means

pnorm VARlist

Standardised normal probability plot

predict dfbetaVARname, dfbeta(VARname)

Influence statistics

predict RESNAME, resid predict YpredVARname pwcompare VARcat, effects mcompare(DECISION RULE) pwcorr VARlist, sig obs

regress DV IV

regress DV IVlist

Regression - testing assumptions

MUST follow 'margins' command as it plots the result of this command

this creates a new variable with values of your residuals

Creates predictions for This must follow the regression you want to sample in which model was fit test

Pairwise contrasts

Pairwise comparison contrasts & 'mcompare' adjusts contrasts using chosen decision rule E.g Scheffe, Bonferroni

Pairwise sample correlations selects cases pairwise (rather than the default matrix listwise) for analysis

Simple regression

Runs a linear regression. First put in DV followed by the IV

Multiple regression

Runs a linear regression with more IV's (if applicable) after the DV

regress DV Ivlist, beta

Display beta regression coefficients value (0.0 - 1.0) instead of CI95% as z-scores so the effects are comparable (which IV has more effect on Beta (standardised) rather than confidence the DV) intervals

robvar VAR, by(groupVAR)

Levene’s test of equality of variance: homoscedasticity

rvfplot

Residual-versus-fitted scores Plots residuals (of observations) against the plot regression line

rvpplot

looks for violations of the regression assumptions – If assumptions are correct, Residual-versus-predictor plot there should be NO pattern on the graph

scatter DV Ypred IVlist

we want the P value to be more than .05: the variability is not significantly different

Observed vs. predicted values Formation of scatterplot

scatter RESNAME IVlist

Scatterplot with residual

scatter RESNAME Ypred

Scatterplot with line of best fit Also shows predicted values

Summarize VARlist

See summary information for the variables Basic descriptives of variables listed

Summarize VARlist, detail

Detailed Descriptives

Descriptives with skew, kurtosis, etc

swilk VARlist

shapiro wilk test of normality

we want the P value to be more than .05

tab VARlist

contingency table

tab VARlist, exact

independence test without distribution convergence assumption

tab VARlist, row expected chi2

chi-square test with freq, %, expected

be careful- computationally difficult to compute- if there is a lot of data

frequency tabulation

Shows categorical variables in frequencies and percentages

2-factor contingency table

creates 2-factor contingency tables for all combinations of variations in VAR list

table IVlist, contents (n DV mean DV sd DV) row col format(%6.2f)

Used to find the cell and marginal means of factorial anova

Gives n, mean and SD of each Iv * IV combination

format(%6.2f)

The output will show 6 figures and round of to 2 decimal places (??) Set variables’ output format (fixed)???

tab1 VARlist tab2 VARlist

tabulate VARlist tabulate IVlist, summarize (DV)

ttest DV, by(IV)

Same as tab1 varlist

Shows categorical variables in frequencies and percentages

Summarise mean, SD and Freq for the variables t-test between two groups (between subject) Gives you t-statistic for your DV by the IV

ttest VAR == testVALUE

One-sample t test and Twosample t test using groups

ttest VAR1 = VAR2

Two-sample t test using variables and Paired t test

twoway scatter DV IV

Scatterplot between two variables

Scatterplot showing the DV and IV (with no line of best fit)

twoway scatter DV IV || lfit DV IV

Scatterplot with linear prediction added

Scatterplot with line of best fit

webuse FILE

Scatterplot with linear prediction added

access data file on server

webuse set URL

Scatterplot with linear prediction added

access server holding data...