Table-related commands in STATA

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Abbreviations of commands

table (no abbv.)
tabulate ta
tab
tabstat (no abbv.)
summarize su

Differences between table, tabulate, tabstat, summarize

one-way two-way options
table
table v1

create a one-way table of v1
with simple frequency

table v1 v2

create a two-way table of v1
in row† and v2 in column†

,statistics( )
tabulate
tabulate v1

create a one-way table of v1
with detailed frequency

tabulate v1 v2

create a two-way table with v1
in row† and v2 in column†

,chi2

Pearson's chi-squared test; *only for two-way

,summarize(v3)

detailed statistics for v3

tabstat
tabstat v1

create a one-way table of v1
with detailed statistics

*no two- or multiple-way table
,statistics( )
,by(v3)

detailed statistics for each of v3

summarize
summarize v1

detailed statistics of v1

*no two- or multiple-way summary
,detail

† row = transverse direction, column = longitudinal direction

Sample data

Suppose we have such a dataset in STATA.

 

Where,

id discrete :Identification number
sex binary :Male=0, Female=1
data1 continuous :Results of a certain test
factorA, B, C binary :Negative=0, Positive=1
SES categorical :Categories of Socio-Economic Status, divided into four
disease binary :Free from a certain disease=0, Having the disease=1

One-way

Summary of sex, a binary variable

table table sex

 

Both reports frequency
but tabulate is more detailed
tabulate tabulate sex

 

tabstat >tabstat sex

 

Both reports mean
but summarize is more detailed
summarize summarize sex

 

Summary of data1, a continuous variable

table   Both reports frequency of each value,
which does not make sense
tabulate  
tabstat   Both reports mean
but summarize is more detailed
summarize  

Summary of SES, a categorical variable

table   Both reports frequency
but tabulate is more detailed
tabulate  
tabstat   Both reports mean
but summarize is more detailed
summarize  

One-way, multiple

table *Both do not create one-way multiple table
tabulate
tabstat   Reports mean in row (transverse) direction
summarize   Reports more details in column (longitudinal) direction

Two-way

Summary of factorA based on sex

table   Both creates the same table
but tabulate is better visualized
tabulate  
tabstat   Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize  

Summary of sex based on factorA

table   Both creates the same table
but tabulate is better visualized
tabulate  
tabstat   Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize  

Summary of data1 based on disease

table *Both do not create a meaningful table for continuous variable
tabulate
tabstat   Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize  

Two-way with proportions

Summary of factorA based on sex with proportions

table   This calculates proportions of cells compared to the whole
without showing raw values
  This calculates proportions in column (longitudinal) directions
without showing raw values
  This calculates proportions in row (transverse) directions
without showing raw values
tabulate   This calculates proportions in column (longitudinal) directions
  This calculates proportions in row (transverse) directions

Two-way, multiple

Summary of factorA, factorB, factorC based on disease

tabstat  
  factorA,B,C are binary variables so summations of values provide the positivities of factorA,B,C
  statistic(n) (statistic(count) is the same) only counts observations with real values, which only tell non-missing observations

Summary of factorA, factorB, factorC based on SES

tabstat  
  factorA,B,C are binary variables so summations of values provide the positivities of factorA,B,C
  statistic(n) (statistic(count) is the same) only counts observations with real values, which only tell non-missing observations

Two-way of binary/categorical plus summary of continuous

Summary of data1 based on disease and SES

tabulate   This tells means, SDs and frequencies of a continuous variable divided in two-way of binary/categorical variables