Uslaner 1999 Brief Guide To Stata Commands

Uslaner 1999 Brief Guide To Stata Commands - Transcript

Brief Guide to STATA Commands Eric M Uslaner December 1999

use save

clear set mem 80m

exit help

search log

if

in describe des ds codebook inspect summarize sum lsum list varlist sort gsort order aorder

use a data set e g use anes92 save a data set save anes92 or save anes92 replace if the data set already exists save anes92 old for STATA 5 format Or Control S clear existing data in memory allocate 80 megs of memory to STATA default depends on machine NOTE THAT STATA has a maximum limit of 2047 variables no matter how much memory you have exit STATA help for STATA command help contents gives a list of STATA commands where you can get help search the on line manual for all references to e g search regress gives all references to regress in STATA it s a lot set log file for output e g log using c log mylog will produce a file called mylog log which you can edit in any ASCII word processor Variations log using c log mylog append will add to existing file log using c log mylog replace will replace existing file log close will close log file can reopen with append restricts commands to those cases that fulfill condition e g sum var1 if partyid 1 note two equal signs needed will produce a summary see below of var1 only for partyid 1 e g Democrats restricts commands to ranges you specify sum var1 in 1 20 will summarize only the first 20 cases of var1 produces a list of variables with format and labels produces a list of variables without format or labels will produce codebook of variable information takes time will provide mini histograms of data will provide summaries of data means sds numbers of cases max min sum varlist detail will provide breakdowns by quartiles If installed lsum varlist will give you summary only for cases that are not missing for any variable will print out all values of variables in varlist sorts variables in ascending order If installed sorts variables in ascending or descending order changes the order of variables to your preferences If installed orders variables alphabetically

recode

recode variables as in recode var1 3 2 2 1 1 0 4 Note that var1 is recoded to missing see mvdecode Also note that you can only recode one variable at a time in a recode command but see for below

mvencode

mvencode changes all occurrences of missing to in the specified varlist see below mvdecode changes all occurrences of to missing in the specified varlist e g mvdecode var1 var99 mv 999 changes all instances of 999 in var1 through var99 to system missing

mvdecode

generate gen

create new variables the equivalent of compute in SPSS E g gen pcincome income pop where pop population A neat feature is gen byte democrat partyid 1 creates a dummy variable democrat that is equal to 1 if partyid 1 and 0 otherwise you generally need to recode missing values in the new dummy variable to be missing in the new variable which is best done with the replace command Note that the byte command makes the new variable a byte variable occupying less space in the data set You can also use generate with many functions such as mean while also using the command by E g gen incstate income by state which will give you a contextual variable of the income level in each state computed from income figures and the state code in variable state

egen

like gen it stands for extensions to generate egen handles some more complex functions While gen x mean var1 will produce a constant containing the mean for var1 egen z rmean var2 var3 var4 var7 will produce a variable that has the row means for variables 2 3 4 and 7 rsum e g gives the row sum of the variables in the list and rmiss gives the number of missing variables of variables in the list

replace

like generate but the variable already exists E g replace democrat if partyid More generally gen byte inccat 0 if income 10000 replace inccat 1 if income 10000 income 30000 replace inccat 2 if income 20000 income 60000 replace inccat 3 if income 60000 This will give you a four category 0 1 2 3 variable for inccat income category Alternatively if you have the egen function gcut installed you may type egen byte inccat cut income at 0 10000 30000 60000 which will accomplish the same thing as the above four commands

rename

renames variables as in ren var1 partyid If the old dataset simply names variables var1 etc you can use rename to change their names If installed renames lets you rename many variables in one command creates variable labels as in label var partyid party identification

label var

label define

defines value labels for variable e g label define partyid 1 Democrat 2 Independent 3 Republican Or label define partyid 1 dem identify 2 independ identify 3 repub identify

label values

Use after label define e g label values partyid partyid assigns the value label partyid to the variable partyid label comes first variable name second

genl

If installed Lets you generate a new variable and assign variable label in one command genl pcincome income pop label per capita income

merge

merges two data sets There must be at least one common variable and both data sets must be sorted on that variable and saved If the common variable is xxx the syntax is merge xxx using mydata merge creates a variable called merge that helps you to determine whether the merge worked correctly merge 3 occurs when the observation is in both data sets what you normally expect merge 1 occurs when the observation only occurs in the master data set in memory and merge 2 occurs when the observation only occurs in the data set that you are bringing in mydata If you expect that all observations are in both data sets and some values of merge are either 1 or 2 you have not merged correctly Once you have completed the merge you can delete the variable merge If you have already installed mmerge it will sort the data for you and will drop the merge variable

drop

Use drop to delete variables or observations drop var1 eliminates var1 drop var1 in 1500 2000 drops observations 1500 2000 for var1 drop in 1500 2000 drops observations 1500 2000 for all variables label drop partyid drops the label partyid

keep

the opposite of drop using keep drops everything that is NOT in the keep command if installed a neat utility that automatically drops all variables where all values are missing don t laugh a lot of surveys have questions such as this

dropmiss

set more

more is the command that controls scrolling set more on output stops at the bottom of the window until you hit spacebar set more off output scrolls continously most useful if output is being directed to a log file and you don t need it to stop each page

set matsize

set matsize sets the maximum number of variables that can be included in any of Stata s model estimation commands Sometimes you will run up against a matsize too small command if you are doing a particularly memory intensive task Then you should type in something like set matsize 500 STATA may refuse to do this saying that data in memory would be lost Then you need to clear the data set reload it and type in the command for matsize and type in the command for your estimation again

recast or compress

Used STAT Transfer on a SAS data set and forgot to invoke the optimize option Then you have a huge data set since SAS always saves data as double precision lots of memory used So what do you do Use STATA s compress command to change variables to their smallest possible size Simply type compress and hit enter If you only need to change a few variables use STATA s recast function to change a particular variable from double to byte recast byte partyid

lookfor

A good enough reason to change from SPSS or SAS to STATA Suppose that you think that you have some variables on party identification in your data set but can t remember what you called them but you know that they either have variable labels with party in them or are called party You type lookfor party And STATA returns as in describe partyid pid3 giveprty party identification 3 point party identification contributed money to political party

for

The mother of all STATA commands It allows repetition of STATA commands The STATA manual states for listtype list command containing X If listtype is then list is a varlist varlist newlist new varlist numlist numlist see help numlist anylist list of words Basic syntax examples for var m replace X X 10 for new u1 u10 gen X uniform for num 1 5 count if freq X for any 1 replace x if y X In each example elements from the list are substituted one at a time for the capital X and the command executed Or in English for var var1 var2 var3 var4 partyid recode X 4 3 3 2 2 1 for num 1 2 3 4 recode varX 4 3 3 2 2 1 for new newvar1 newvar 2 newvar3 for var var1 var2 var3 gen X Y pop The first for statement recodes four variables at a time using for The second for statement recodes four variables at a time when these variables have similar names var1 var2 var3 var4 You can do the same thing by writing for var var1 var4 recode X 4 3 3 2 2 1 The third statement creates four new variables using the place holder Y from four old variables using the place holder X

by

repeats STATA commands E g by partyid regress y var1 var4 performs the regression of var1 through var 4 on y for each category of partyid Note that you must first sort the data set on partyid unless you have already installed bys which automatically sorts the data for you Note Not all commands allow you to use either by or bys

Key statistical commands regress reg3 factor anova tab tabulate OLS regression two stage and three stage least squares factor analysis analysis of variance cross tabs for more complex tables see the tables command Note that tab has a very useful data manipulation feature tab var1 gen newvar This tabulates var1 and creates new variables newvar1 through newvarx for the x categories of var1 So if you type tab partyid gen newvar you will get newvar1 label partyid 1 for Democrats newvar2 label partyid 2 for Independents newvar3 label partyid 3 for Republicans and you might want to type for newvar1 newvar2 newvar3 for any democrat independ republic ren X Y and then for democrat independ republic replace X if partyid Or alternatively for democrat independ republic recode X 0 if partyid tab1 probit logit oprobit or ologit mlogit z or zz m or mm tobit dtobit sureg corr pwcorr pcorr ttest sparl univariate tabulate for multiple variables probit analysis logit analysis ordered probit logit analysis multinomial logit programs for calculating marginal effects after probit or logit programs for calculating marginal effects after multinomial logit tobit analysis tobit with marginal effects or dtobit2 if installed seemingly unrelated systems of equations correlation pair wise correlations partial correlations ttests if installed provides scatterplot of two variables with regression line drawn and R2 and regression equation above the graph

I ve referred to files that you might install yourself Where do you get these files On the web go to http ideas uqam ca ideas data bocbocode html which is a site maintained by Boston College Economics Professor Kit Baum You can also find a link to it and other useful resources at of coures http www stata com and you can get lots of useful files by subscribing to STATALIST a daily listserv on STATA issues that you can find by going to STATA s web site warning if you join you should select the digest option lest you get 50 or so distinct messages each day

Stata Quick Reference
This quick reference shows examples of the Stata commands illustrated in the Stata class and Stata learning modules The Stata commands are preceded by a period but do not type the period when you type the command For example
l ist

lists out the observations for the datafile in memory
Fundamentals of Using Stata Part I o o o Starting and Stopping Stata Descriptive Information Statistics Getting Help

Fundamentals of Using Stata Part II o o o o Exploring Data with Graphics Using if with Stata Commands Overview of Statistical Tests in Stata General Syntax of Stata Commands



Reading Data in Stata o o o o Using and Saving Stata data files Inputting data into Stata using the Stata Data Editor Inputting data into Stata Reading Dates into Stata and using date variables



Basic Data Management in Stata o o o Labeling Data Variables and Values Creating and Recoding Variables Subsetting Variables and Observations



Advanced Data Management in Stata o o o o Collapsing data across observations Combining Stata data files Reshaping data from wide to long Reshaping data from long to wide

M aking and Running do files

F undamentals of Using Stata Part I S TARTING STOPPING STATA

Starting Stata on the PC Click Start Programs Stata Intercooled Stata At SSC you can Click Stata in Applications window Stopping Stata Type exit in the command window DESCRIPTIVE INFORMATION STATISTICS
d escribe

provides information about the current data file including the number of variables observations and a listing of the variables in a data file
c odebook

produces codebook like information for the current data file
i nspect

provides a quick overview of datafile
l ist model mpg

lists out the variables model and mpg
c ount

counts the number of observations
t abulate mpg

makes a table of mpg
t abulate rep78 foreign

makes a two way table of rep78 by foreign
s ummarize mpg price

produces summary statistics of mpg and price
s ort foreign b y foreign summarize mpg

produces summary statistics for mpg separately for foreign domestic cars
t abulate foreign summarize mpg

produces summary statistics for mpg by foreign prior sorting not required

GETTING HELP
h elp summarize

shows stata help page for the summarize command also try the pulldown menu clicking help then Stata Command
s earch memory

searches online help for the keyword memory also try the pulldown menu clicking help then search Stata Web Site See the Stata web site at http www stata com OAC Web Site See the OAC web site at http www oac ucla edu From the home page click Training Consulting and then click Statistical Computing for information about OAC Classes Consulting Services Computing Services including access to the online Stata Learning Modules Also see the page with Resources to Help You Learn and Use Stata

Fundamentals of Using Stata Part II EXPLORING DATA WITH GRAPHICS
g raph weight

make a histogram of the variable weight
g raph weight box

make a boxplot of the variable weight
g raph weight price

make a scatterplot of price weight
g raph weight price twoway oneway box

show scatterplot of weight and price with boxplots and oneway plots
g raph mpg weight price matrix

show scatterplot matrix of mpg weight price
g raph weight by foreign

make separate histograms for foreign and domestic cars the by foreign could be added to most graphs to get separate graphs for foreign and domestic cars USING if WITH STATA COMMANDS
s ummarize price if rep78 4

Creates summary statistics of price for observations where rep78 is 4 or greater and also missing if there is missing data
s ummarize price if rep78 4 rep78

Creates summary statistics of price for observations where rep78 is 4 or greater and rep78 is not missing
s ummarize price if rep78 1 rep78 2

Creates summary statistics of price for observations where rep78 is

1 or rep78 is 2
O VERVIEW OF STATISTICAL TESTS IN STATA t test price by foreign

performs a t test on price by foreign which has 2 values 0 and 1 representing domestic and foreign cars
t abulate rep78 foreign chi2

performs a chi square test of independence to see if rep78 is independent of foreign
c orrelate price mpg weight rep78

displays correlations among price mpg weight and rep78
p wcorr price mpg weight rep78 obs

displays correlations among price mpg weight and rep78 using pairwise deletion for missing data and displaying the number of observations for each pair
r egress price mpg weight

performs OLS regression analysis predicting price from mpg weight
o neway price rep78

performs analysis of variance with price as the dependent variable and rep78 as the indendent variable
a nova price rep78 mpg weight continuous mpg weight

performs analysis of variance with price as the dependent variable rep78 as the indendent variable and mpg weight as covariates

G ENERAL SYNTAX OF STATA COMMANDS

The summarize command is used to illustrate the general syntax of Stata commands
by varlist summarize varlist if exp in range options

Examples
s ummarize mpg s ummarize mpg if weight 2000 s ummarize mpg in 1 10 s ummarize mpg detail s ummarize mpg if foreign 1 detail s ort foreign b y foreign summarize mpg

Here are more examples illustrating the use of in
s ummarize in 1

summary statistits for observation number 1
s ummarize in 1 10

summary statistics for observation number 1 10
s ummarize in 10 1

summary statistics for 10th from last to last observation Here are more examples illustrating the use of if
T he if exp can be simple like s ummarize if mpg 20

if mpg is 20
s ummarize if mpg 20

if mpg is less than 20
s ummarize if mpg 20

if mpg is leass than or equal to 20
s ummarize if mpg 20

if mpg is not 20 but see below if mpg has missing data
s ummarize if mpg 20

if mpg is greater than 20 but see below if mpg has missing data
s ummarize if mpg 20

if mpg is greater than or equal to 20 but see below if mpg has missing data

If mpg has missing data the if exp for and should be written as below if you want the missing values to be excluded

s ummarize if mpg 20 mpg

if mpg is not 20 and mpg is not missing
s ummarize if mpg 20 mpg

if mpg is greater than 20 and mpg is not missing
s ummarize if mpg 20 mpg

if mpg is greater than or equal to 20 and mpg is not missing The if exp can be complex using and to join parts together
s ummarize if foreign 1 mpg 30

summarize if foreign is 1 AND mpg under 30
s ummarize if foreign 1 mpg 30

summarize if foreign is 1 OR mpg under 30

Reading Data into Stata USING AND SAVING STATA DATA FILES
u se auto

use the file called auto dta in the current directory
c lear

clears out the existing data in memory
u se auto clear

clears out the existing data in memory then uses the file auto dta
s ave auto2

saves the data currently in memory to the file called auto2 dta
s ave auto2 replace

saves the data currently in memory to the file called auto2 dta and replaces the file if it currently exists
s et memory 2m

allocates 2 megabytes of memory for data in memory permitting you to use a file up to 2 megabytes in size

INPUTTING DATA USING THE STATA DATA EDITOR Here are steps you can follow for using the Stata data editor
c lear

Clears out any existing data
e dit

Starts the Stata data editor Once in the editor Type in variables for the first observation use tab to move across variables Double click each column to supply a variable name and label Enter all the rest of your data When you are done click the X in the top right of the window to close the window
s ave mydata

Saves the data from the data editor in a file called mydata dta INPUTTING DATA INTO STATA
i nsheet using auto2 raw

reads in the comma or tab delimited file called auto2 raw taking the variable names from the first line of data
i nsheet make mpg weight price using auto3 raw

reads in the comma or tab delimited file called auto3 raw naming the variables mpg weight and price
i nfile str15 make mpg weight price using auto4 raw

reads in the space separated file named auto4 raw The variable make should be surrounded by quotes if it has embedded blanks
i nfix str make 1 13 mpg 15 16 weight 18 21 price 23 26 using auto5 raw

reads in the fixed format file named auto5 raw
R EADING DATES INTO STATA AND USING DATE VARIABLES U nder Construction

Basic Data Management in Stata LABELING DATA VARIABLES VALUES
l abel data 1978 auto data

assign a label to the datafile currently in memory
l abel variable foreign origin of car foreign or domestic

assign a label to the variable foreign
l abel define labfor 0 domestic car 1 foreign car l abel values foreign labfor

create the value label labfor and assign it to the variable foreign

CREATING AND RECODING VARIABLES
g enerate len ft length 12

Create a new variable len ft which is length divided by 12
r eplace len ft length 12

Change values of an existing variable named len ft
g enerate weight3 r eplace weight3 1 if weight 2640 r eplace weight3 2 if weight 2641 weight 3370 r eplace weight3 3 if weight 3371 weight

recode weight into weight3 having 3 categories 1 2 3 using replace if
g enerate weight3a weight r ecode weight3a min 2650 1 2651 3370 2 3371 max 3

Recode weight into weight3a having 3 categories 1 2 3 using generate and recode
g enerate weightfd weight r ecode weightfd min 3360 0 3361 max 1 if foreign 0 r ecode weightfd min 2180 0 2181 max 1 if foreign 1

recode weight into weightfd having 2 categories but using different cutoffs for foreign and domestic cars SUBSETTING VARIABLES AND OBSERVATIONS
k eep make mpg price

keeps just the variables make mpg and price for the data file in memory
d rop displ gratio

drops the variables displ and gratio for the data file in memory
d rop if rep78

drops observations where the variable rep78 is missing for the data file in memory
k eep if rep78 3

keeps observations where the variable rep78 is less than or equal to 3 for the data file in memory
u se make mpg price using auto

uses the file auto and reads in only the variables make mpg and price
u se auto if rep78 3

uses the file auto and reads in only the observations where rep78 is 3 or less
u se make mpg price rep78 using auto if rep78 3

uses the file auto and reads in only the variables make mpg price rep78 and only the observations where the variable rep78 is 3 or less

A dvanced Data Management in Stata C OLLAPSING DATA ACROSS OBSERVATIONS c ollapse age by famid

Creates one record per family famid with the average of age within each family
c ollapse mean avgage age avgwt wt by famid

Creates one record per family famid with the average of age called avgage and average wt called avgwt within each family
collapse mean avgage age avgwt wt count numkids age by famid

Same as above example but also counts the number of kids within each family calling that numkids Assumes age is not missing
t abulate sex generate sexdum c ollapse sum girls sexdum1 boys sexdum2 by famid

Counts the number of boys and girls in each family by using tabulate to create dummy variables based on sex and then summing the dummy variables within each family

C OMBINING STATA DATA FILES u se dads clear a ppend using moms

Appends the dads moms files together by stacking them one atop the other
u se dads clear s ort famid s ave dads replace u se faminc clear s ort famid s ave faminc replace u se dads clear m erge famid using faminc

Match merges the dads file with the faminc file on famid The four steps are 1 sort dads on famid and save that file 2 sort kids on famid and save that file 3 use the dads file 4 merge the dads file with the kids file using famid to match them

R ESHAPING DATA FROM WIDE TO LONG W ide format O bs famid 1 1 2 2 3 3

faminc96 40000 45000 75000

faminc97 40500 45400 76000

faminc98 41000 45800 77000

Long Format Obs famid 1 1 2 1 3 1 4 2 5 2 6 2 7 3 8 3 9 3

year 96 97 98 96 97 98 96 97 98

faminc 40000 40500 41000 45000 45400 45800 75000 76000 77000

r eshape long faminc i famid j year

Changes the data from wide format with one record per famid to one record for every year 96 97 98 for every famid
T he general syntax of reshape long can be expressed as r eshape long stem of wide vars i wide id var j var for suffix

where stem of wide vars is the stem of the wide variables e g faminc wide id var is the variable that uniquely identifies wide observations e g famid var for suffix is the variable that will contain the suffix of the wide variables e g year
R ESHAPING DATA FROM LONG TO WIDE L ong format O bs famid 1 1 2 1 3 1 4 2 5 2 6 2 7 3 8 3 9 3

birth 1 2 3 1 2 3 1 2 3

age 9 6 3 8 6 2 6 4 2

W ide format O bs famid 1 1 2 2 3 3

age1

age2 9 8 6

age3 6 6 4

3 2 2

r eshape wide age j birth i famid

Changes the data from long format with one record per kid to long format with one record per famid The general syntax of reshape wide can be expressed as
r eshape wide long var s i wide id var j var with suffix

where long vars is the name of the long variable s to be made wide e g age wide id var is the variable that uniquely identifies wide observations e g famid var with suffix is the variable from the long file that contains the suffix for the wide variables e g age

Other MAKING AND RUNNING DO FILES
d o test1 do

executes the stata commands in test1 do displays output
r un test1 do

executes the stata commands in test1 do but displays no output
d oedit test1 do

brings up do file editor with test1 do in it
d oedit

brings up do file editor with an empty file

A typical do file say it is called test do may look like this
l og using test log replace

do file commands here
l og close

Stata Class Notes
Create and Modify Variables
1 0 Stata commands in this unit
generate replace recode egen

2 0 Demonstration and Explanation 2 1 Create and modify variables
use hsb2 clear generate total read write summarize total replace total total 2 math summarize total generate sex gender tabulate sex recode sex 1 0 2 1 tabulate sex The generate command allows you to create new variables The replace command allows you to change an existing variable The recode command allows you the change specific values of the variables

2 2 Egen
egen zread std read standard scores for read list read zread summarize read zread egen rmean mean read by ses mean read for each ses list ses read rmean egen mread median read by prog median read for each prog list prog read mread egen rread rank read rank for read list read rread egen stands for extended generate and is an extremely powerful command that has many options for creating new variables Only a few of these options are demonstrated above Here is a list of some of the other options

Egen Functions
count diff fill group iqr ma max mean median min pctile rank rmean sd std sum number of non missing vlaues compares variables 1 if different 0 otherwise fill with a pattern creates a group id from a list of variables interquartile range moving average maximum value mean median minimum value percentile rank mean across variables standard deviation standard scores sums

3 0 Try the commands on your own
generate tot read write math summarize tot replace tot read math science summarize tot generate newprog prog recode newprog 1 3 2 2 1 tabulate nprog egen aread mean read by prog list prog read aread 6 0 Web Notes The Stata Class Notes are available on the World Wide Web by visiting http www oac ucla edu training stata notes The dataset hsb2 dta can be loaded directly into Stata over the Internet using the following commands use http www oac ucla edu training stata notes hsb2 10 Jun 1999 pbe updated 06 24 99

Stata Class Notes
Creating Your Own Datasets
1 0 Stata commands in this unit
By now you know that we will not be showing all of the options for any of the commands clear edit save infile insheet

2 0 Demonstration and Explanation
clear The clear command clears out the dataset that is currently in memory We need to do this before we can create or read a new dataset

2 1 A small dataset
name Smith Jones Brown Adraktas midterm 79 87 91 80 final 84 86 94 84

2 2 Creating a dataset using the Data Editor
edit The edit command opens up a spreadsheet like window in which you can enter and change data You can also get to the Data Editor from the pulldown Window menu or by clicking on the Data Editor icon on the tool bar Enter values and press return Double click on the column head and you can change the name of the variables When you are done click the close box for the Data Editor window

save grades save grades replace The save command will save the dataset as grades dta Editing the dataset changes data in the computer s memory it does not change the data that is stored on the computer s disk The replace option allows you to save a changed file to the disk replacing the original file list summarize Let s list the contents and run some statistics on the new data set

2 3 Creating a dataset from an ASCII file
clear type ascii raw infile str10 name midterm final using ascii raw list The infile command is used to read data from an external ascci file The names of the variables are given followed by the keyword using which in turn is followed by the name of the file str10 is not a variable name but indicates that name is a string variable up to 10 characters long The ASCII file called ascii raw that looks like this
Smith Jones Brown Adraktas 79 87 91 80 84 86 94 84

2 4 Creating a dataset from a spreadsheet or database
clear type spread raw insheet using spread raw list The insheet command is used to read data from a file created by a spreadsheet or database program The values in the file must be either comma or tab delimited The names are included in the file The spreadsheet file called spread raw that looks like this
n ame midterm final Smith 79 84 Jones 87 86 Brown 91 94 Adraktas 80 84

3 0 Try the commands on your own
clear edit save grades save grades replace list summarize clear infile str10 name midterm final using ascii raw clear insheet using spread raw

Stata Class Notes
Let s Get Organized
1 0 Stata commands in this unit
order rename label data label variable label define label values replace recode note notes save replace

2 0 Demonstration and Explanation
Let s begin by using a new data set schdat dta it looks like this
id 1 2 3 4 5 6 7 8 9 10 a1 25 23 17 19 21 24 16 21 19 18 t1 48 46 40 40 41 47 35 43 39 38 gender 1 2 1 1 2 2 2 1 2 1 a2 24 21 21 19 24 22 19 23 20 20 t2 tgender 95 1 94 1 84 1 87 0 83 0 96 0 76 0 82 1 78 1 80 1

cd A statacls use schdat clear describe The describe tells us the names of the variables but doesn t provide much more information Here s the scoop on the data a1 and a2 are scores on two assignments t1 and t2 are the scores on the midterm and final respectively and for gender 1 s are males and 2 s are females The variable tgender is the gender of the teacher and is scored 0 for male and 1 for female None of this is obvious from looking at the data so let s get organized

2 1 ordering rename
order id gender tgender a1 a2 t1 t2 rename a1 assign1 rename a2 assign2 rename t1 midterm rename t2 final The order command changes the order of the varibles The four rename commands change the names of some of the variables to more meaningful ones This is a good start but we really need to add some labels to make things clear

2 2 Some labels
label data Fall 1999 Stat 100 Scores label variable gender student gender label variable tgender teacher gender generate total assign1 assign2 midterm final replace total total 2 label variable total total score describe The label data command places a label on the whole dataset The label variable command makes labels that help explain individual variables replace replaces the value of total with the value of total 2 Next we need to get gender and tgender scored that same and assign value labels

2 3 recode more labels
Let s recode both gender and tgender so that 1 is male and 0 is female recode gender 2 0 recode tgender 0 1 1 0 label define sex 1 male 0 female label values gender sex label values tgender sex describe tab1 gender tgender t ab1 gender tgender nolabel The recode command allows us to code both gender and tgender the same way The label define command creates a definition for the values 0 and 1 called sex The label values command connects the values defined for sex with the values in gender and tgender

2 4 Make a note of this
note gender is self report note the final was a take home exam notes save schdat2 replace use schdat2 clear The note note the colon command allows you to place notes into the dataset The command notes displays the notes The save replace saves the dataset as schdat dta replacing the previous version

3 0 Try the commands on your own
cd A statacls use schdat clear describe order id gender tgender a1 a2 t1 t2 rename a1 assign1 rename a2 assign2 rename t1 midterm rename t2 final label data Fall 1999 Stat 100 Scores label variable gender student gender label variable tgender teacher gender generate total assign1 assign2 midterm final label variable total total score recode gender 2 0 recode tgender 0 1 1 0 label define sex 1 male 2 female label values gender sex label values tgender sex describe codebook gender tgender note gender is self report note the final was a take home exam notes save schdat2
22 Jun 1999 pbe