1. Stata Workshop #1 Chiu-Hsieh (Paul) Hsu Associate Professor College of Public Health pchhsu@email.arizona.edu

2. Outline Do files Data entry Data management Data description Estimation: Confidence Interval Hypothesis testing

3. Do files Stata programs –Easy to add or skip comments –One click/command can run the whole program Reproducible –Don’t need to retype all of the commands Interactive work vs. do files

4. Data Entry

5. Stata Commands 1.cd: Change directory 2.dir or ls: Show files in current directory 3.insheet: Read ASCII (text) data created by a spreadsheet 4.infile: Read unformatted ASCII (text) data 5.infix: Read ASCII (text) data in fixed format 6.input: Enter data from keyboard 7.save: Store the dataset currently in memory on disk in Stata data format 8.use: Load a Stata-format dataset 9.count: Show the number of observations 10.list: List values of variables 11.clear: Clear the entire dataset and everything else 12.memory: Display a report on memory usage 13.set memory:Set the size of memory

6. Ways to enter data Change the directory to the folder you like cd c:\Stata Common separated values (.csv) format files insheet using test.csv,clear (with variable names) infile gender id race ses schtyp str10 prgtype read write math science socst using hs0.raw, clear (without variable names) Stata (.dta) files use test Type in data one by one input id female race ses str3 schtype prog read write math science socst End (when you are done) What’s in the dataset? describe list

7. Data Management

8. Stata Commands 1.pwd: show: current directory (pwd=print working directory) 2.keep if: keep observations if condition is met 3.Keep: keep variables or observations 4.drop: drop variables or observations 5.append: append a data file to current file 6.sort: sort observations 7.merge: merge a data file with current file 8.codebook: show codebook information for file 9.label data: apply a label to a data set 10.order: order the variables in a data set 11.label variable: apply a label to a variable 12.label define: define a set of a labels for the levels of a categorical variable 13.label values: apply value labels to a variable 14.encode: create numeric version of a string variable 15.rename a variable 16.recode: recode the values of a variable 17.notes: apply notes to the data file 18.generate: creates a new variable 19.replace: replaces one value with another value 20.egen: extended generate - has special functions that can be used when creating a new variable

9. Merging two datasets test1 and test2 have the same variables but different subjects use test1 append using test2 save test12 test3 and test4 have the same subjects and only share a link variable, e.g. ID use test3, clear sort id save test3,replace use test4, clear sort id save test4,replace use test3 merge id using test4 save test34

10. Play with Variables use test label variable gender "Male" rename gender male gen female=1-male order id male female encode prgtype, gen(prog) codebook prog keep if female==1 (delete male) drop female

11. Dummy Variables A categorical variable with K possible levels Need K-1 dummy variables (one as the reference) Dummy variables are convenient for regression analysis How to create dummy variables? Use generate command –gen female=1-gender Use tabulate command –tabulate gender, gen(male) Use factor variables –xi i.gender –list,clean

12. Data Description

13. Stata Commands 1.describe: describe a dataset 2.log: create a log file 3.summarize: descriptive statistics 4.tabstat: table of descriptive statistics 5.table: create a table of statistics 6.stem: stem-and-leaf plot 7.graph: high resolution graphs 8.kdensity: kernel density plot 9.histogram: histogram for continuous and categorical variables 10.tabulate: one- and two-way frequency tables 11.correlate: correlations 12.pwcorr: pairwise correlations

14. Example: raw data log using test.txt, text replace use lead describe sum maxfwt, detail histogram maxfwt, by(Group) normal graph box maxfwt, by(Group) stem maxfwt kdensity maxfwt tab Group sex cor ageyrs maxfwt,sig cor ageyrs maxfwt if sex==1 (male only),sig pwcorr ageyrs maxfwt fwt_r,sig log close

15. Example: grouped data use group (a grouped dataset) sum age [fweight=freq],detail hist age [fweight=freq] Pretty much the same as raw data. Just need to specify the weight.

16. Some Review Use both location and spread measures to summarize a dataset Mean, standard deviation and range are easily affected by extreme observations Median and inter-quartile range are less affected by extreme observations Coefficient of variation (standard deviation divided by mean) removes the scale effect.

17. Estimation

18. Estimation of Parameters Binomial distribution –Parameters n (usually known) and p –How to estimate p? Poisson distribution –Parameter λ –How to estimate λ? Normal distribution –Parameters µ and σ 2 –How to estimate µ and σ 2 ? –σ 2 unknown  t distribution

19. Stata Commands Raw data –ci [varlist] [if] [in] [weight] [, options] confidence intervals for mean, proportion (b) and count (p) Summarry statistics –cii #obs #mean #sd [, ciin_option] Normal –cii #obs #succ [, ciib_options] Binomial

20. Examples gen female=sex-1 tab female Group What’s the average maxfwt for females in the exposed group? –ci maxfwt if female==1 & Group==2 (raw data) –sum maxfwt if female==1 & Group==2 –cii 16 59 20.887,level(95) (summary statistics) What’s the proportion of females in the exposed group? –gen expose=Group-1 –ci expose if female==1,b –cii 48 16,level(95)

21. Hypothesis Testing

22. Stata Commands (mean) ttest –Raw data ttest varname == # [if] [in] [, level(#)] ttest varname1 == varname2 [if] [in], unpaired [unequal welch level(#)] ttest varname1 == varname2 [if] [in] [, level(#)] ttest varname [if] [in], by(groupvar) [options1] –Summarry statistics ttesti #obs #mean #sd #val [, level(#)] ttesti #obs1 #mean1 #sd1 #obs2 #mean2 #sd2 [, options2]

23. Examples One sample –Is the average maxfwt for females in the exposed group significantly lower than 45? ttest maxfwt==45 if female==1 & Group==2 ttesti 16 59 20.887 45 (summary statistics) Two samples –Do females have a higher average maxfwt than males in the exposed group? ttest maxfwt if Group==2, by(female) sum maxfwt if female==0 & Group==2 ttesti 16 59 20.887 30 60.167 27.28

24. Stata Commands (variance) sdtest –Raw data sdtest varname == # [if] [in] [, level(#)] sdtest varname1 == varname2 [if] [in] [, level(#)] sdtest varname [if] [in], by(groupvar) [level(#)] –Summarry statistics sdtesti #obs {#mean |. } #sd #val [, level(#)] sdtesti #obs1 {#mean1 |. } #sd1 #obs2 {#mean2 |. } #sd2 [, level(#)]

25. Examples One sample –Is the variance of maxfwt for females in the exposed group significantly greater than 100? sdtest maxfwt==10 if female==1 & Group==2 sdtesti 16 59 20.887 10 (summary statistics) Two samples –Do females have a greater variation in maxfwt than males in the exposed group? sdtest maxfwt if Group==2, by(female) sum maxfwt if female==0 & Group==2 sdtesti 16 59 20.887 30 60.167 27.28

26. Stata Commands (proportion) prtest –Raw data prtest varname == #p [if] [in] [, level(#)] prtest varname1 == varname2 [if] [in] [, level(#)] prtest varname [if] [in], by(groupvar) [level(#)] –Summarry statistics prtesti #obs1 #p1 #p2 [, level(#) count] prtesti #obs1 #p1 #obs2 #p2 [, level(#) count]

27. Examples One sample –Is it more than 50% of females in the exposed group? prtest expose==0.5 if female==1 prtesti 48 0.3333333 0.5 Two samples –Are there more females in the exposed group than the control group? prtest female, by(expose) tab expose female, r prtesti 78 0.4103 46 0.3478

28. Power and Sample Size

29. Stata Command (sample size) One sample –continuous sampsi μ 0 μ 1, sd(.) p(.) a(.) onesam sampsi 3500 3800, sd(420) p(.9) onesam –Binary proportions sampsi p0 p1, p(.) onesam sampsi 0.4 0.25, p(0.9) onesam Two samples –continuous sampsi μ 1 μ 2, p(.) sd1(.) sd2(.) a(.) sampsi 132.86 127.44, p(0.8) sd1(15.34) sd2(18.23) –Binary proportions sampsi p1 p2, p(.) sampsi 0.4 0.25, p(0.9)

30. Stata Command (power) One sample –continuous sampsi μ 0 μ 1, sd(.) n(.) a(.) onesam sampsi 84.4 90.1, sd(10.3) n(5) onesam onesided –Binomial proportion sampsi p 0 p 1, n1(.) onesam sampsi 0.25 0.4, n1(100) onesam Two samples –continuous sampsi μ 1 μ 2, n1(.) n2(.) sd1(.) sd2(.) a(.) sampsi 9 14, n1(100) n2(100) sd1(15.34) sd2(18.23) –Binomial proportions sampsi p1 p2, n1(.) n2(.) sampsi 0.4 0.25, n1(100) n2(150)

31. Useful links http://www.ats.ucla.edu/stat/stata/ Once the D2L site is created, all of the handouts and related materials will be posted on the D2L site.