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Teaching with Stata. Peter A. Lachenbruch & Alan C. Acock Oregon State University peter.lachenbruch@oregonstate.edu alan.acock@oregonstate.edu. First Course Requirement—Data Entry. I want a first course to be able to do the things I want students to do:

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teaching with stata

Teaching with Stata

Peter A. Lachenbruch

&

Alan C. Acock

Oregon State University

peter.lachenbruch@oregonstate.edu

alan.acock@oregonstate.edu

first course requirement data entry
First Course Requirement—Data Entry
  • I want a first course to be able to do the things I want students to do:
    • Enter and edit data--must be “want to know topic”
    • Students can do a small survey to get data on topics of interest to them.
      • Voter poll
      • Attitudes toward diversity issues on campus
      • Beliefs about regulating the internet
    • Learn how to create a codebook, use codebookandcodebook, compact
  • Where possible use “real” data

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first course requirement data management
First Course Requirement—Data Management
  • Balance statistical content with proper data management content—hard decision
  • Storing original dataset and creating a working dataset
  • Keeping a record of every data modification they make using do-file
    • Menu system is an aid
    • Do-files are the requirement
  • Missing values--distinguish types
  • Variable names, labels, and value labels

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first course requirements data management
First Course Requirements—Data Management
  • Transformations – log, , exp
  • Logical editing – beware of logical transformations when missing values are present (gen y = x < 10 leads to “.” transforming to 0)
  • Appending
    • Append student generated datasets
  • Merging
    • Merging two waves of data

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first course requirements data management5
First Course Requirements—Data Management
  • Constructing Measures
    • When to use egen newvar =rowtotal(var1, var2, var3)
    • When to use egen newvar =rowmean(var1, var2, var3)
    • When to use misschk command, what it does
  • Suppose the variable category is 0 or 1
  • If there are missing values in category, there is a difference between
    • gen y = 1 if category
    • gen y = 1 if (category==1)
    • gen y = 1 if (category>0)
    • The first and third will give scores of 1 for missing values. The second will give a score of 0 for missing values - BEWARE

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first course requirements data management6
First Course Requirements—Data Management
  • edit command, insheet input, infile(csv files)
  • gen newvar = ln(oldvar)
  • Rarely use replace oldvar = sqrt(oldvar) – only when correcting an error – don’t replace data
  • merge ptid assessment using file, update (need for data to be sorted)

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first course requirement 2
First Course Requirement (2)
  • Data presentation, numerical summary measures – summarize, detail; list; browse; edit; describe; codebook; codebook, compact
  • Graphic presentation--bar chart, histogram, box plot seem minimum
  • Probability computations – binomial, binomialtail, chi2, chi2tail, F, Ftail, normal – use of the inverse functions for these.

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examples
Examples
  • summarize sp,detail; list sp; describe s*; codebook s*
  • display binomial(10,3,0.1) for cumulative or display Binomial(10,3,.1) for reverse cumulative; Note disp 1-binomial(10,2,.1) gives the same result (also binomialtail(10,3,.1)
  • display normal(1.2)
  • gen y = invnormal(uniform())*5+20

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first course requirement 3
First Course Requirement (3)
  • Confidence intervals
    • Binomial – ci—ci variable
    • Normal – ci—ci variable
    • Poisson – ci—ci variable, poisson
  • Percentiles –
    • summarize,d
    • centile price, c(10(10)90)

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examples10
Examples
  • cii 20 4;
    • cii 20 4, agresti
      • Sometimes we want to use the Agresti formulation. The exact is usually preferable
  • ci varname, level(99)
  • summarize weakness, detail
    • Can use su weakn,d (i.e. abbreviate commands, options and variables)
  • centile weakness,c(20,40,60,80)
    • Or centile weakness,c(20(20)80)

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first course requirements 4
First Course Requirements (4)
  • Hypothesis Testing:
    • Normal r.v.s
      • One sample (including paired data) -
      • Two sample - ttest
      • K samples – ANOVA
    • Binomial variables
      • One sample – proportion
      • Two samples – tabulate, chi2

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examples12
Examples
  • ttest sp = 120 [one-sample]
  • ttest spmen = spfem [paired]
  • ttest spmen = spfem, unpaired unequal welch
  • ttest sp, by(sex) [unequal welch etc.]
  • Also immediate form – see help
  • anova sp agegrp

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examples13
Examples
  • bitest success = 0.8[one sample binomial]
  • tabulate success group, chi2 row col
  • prtest success, by(group)[two sample binomial]

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first course requirements 5
First Course Requirements (5)
  • Hypothesis Testing (cont.)
    • Power considerations – sampsi (or spreadsheet – nice exercise for some good ones)
    • Nonparametric methods – sign, signrank, ranksum
  • Contingency tables – tabulate, epitab

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examples15
Examples
  • sampsi 132.86 127.44, p(0.8) r(2) sd1(15.34) sd2(18.23)
  • ranksum sp, by(survive)
  • signrank before = after
  • When should we supplement Stata with other software such as G*power 3 that is free and more flexible than sampsi or other software such as PASS or nQuery Advisor?

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first course requirements 6
First Course Requirements (6)
  • Simple linear regression – regress, rvfplot, other diagnostics
  • Correlation – corr, spearman, ktau – I tend not to use corr because of the sensitivity to the normality assumption for tests and confidence intervals
  • Only pwcorr and not corr provide test of significance

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examples17
Examples
  • regress mpg weight
  • rvfplot
  • Stata’s “type a little, get a little” very different from other packages
  • correlate mpg weight or pwcorr mpg weight (especially when you have more than 2 variables – can specify sig and obs—Note that these only work with pwcorr)
  • spearman mpg weight – would be nice to have Stata produce a Spearman correlation matrix

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examples18
Examples
  • It’s easy to use permutation tests

. permute anyhcq t=r(t):ttest ald7 if adult==1 & assnum==1,by(anyhcq) (running ttest on estimation sample)

Monte Carlo permutation results Number of obs = 97

command: ttest ald7, by(anyhcq)

t: r(t)

permute var: anyhcq

---------------------------------------------------------------------------

T | T(obs) c n p=c/n SE(p) [95% Conf. Interval]

-------------+-------------------------------------------------------------

t | 1.648305 13 100 0.1300 0.0336 .071073 .2120407

---------------------------------------------------------------------------

Note: confidence interval is with respect to p=c/n.

Note: c = #{|T| >= |T(obs)|}

  • One can do similar things with the bootstrap
  • These are easy to use and intuitive for students

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use of stata in the classroom
Use of Stata in the Classroom
  • Use Stata sparingly
    • It’s not easy to follow commands typed or used from menus – students will get confused
    • Have handouts of what you do – make spacing large enough that students can annotate – even if only to write nasty things about the instructor
    • Balancing coverage of Stata, e.g. data management with coverage of Statistics is a constant issue
    • Remember – it’s a course in statistics, not in Stata

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data sets
Data Sets
  • Place data sets on a LAN or common drive or available for copying to flash drive or CD
  • Use real data
    • Not too many variables
    • May have missing values – but should not affect main analyses – unless you want to demonstrate the problems with missing values

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in the classroom
In the Classroom
  • Using CD rather than flash drive is better(?)
    • Many desktops have USB port located inconveniently (darn you Dell!)
    • Sometimes newer PCs have USB port on monitor, and laptops usually have an easy slot for the flash drive
    • Light level in the room should allow students to read easily
    • Days of dim projectors are over

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in the classroom 2
In the Classroom (2)
  • Enlarge the Stata font by using right mouse button
    • I have found that 14 point is pretty good
    • Be careful about wraparound of output – if needed, reduce point size temporarily
    • Don’t ever use red on blue font
    • See what I mean? It’s more difficult to read
  • Show how to move and fix windows

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in the classroom 223
In the Classroom (2)
  • Optimizing visibility with projector
    • Use rich color background
    • EditPreferencesGeneral preferences. Blue background option good but it relies on red for errors, green for Standard text, and doesn’t bold fonts.
    • Custom may be better because you can make fonts bold and pick colors that do not disadvantage students who are colorblind.

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virtual lab
Virtual Lab
  • A server supporting 30 simultaneous sessions of Stata is remarkably inexpensive.
  • A department can require students to have laptops or provide a cart with enough laptops
  • Because laptops are really “dumb” terminals with server, the laptops can be cheap and not updated very often
  • Any room becomes a lab
  • Students should have 24/7 access to the server

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handouts and data sets
Handouts and Data Sets
  • Have handouts of your lecture notes
  • Have handouts of your data analysis demonstrations
    • Include commands as well as output!
  • Data sets
    • On line – LAN or CD or Floppy disk --Lots of laptops don’t have floppy drives any more, flash drives are inexpensive
  • Include
    • Student generated datasets
    • Datasets with large Ns and relatively few variables

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emphasis in course
Emphasis in Course
  • Lectures devoted to statistics
  • Labs to learning Stata and working on homework and discussion
  • Proper printing of output
    • Don’t split output between two pages if possible (at least, find a good break point)
    • Always use a monotype font (such as Courier New)

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some final issues
Some Final Issues
  • Multiple testing can distort inference (i.e. doing 100 tests guarantees some significant results – but they may be meaningless) – Worry about this
  • Controlling the digits in the output. Use outreg, estout, esttab

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the end
The End

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