SEM data

Data used in Structural equation models: An open textbook

These data sets are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

cards2.data.txt: Simulated from a study of income and credit card debt. For a sample of real estate agents in different towns and cities, record
- HomePrice: Median price of resale home, in 100k units.
- Income: Income last year after taxes, in thousands.
- CardDebt: Credit card balance carried forward last month, in thousands.
Babydouble.data.txt: Simulated W₁, W₂, Y for an easy double measurement regression example.
bmi.data.txt: A constructed data set illiustrating the advantages of the double measurement design. Health-related information was obtained from participants independently, in two sets of measurements. Except for cholesterol level, the measurements in set 2 are of generally higher quality. Variables are
- age1: Self reported age in years
- bmi1: Body mass index based on measurements in the doctor's office.
- fat1: Estimated percent body fat using tape and calipers, Dr. Office
- cholest1: Cholesterol level from a blood sample, assessed in Lab 1
- diastol1: Diastolic blood pressure, using blood pressure cuff, Dr. office
- age2: Age from passport or birth certificate
- bmi2: Body mass index based on measurements by a lab technician, no shoes, gown
- fat2: Estimated percent body fat, submerge in water tank
- cholest2: Cholesterol level from a blood sample, assessed in Lab 2
- diastol2: Diastolic blood pressure assessed by digital readout, mostly automatic.
openpigs.data.txt: Reconstructed data based on a data set in Fuller's Measurement error models.
- Col 1: Farm
- Col 2: Number breeding sows on farm June 1: Questionnaire One
- Col 3: Number sows giving birth June 1 to Aug. 31: Questionnaire One
- Col 4: Number breeding sows on farm June 1: Questionnaire Two
- Col 5: Number sows giving birth June 1 to Aug. 31: Questionnaire Two
timmy1.data.txt: A major Canadian coffee shop chain is trying to break into the U.S. Market. They assess the following variables twice on a random sample of coffee-drinking adults. The two measurements of each variable are conducted at different times by different interviewers asking somewhat different questions, in such a way that the errors of measurement may be assumed independent. The variables are
- Brand Awareness: Familiarity with the coffee shop chain
- Advertising Awareness: Recall for advertising of the coffee shop chain
- Interest in the product category: Mostly this was how much they say they like doughnuts.
- Purchase Intention: Expressed willingness to go to an outlet of the coffeeshop chain and make an order.
- Purchase behaviour: Reported dollars spent at the chain during the 2 months following the interview.
All variables were measured on a scale from 0 to 100 except purchase behaviour, which is in dollars. This is a constructed data set.
```
           w1 = Brand Awareness 1
           w2 = Brand Awareness 2
           w3 = Ad Awareness 1
           w4 = Ad Awareness 2
           w5 = Interest 1
           w6 = Interest 2
           v1 = Purchase Intention 1
           v2 = Purchase Intention 2
           v3 = Purchase Behaviour 1
           v4 = Purchase Behaviour 2;
```
bodymind.data.txt: Educational test scores and physical measurements for a sample of high school students. This is a modified subset of data reported in the journal Human Biology. The reference is
Clark, P. J., Vandenberg, S. G., and Proctor, C. H. (1961), On the relationship of scores on certain psychological tests with a number of anthropometric characters and birth order in twins, Human Biology, 33, 163-180.
The data are used without permission, but I believe they have been modified enough so that the original copyright no longer applies, and they can be protected under a Creative Commons license. The variables are
- sex: F or M
- progmat: Progressive matrices (puzzle) score
- reason: Reasoning score
- verbal: Verbal (reading and vocabulary) score
- headlng: Head Length in mm
- headbrd: Head Breadth in mm
- headcir: Head Circumference in mm
- bizyg: Bizygomatic breadth in mm, basically how far apart the eyes are.
- weight: In pounds
- height: In cm.
norm1.data.txt: Simulated normal data with mean equal to standard deviation = θ. Used in Appendix A to illustrate numerical maximum likelihood.
mystery2.data.txt: Simulated data for numerical maximum likelihood. The density is
beta24.data.txt: Simulated data for numerical maximum likelihood. The density is beta, with α = 2 and β = 4. The sample size is n= 500.
openSAT.data.txt: In the United States, admission to university is sometimes based partly on the Scholastic Aptitude Test, or SAT. In the old days there were two subtests, Verbal and Math. This data set has Verbal score, Math score and first-year grade point average for a sample of 200 students. It is a reconstructed data set based on a Minitab data set. I believe the Minitab data set is a cleaned-up version of real data from Penn State University.
CancerTax.xlsx: An Excel spreadsheet with Unite States state-level information on a set of variables related to smoking and lung cancer. The variables are
- Population density
- Per capita income in 2023
- Tax on a pack of cigarettes: 2023
- Age-adjusted rate of new Lung and Bronchus cancers, 2019
- Age-adjusted rate of new Brain and other nervous system cancers
The sources are
- Income from https://worldpopulationreview.com/state-rankings/per-capita-income-by-state
- Cancer from https://gis.cdc.gov/Cancer/USCS/#/AtAGlance/
- Tax: https://igentax.com/cigarette-tax-state/
- Population density from cancer website
Cigarette tax is demonstrably a bogus instrumental variable, because it's correlated with the potentially "omitted" variable income -- part of a whole complex of political and soci-cultural factors that include taxation and lifestyle.
CancerTax2.data.txt: Subset of CancerTax.xlsx, with just tax, smoking rate, lung cancer and brain cancer. This is a plain text data file.
statclass.data.txt: Quiz scores, Computer assignment scores, Midterm score and Final Exam score from a statistics class, long ago. The "S" column is my guess at sex: 1=F, 0=M. The "E" column is my guess at ethnicity: 1=Asian, 2-European, 3=Other.
co-opManager.data.txt: A farm co-operative (co-op) is an association of farmers. The co-op can buy fertilizer and other suppies in large quantities for a lower price, it often provides a common storage location for harvested crops, and it arranges sale of farm products in large quantities to grocery store chains and other food suppliers. Farm co-ops usually have professional managers, and some do a better job than others.
We have data from a study of farm co-op managers. The variables in the ``latent variable" part of the model are the following, but note that one of them is assumed observable.
- Knowledge of business principles and products (economics, fertilizers and chemicals). This is a latent variable measured by know1 and know2
- Profit-loss orientation (``Tendency to rationally evaluate means to an economic end"). This is a latent variable measured by ploss1 and ploss2.
- Job satisfaction. This is a latent variable measured by sat1 and sat2.
- Formal Education This is an observable variable, assumed to be measured without error.
- Job performance. This is a latent variable measured by perf1 and perf2.
The data file has these observable variables in addition to an identification code for the managers:
- know1: Knowledge measurement 1
- know2: Knowledge measurement 2
- ploss1: Profit-Loss Orientation 1
- ploss2: Profit-Loss Orientation 2
- sat1: Job Satisfaction 1
- sat2: Job Satisfaction 2
- educat: Number of years of formal schooling divided by 6.
- perf1: Job Performance 1
- perf2: Job Performance 2
In this study, the double measurements are obtained by just splitting questionnaires in two, as in split half reliability.
This is a reconstructed data set based on a covariance matrix in Jorekog (1978, p. 465). Joreskog got it from Warren, White and Fuller (1974).