The name is absent



Stata Technical Bulletin

11


. tab married, summ(mhiθ)

I
married

Summary of Baseline Mental Health

Mean

Std. Dev.

Freq.

0 I

65.238349

23.932533

751

i I

71.337259

21.6785

1104

____________ ^-_

Total I

68.868105

22.809219

1855

. tab married, summ(mhi3mo)

I Summary of Mental Health at 3 months

married

Mean

Std. Dev.

Freq.

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

0 I

68.699844

22.002264

1376

1 I

73.239149

20.31232

1968

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

Total I

71.371301

21.139313

3344

. tab married,

. summ(mhilyr)

I
married

Summary of Mental Health at
Mean Std. Dev.

1 year
Freq.

0 I

69.756461

21.821531

761

i I

74.669736

18.927619

1086

———————————-⅛-

Total I

72.645371

20.309153

1847

. tab married,

, summ(mhi2yr)

I
married

Summary of Mental Health
Mean Std. Dev.

at 2

years
Freq.

0 I

70.728295

21.383028

741

i I

74.994872

19.236051

1040

———————————-⅛-

Total I

73.219727

20.26075

1781

. tab married,

, summ(mhi4yr)

I
married

Summary of Mental Health
Mean Std. Dev.

at 4

years
Freq.

0 I

72.62061

21.52095

579

i I

76.28414

18.028531

847

———————————-⅛-

Total I

74.796634

19.597544

1426

The first measurement (mhiθ) is available on a random half of the sample only, but mhi3mo is available on just about everybody.
Between 3 months and one year, approximately half of the sample was discarded by the original investigators. There is additional
attrition during the follow-up period.

It appears that married persons have “better” mental health (higher values), but that the gap may be narrowing over
time. One purpose of longitudinal studies is to distinguish cohort effects—relationships that appear to be present but are really
manifestations of group associations—from causal effects or trends. For example, it could be that marriage produces good mental
health—a real effect—or it could be that people with good mental health tend to get married—a manifestation. If the latter is
the case, scores would regress toward the mean over time.

A multivariate examination is now warranted. I will begin with a multivariate examination of the cross-sectional effects:

. corr married imale Iagecont Inonwht
(obs=3691)

I married imale iagecont inonwht
--------+------------------------------------
married I   1.0000

imaleI   0.2456   1.0000

iagecontI   0.0663   0.0872   1.0000

inonwht  -0.0749  -0.0814  -0.0904   1.0000

Fortunately, there does not appear to be much intercorrelation between the demographic variables so we do not have to be overly
concerned with which variables we include in the model. Even if we omitted a relevant variable, it would have little effect on
the estimated coefficients of the others.



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