Errata for Multilevel and Longitudinal Modeling Using Stata
The errata for Multilevel and Longitudinal Modeling Using Stata are
provided below. Click here for an
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| (1) | Chapter 1, p. 20, third paragraph, third sentence |
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In the top panel, 80% of this variance is due to subjects,
whereas in the bottom panel, 80% is due to within-subject variability.
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In the bottom panel, 80% of this variance is due to subjects,
whereas in the top panel, 80% is due to within-subject variability.
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| (1) | Chapter 1, p. 23, fifth line |
| (1) | Chapter 1, p. 29, exercise 1.5.2, line 2 |
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... components within and between raters.
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... components within and between graders.
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| (1) | Chapter 1, p. 41, footnote |
| (1), (2) |
Chapter 1, p. 47, second histogram command in the middle of the page |
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histogram levm2 if time==1, normal xtitle(Standardized level-2 residuals)
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histogram lev2m1 if time==1, normal xtitle(Standardized level-2 residuals)
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| (1) | Chapter 2, p. 54, exercise 2.5, number 3 |
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Plot mnw versus size, using different symbols for the two treatment groups.
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Plot mnw versus size, using different symbols for the three treatment groups.
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| (1) | Chapter 2, p. 54, exercise 2.6, third variable in variable list |
| (1) | Chapter 3, p. 60, commands at the bottom of the page |
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. merge school using gcse
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. sort school
. merge school using http://www.stata-press.com/data/mlmus/gcse
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| (1), (2) | Chapter 3, p. 61, last line |
| (1) | Chapter 3, p. 76, gllamm command line |
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. gllamm gcse lrt, i(school) nrf(2) eqs(inter slope) ip(m) nip(15) from(a) copy
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. gllamm gcse lrt, i(school) nrf(2) eqs(inter slope) ip(m) nip(15) adapt from(a) copy
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| (1) | Chapter 3, p. 89, gllamm command line |
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. gllamm weight age2, nocons i(id) nrf(2) eqs(inter slope) ip(m) nip(15) from(a) geqs(r1 r2) adapt
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. gllamm weight age2, nocons i(id) nrf(2) eqs(inter slope) ip(m) nip(15) geqs(r1 r2) adapt
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| (1) | Chapter 3, p. 95, exercise 3.1.4, second sentence |
Also obtain ML (or OLS) estimates of
 and
 by
first subtracting
with at least two observations.
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Also obtain ML (or OLS) estimates of
and
first
by subtracting
and then by using the statsby
command for children with at least two observations.
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| (1) | Chapter 3, p. 97, exercise 3.4, fourth variable in level-1 variable list |
Switch the order of exercises 3.5.4 and 3.5.5 so that you
“fit the random-coefficient model” first.
| (1) | Chapter 3, p. 99, exercise 3.6, third description in variable list |
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age 14, number of years ...
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age - 14, number of years ...
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| (1) | Chapter 3, p. 100, exercise 3.8 |
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Using (3.2) and (3.3) and the estimates for Model 2 in table 3.1, ...
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Using (3.2) and the estimates for Model 2 on page 76, ...
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| (1) | Chapter 4, p. 102, equation 4.1 |
| (1) | Chapter 4, p. 114, last line of Stata command |
Replace “Probability” with “Proportion”
so that the y-axis title in figure 4.7 is “Proportion
of onycholysis“.
| (1) | Chapter 4, p. 120, last displayed equation |
| (1) | Chapter 4, p. 129, fourth equation |
| (1) | Chapter 4, p. 138, exercise 4.5.1, last line |
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... by case (age), sort: gen lag=y[_n-1].
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... by case (age), sort: gen lag = wemp[_n-1].
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| (1) |
Chapter 4, p. 140, exercise 4.7.2, space missing between nr and (year) |
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... by nr(year), sort: gen lag = union[_n-1].
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... by nr (year), sort: gen lag = union[_n-1].
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| (1) | Chapter 4, p. 142, exercise 4.9.1 |
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Estimate the values of the estimated school-specific regression ...
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Guess the values of the estimated school-specific regression ...
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| (1) | Chapter 5, p. 154, equation (5.2) |
| (2) | Chapter 5, p. 154, equation (5.2) |
| (1) | Chapter 5, p. 156, table 5.1, estimate of kappa_1 from RC-POM |
| (1) |
Chapter 5, p. 158, equation (5.3) and equation in sentence below equation (5.3) |
| (1) | Chapter 5, p. 159, equation (5.4) |
| (2) | Chapter 5, p. 159, equation (5.4) |
| (1), (2) | Chapter 5, p. 167, section 5.11.1, 3rd equation |
| (1) | Chapter 5, p. 178, exercise 5.4.1, line 3 |
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... with a random intercept for graders.
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... with a random intercept for essays.
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| (2) | Chapter 6, p. 182, first equation |
| (2) | Chapter 6, p. 190, chapter heading |
6. Random-intercept Poisson regression
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6.8 Random-intercept Poisson regression
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| (2) | Chapter 6, p. 190, chapter subheading |
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6. .1 Model specification
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6.8.1 Model specification
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| (1) | Chapter 7, p. 227, table 7.1, log likelihood for model 4 |
| (1) | Chapter 7, p. 247, caption for figure 7.5 |
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(left panel: two-stage formulation ...)
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(left panel: three-stage formulation ...)
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| (1) | Chapter 8, p. 261, line 1 |
| (1) | References, p. 304 and throughout the book |
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Hedeker, D. and R. D. Gibbons. 1996a. Applied Longitudinal Data Analysis. Chichester,
UK: Wiley.
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Hedeker, D. and R. D. Gibbons. Forthcoming. Applied Longitudinal Data Analysis. Chichester,
UK: Wiley.
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