File: data-documentation.R

package info (click to toggle)
r-cran-clubsandwich 0.5.3-1
  • links: PTS, VCS
  • area: main
  • in suites: bookworm, bullseye, sid
  • size: 1,160 kB
  • sloc: sh: 13; makefile: 2
file content (155 lines) | stat: -rw-r--r-- 7,517 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
#' Achievement Awards Demonstration program
#' 
#' Data from a randomized trial of the Achievement Awards
#' Demonstration program, reported in Angrist & Lavy (2009).
#' 
#' @format A data frame with 16526 rows and 21 variables: \describe{ 
#'   \item{school_id}{Fictitious school identification number}
#'   \item{school_type}{Factor identifying the school type (Arab religious, Jewish religious, Jewish secular)}
#'   \item{pair}{Number of treatment pair. Note that 7 is a triple.} 
#'   \item{treated}{Indicator for whether school was in treatment group}
#'   \item{year}{Cohort year}
#'   \item{student_id}{Fictitious student identification number}
#'   \item{sex}{Factor identifying student sex}
#'   \item{siblings}{Number of siblings}
#'   \item{immigrant}{Indicator for immigrant status}
#'   \item{father_ed}{Father's level of education}
#'   \item{mother_ed}{Mother's level of education}
#'   \item{Bagrut_status}{Indicator for Bagrut attainment}
#'   \item{attempted}{Number of Bagrut units attempted}
#'   \item{awarded}{Number of Bagrut units awarded}
#'   \item{achv_math}{Indicator for satisfaction of math requirement}
#'   \item{achv_english}{Indicator for satisfaction of English requirement}
#'   \item{achv_hebrew}{Indicator for satisfaction of Hebrew requirement}
#'   \item{lagscore}{Lagged Bagrut score}
#'   \item{qrtl}{Quartile within distribution of lagscore, calculated by cohort and sex}
#'   \item{half}{Lower or upper half within distribution of lagscore, calculated by cohort and sex}
#'   }
#'   
#' @source \href{https://economics.mit.edu/faculty/angrist/data1/data/angrist}{Angrist Data Archive}
#'   
#' @references Angrist, J. D., & Lavy, V. (2009). The effects of high stakes 
#'   high school achievement awards : Evidence from a randomized trial.
#'   \emph{American Economic Review, 99}(4), 1384-1414.
#'   \doi{10.1257/aer.99.4.1384}
#'   

"AchievementAwardsRCT"



#' Dropout prevention/intervention program effects
#'
#' A dataset containing estimated effect sizes, variances, and covariates from a
#' meta-analysis of dropout prevention/intervention program effects, conducted
#' by Wilson et al. (2011). Missing observations were imputed.
#'
#' @format A data frame with 385 rows and 18 variables: \describe{
#'   \item{LOR1}{log-odds ratio measuring the intervention effect}
#'   \item{varLOR}{estimated sampling variance of the log-odds ratio}
#'   \item{studyID}{unique identifier for each study} \item{studySample}{unique
#'   identifier for each sample within a study} \item{study_design}{study design
#'   (randomized, matched, or non-randomized and unmatched)}
#'   \item{outcome}{outcome measure for the intervention effect is estimated
#'   (school dropout, school enrollment, graduation, graduation or GED receipt)}
#'   \item{evaluator_independence}{degree of evaluator independence
#'   (independent, indirect but influential, involved in planning but not
#'   delivery, involved in delivery)} \item{implementation_quality}{level of
#'   implementation quality (clear problems, possible problems, no apparent
#'   problems)} \item{program_site}{Program delivery site (community, mixed,
#'   school classroom, school but outside of classroom)}
#'   \item{attrition}{Overall attrition (proportion)}
#'   \item{group_equivalence}{pretest group-equivalence log-odds ratio}
#'   \item{adjusted}{adjusted or unadjusted data used to calculate intervention
#'   effect} \item{male_pct}{proportion of the sample that is male}
#'   \item{white_pct}{proportion of the sample that is white}
#'   \item{average_age}{average age of the sample} \item{duration}{program
#'   duration (in weeks)} \item{service_hrs}{program contact hours per week}
#'   \item{big_study}{indicator for the 32 studies with 3 or more effect sizes}
#'   }
#'
#' @source Wilson, S. J., Lipsey, M. W., Tanner-Smith, E., Huang, C. H., &
#'   Steinka-Fry, K. T. (2011). Dropout prevention and intervention programs:
#'   Effects on school completion and dropout Among school-aged children and
#'   youth: A systematic review. _Campbell Systematic Reviews, 7_(1), 1-61.
#'   \doi{10.4073/csr.2011.8}
#'
#' @references Wilson, S. J., Lipsey, M. W., Tanner-Smith, E., Huang, C. H., &
#'   Steinka-Fry, K. T. (2011). Dropout prevention and intervention programs:
#'   Effects on school completion and dropout Among school-aged children and
#'   youth: A systematic review. _Campbell Systematic Reviews, 7_(1), 1-61.
#'   \doi{10.4073/csr.2011.8}
#'
#'   Tipton, E., & Pustejovsky, J. E. (2015). Small-sample adjustments for tests
#'   of moderators and model fit using robust variance estimation in
#'   meta-regression. _Journal of Educational and Behavioral Statistics, 40_(6), 604-634.
#'   \doi{10.3102/1076998615606099}
#'   

"dropoutPrevention"


#' State-level annual mortality rates by cause among 18-20 year-olds
#' 
#' A dataset containing state-level annual mortality rates for select causes of
#' death, as well as data related to the minimum legal drinking age and alcohol
#' consumption.
#' 
#' @format A data frame with 5508 rows and 12 variables: \describe{ 
#'   \item{year}{Year of observation} 
#'   \item{state}{identifier for state} 
#'   \item{count}{Number of deaths} 
#'   \item{pop}{Population size} 
#'   \item{legal}{Proportion of 18-20 year-old population that is legally allowed to drink} 
#'   \item{beertaxa}{Beer taxation rate} 
#'   \item{beerpercap}{Beer consumption per capita} 
#'   \item{winepercap}{Wine consumption per capita} 
#'   \item{spiritpercap}{Spirits consumption per capita} 
#'   \item{totpercap}{Total alcohol consumption per capita} 
#'   \item{mrate}{Mortality rate per 10,000} 
#'   \item{cause}{Cause of death} 
#'   }
#'   
#' @source
#'   \href{http://masteringmetrics.com/wp-content/uploads/2015/01/deaths.dta}{Mastering
#'   'Metrics data archive}
#'   
#' @references
#' 
#' Angrist, J. D., and Pischke, J. S. (2014). _Mastering'metrics: the path from
#' cause to effect_. Princeton University Press, 2014.
#' 
#' Carpenter, C., & Dobkin, C. (2011). The minimum legal drinking age and public
#' health. _Journal of Economic Perspectives, 25_(2), 133-156.
#' \doi{10.1257/jep.25.2.133}
#' 
 
"MortalityRates"

#' Randomized experiments on SAT coaching
#' 
#' Effect sizes from studies on the effects of SAT coaching,
#' reported in Kalaian and Raudenbush (1996)
#' 
#' @format A data frame with 67 rows and 11 variables: 
#' \describe{ 
#'   \item{study}{Study identifier}
#'   \item{year}{Year of publication} 
#'   \item{test}{Character string indicating whether effect size corresponds to outcome on verbal (SATV) or math (SATM) test}
#'   \item{d}{Effect size estimate (Standardized mean difference)} 
#'   \item{V}{Variance of effect size estimate} 
#'   \item{nT}{Sample size in treatment condition} 
#'   \item{nC}{Sample size in control condition} 
#'   \item{study_type}{Character string indicating whether study design used a matched, non-equivalent, or randomized control group} 
#'   \item{hrs}{Hours of coaching} 
#'   \item{ETS}{Indicator variable for Educational Testing Service} 
#'   \item{homework}{Indicator variable for homework} 
#'   }
#'   
#' @references Kalaian, H. A. & Raudenbush, S. W. (1996). A multivariate mixed 
#'   linear model for meta-analysis. \emph{Psychological Methods, 1}(3),
#'   227-235. 
#'   \doi{10.1037/1082-989X.1.3.227}
#'   

"SATcoaching"