Univariate Descriptive Statistics (Means and Standard Deviations)
BIFIE.univar.RdComputes some univariate descriptive statistics (means and standard deviations).
Arguments
- BIFIEobj
Object of class
BIFIEdata- vars
Vector of variables for which statistics should be computed
- group
Optional grouping variable(s)
- group_values
Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.
- se
Optional logical indicating whether statistical inference based on replication should be employed.
- object
Object of class
BIFIE.univar- digits
Number of digits for rounding output
- ...
Further arguments to be passed
Value
A list with following entries
- stat
Data frame with univariate statistics
- stat_M
Data frame with means
- stat_SD
Data frame with standard deviations
- output
Extensive output with all replicated statistics
- ...
More values
See also
See BIFIE.univar.test for a test of equal means and
effect sizes \(\eta\) and \(d\).
Descriptive statistics without statistical inference can be
estimated by the collection of
miceadds::ma.wtd.statNA
functions from the miceadds package.
Further descriptive functions:
survey::svymean,
intsvy::timss.mean,
intsvy::timss.mean.pv,
stats::weighted.mean,
Hmisc::wtd.mean,
miceadds::ma.wtd.meanNA
survey::svyvar,
Hmisc::wtd.var,
miceadds::ma.wtd.sdNA,
miceadds::ma.wtd.covNA
Examples
#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################
data(data.timss1)
data(data.timssrep)
# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
wgtrep=data.timssrep[, -1 ] )
#> +++ Generate BIFIE.data object
#> |*****|
#> |-----|
# compute descriptives for plausible values
res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI","books") )
#> |*****|
#> |-----|
summary(res1)
#> ------------------------------------------------------------
#> BIFIEsurvey 3.8.0 ()
#>
#> Function 'BIFIE.univar'
#>
#> Call:
#> BIFIEsurvey::BIFIE.univar(BIFIEobj = bdat, vars = c("ASMMAT",
#> "ASSSCI", "books"))
#>
#> Date of Analysis: 2026-01-11 08:35:30.129748
#> Time difference of 0.05973172 secs
#> Computation time: 0.05973172
#>
#> Multiply imputed dataset
#>
#> Number of persons = 4668
#> Number of imputed datasets = 5
#> Number of Jackknife zones per dataset = 75
#> Fay factor = 1
#>
#> Univariate Statistics | Means
#> var Nweight Ncases M M_SE M_df M_t M_p M_fmi M_VarMI
#> 1 ASMMAT 78332.99 4668 508.311 2.617 Inf 194.268 0 0.050 0.284
#> 2 ASSSCI 78332.99 4668 531.502 2.886 369.42 184.185 0 0.104 0.722
#> 3 books 78332.99 4668 2.937 0.040 Inf 73.270 0 0.005 0.000
#> M_VarRep
#> 1 6.505
#> 2 7.461
#> 3 0.002
#>
#> Univariate Statistics | Standard Deviations
#> var Nweight Ncases SD SD_SE SD_df SD_t SD_p
#> 1 ASMMAT 78332.99 4668 62.696 1.088 57.07 467.401 0
#> 2 ASSSCI 78332.99 4668 70.477 1.344 28.07 395.539 0
#> 3 books 78332.99 4668 1.147 0.015 Inf 195.485 0
# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="books",
group_values=1:5)
#> |*****|
#> |-----|
summary(res2)
#> ------------------------------------------------------------
#> BIFIEsurvey 3.8.0 ()
#>
#> Function 'BIFIE.univar'
#>
#> Call:
#> BIFIEsurvey::BIFIE.univar(BIFIEobj = bdat, vars = c("ASMMAT",
#> "ASSSCI"), group = "books", group_values = 1:5)
#>
#> Date of Analysis: 2026-01-11 08:35:30.194969
#> Time difference of 0.04463124 secs
#> Computation time: 0.04463124
#>
#> Multiply imputed dataset
#>
#> Number of persons = 4668
#> Number of imputed datasets = 5
#> Number of Jackknife zones per dataset = 75
#> Fay factor = 1
#>
#> Univariate Statistics | Means
#> var groupvar groupval Nweight Ncases M M_SE M_df M_t M_p
#> 1 ASMMAT books 1 7889.467 484.0 462.463 4.704 67.69 98.307 0
#> 2 ASMMAT books 2 20602.693 1213.2 488.408 3.455 798.28 141.366 0
#> 3 ASMMAT books 3 28233.896 1657.6 516.889 2.408 123.28 214.692 0
#> 4 ASMMAT books 4 11754.811 708.8 532.942 3.158 125.24 168.760 0
#> 5 ASMMAT books 5 9852.122 604.4 532.664 3.210 304.41 165.944 0
#> 6 ASSSCI books 1 7889.467 484.0 474.183 5.900 38.76 80.374 0
#> 7 ASSSCI books 2 20602.693 1213.2 507.314 4.099 102.59 123.778 0
#> 8 ASSSCI books 3 28233.896 1657.6 542.211 2.591 54.59 209.306 0
#> 9 ASSSCI books 4 11754.811 708.8 559.753 3.273 87.11 171.008 0
#> 10 ASSSCI books 5 9852.122 604.4 563.601 3.495 103.83 161.264 0
#> M_fmi M_VarMI M_VarRep
#> 1 0.243 4.483 16.750
#> 2 0.071 0.704 11.092
#> 3 0.180 0.870 4.752
#> 4 0.179 1.485 8.191
#> 5 0.115 0.984 9.122
#> 6 0.321 9.318 23.625
#> 7 0.197 2.764 13.481
#> 8 0.271 1.514 4.894
#> 9 0.214 1.913 8.418
#> 10 0.196 1.998 9.817
#>
#> Univariate Statistics | Standard Deviations
#> var groupvar groupval Nweight Ncases SD SD_SE SD_df SD_t SD_p
#> 1 ASMMAT books 1 7889.467 484.0 59.667 3.350 60.78 138.043 0
#> 2 ASMMAT books 2 20602.693 1213.2 59.146 1.774 163.20 275.269 0
#> 3 ASMMAT books 3 28233.896 1657.6 57.654 1.335 95.77 387.101 0
#> 4 ASMMAT books 4 11754.811 708.8 57.709 1.863 640.19 286.114 0
#> 5 ASMMAT books 5 9852.122 604.4 59.502 2.537 37.73 209.995 0
#> 6 ASSSCI books 1 7889.467 484.0 71.334 3.286 642.31 144.321 0
#> 7 ASSSCI books 2 20602.693 1213.2 66.532 1.806 91.17 280.916 0
#> 8 ASSSCI books 3 28233.896 1657.6 62.825 1.643 22.53 329.936 0
#> 9 ASSSCI books 4 11754.811 708.8 62.818 2.075 110.46 269.812 0
#> 10 ASSSCI books 5 9852.122 604.4 62.951 2.759 64.39 204.256 0
#> SD_fmi SD_VarMI SD_VarRep
#> 1 0.257 2.399 8.344
#> 2 0.157 0.411 2.655
#> 3 0.204 0.304 1.419
#> 4 0.079 0.229 3.195
#> 5 0.326 1.746 4.339
#> 6 0.079 0.710 9.943
#> 7 0.209 0.569 2.578
#> 8 0.421 0.948 1.563
#> 9 0.190 0.683 3.485
#> 10 0.249 1.581 5.716
#############################################################################
# EXAMPLE 2: TIMSS dataset with missings
#############################################################################
data(data.timss2)
data(data.timssrep)
# use first dataset with missing data from data.timss2
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT,
wgtrep=data.timssrep[, -1 ])
#> +++ Generate BIFIE.data object
#> |*|
#> |-|
# some descriptive statistics without statistical inference
res1a <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books"), se=FALSE)
#> |*|
#> |-|
# descriptive statistics with statistical inference
res1b <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books") )
#> |*|
#> |-|
summary(res1a)
#> ------------------------------------------------------------
#> BIFIEsurvey 3.8.0 ()
#>
#> Function 'BIFIE.univar'
#>
#> Call:
#> BIFIEsurvey::BIFIE.univar(BIFIEobj = bdat1, vars = c("ASMMAT",
#> "ASSSCI", "books"), se = FALSE)
#>
#> Date of Analysis: 2026-01-11 08:35:30.271156
#> Time difference of 0.002158165 secs
#> Computation time: 0.002158165
#>
#> Multiply imputed dataset
#>
#> Number of persons = 4668
#> Number of imputed datasets = 1
#> Number of Jackknife zones per dataset = 0
#> Fay factor = 1
#>
#> Univariate Statistics | Means
#> var Nweight Ncases M
#> 1 ASMMAT 78332.99 4668 508.590
#> 2 ASSSCI 78332.99 4668 532.906
#> 3 books 78332.99 4554 2.945
#>
#> Univariate Statistics | Standard Deviations
#> var Nweight Ncases SD
#> 1 ASMMAT 78332.99 4668 62.725
#> 2 ASSSCI 78332.99 4668 69.694
#> 3 books 78332.99 4554 1.146
summary(res1b)
#> ------------------------------------------------------------
#> BIFIEsurvey 3.8.0 ()
#>
#> Function 'BIFIE.univar'
#>
#> Call:
#> BIFIEsurvey::BIFIE.univar(BIFIEobj = bdat1, vars = c("ASMMAT",
#> "ASSSCI", "books"))
#>
#> Date of Analysis: 2026-01-11 08:35:30.273863
#> Time difference of 0.01287937 secs
#> Computation time: 0.01287937
#>
#> Multiply imputed dataset
#>
#> Number of persons = 4668
#> Number of imputed datasets = 1
#> Number of Jackknife zones per dataset = 75
#> Fay factor = 1
#>
#> Univariate Statistics | Means
#> var Nweight Ncases M M_SE M_df M_t M_p M_VarRep
#> 1 ASMMAT 78332.99 4668 508.590 2.575 NA 197.535 NA 6.629
#> 2 ASSSCI 78332.99 4668 532.906 2.691 NA 198.033 NA 7.241
#> 3 books 78332.99 4554 2.945 0.040 NA 73.110 NA 0.002
#>
#> Univariate Statistics | Standard Deviations
#> var Nweight Ncases SD SD_SE SD_df SD_t SD_p
#> 1 ASMMAT 78332.99 4668 62.725 0.948 NA 536.334 NA
#> 2 ASSSCI 78332.99 4668 69.694 1.123 NA 474.625 NA
#> 3 books 78332.99 4554 1.146 0.015 NA 195.134 NA
# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI"), group="books")
#> |*|
#> |-|
# Note that if group_values is not specified as an argument it will be
# automatically determined by the observed frequencies in the dataset
summary(res2)
#> ------------------------------------------------------------
#> BIFIEsurvey 3.8.0 ()
#>
#> Function 'BIFIE.univar'
#>
#> Call:
#> BIFIEsurvey::BIFIE.univar(BIFIEobj = bdat1, vars = c("ASMMAT",
#> "ASSSCI"), group = "books")
#>
#> Date of Analysis: 2026-01-11 08:35:30.294626
#> Time difference of 0.01047182 secs
#> Computation time: 0.01047182
#>
#> Multiply imputed dataset
#>
#> Number of persons = 4668
#> Number of imputed datasets = 1
#> Number of Jackknife zones per dataset = 75
#> Fay factor = 1
#>
#> Univariate Statistics | Means
#> var groupvar groupval Nweight Ncases M M_SE M_df M_t M_p
#> 1 ASMMAT books 1 7609.320 464 466.444 4.520 NA 103.201 NA
#> 2 ASMMAT books 2 19993.365 1174 489.631 3.044 NA 160.834 NA
#> 3 ASMMAT books 3 27691.856 1622 517.070 2.179 NA 237.342 NA
#> 4 ASMMAT books 4 11591.981 699 533.460 2.888 NA 184.719 NA
#> 5 ASMMAT books 5 9702.202 595 532.790 3.046 NA 174.932 NA
#> 6 ASSSCI books 1 7609.320 464 476.618 4.848 NA 98.315 NA
#> 7 ASSSCI books 2 19993.365 1174 510.318 3.676 NA 138.820 NA
#> 8 ASSSCI books 3 27691.856 1622 544.548 2.143 NA 254.100 NA
#> 9 ASSSCI books 4 11591.981 699 560.435 3.011 NA 186.104 NA
#> 10 ASSSCI books 5 9702.202 595 563.692 3.336 NA 168.961 NA
#> M_VarRep
#> 1 20.428
#> 2 9.268
#> 3 4.746
#> 4 8.340
#> 5 9.276
#> 6 23.502
#> 7 13.514
#> 8 4.593
#> 9 9.069
#> 10 11.130
#>
#> Univariate Statistics | Standard Deviations
#> var groupvar groupval Nweight Ncases SD SD_SE SD_df SD_t SD_p
#> 1 ASMMAT books 1 7609.320 464 61.668 2.897 NA 161.007 NA
#> 2 ASMMAT books 2 19993.365 1174 59.064 1.338 NA 366.013 NA
#> 3 ASMMAT books 3 27691.856 1622 57.346 1.201 NA 430.496 NA
#> 4 ASMMAT books 4 11591.981 699 58.107 2.147 NA 248.451 NA
#> 5 ASMMAT books 5 9702.202 595 60.051 1.891 NA 281.705 NA
#> 6 ASSSCI books 1 7609.320 464 70.792 3.258 NA 146.313 NA
#> 7 ASSSCI books 2 19993.365 1174 65.278 1.741 NA 293.111 NA
#> 8 ASSSCI books 3 27691.856 1622 61.913 1.206 NA 451.425 NA
#> 9 ASSSCI books 4 11591.981 699 61.476 1.879 NA 298.189 NA
#> 10 ASSSCI books 5 9702.202 595 63.620 2.789 NA 202.078 NA
#> SD_VarRep
#> 1 8.393
#> 2 1.790
#> 3 1.443
#> 4 4.610
#> 5 3.577
#> 6 10.611
#> 7 3.031
#> 8 1.455
#> 9 3.532
#> 10 7.781