lsa.pcts.means {RALSA}R Documentation

Compute percentages of respondents in groups and/or means (arithmetic average, median or mode) on continuous variables within specified groups

Description

lsa.pcts.means computes percentages of respondents within groups defined by one or more variables and the means for one or more variables.

Usage

lsa.pcts.means(
  data.file,
  data.object,
  split.vars,
  bckg.avg.vars,
  PV.root.avg,
  central.tendency,
  weight.var,
  include.missing = FALSE,
  shortcut = FALSE,
  graphs = FALSE,
  perc.x.label = NULL,
  perc.y.label = NULL,
  mean.x.labels = NULL,
  mean.y.labels = NULL,
  save.output = TRUE,
  output.file,
  open.output = TRUE
)

Arguments

data.file

The file containing lsa.data object. Either this or data.object shall be specified, but not both. See details.

data.object

The object in the memory containing lsa.data object. Either this or data.file shall be specified, but not both. See details.

split.vars

Categorical variable(s) to split the results by. If no split variables are provided, the results will be for the overall countries' populations. If one or more variables are provided, the results will be split by all but the last variable and the percentages of respondents will be computed by the unique values of the last splitting variable.

bckg.avg.vars

Name(s) of continuous background or contextual variable(s) to compute the means for. The results will be computed by all groups specified by the splitting variables. See details.

PV.root.avg

The root name(s) for the set(s) of plausible values. See details.

central.tendency

Which measure of central tendency shall be computed - mean (default) median or mode. See details.

weight.var

The name of the variable containing the weights. If no name of a weight variable is provided, the function will automatically select the default weight variable for the provided data, depending on the respondent type.

include.missing

Logical, shall the missing values of the splitting variables be included as categories to split by and all statistics produced for them? The default (FALSE) takes all cases on the splitting variables without missing values before computing any statistics. See details.

shortcut

Logical, shall the "shortcut" method for IEA TIMSS, TIMSS Advanced, TIMSS Numeracy, eTIMSS PSI, PIRLS, ePIRLS, PIRLS Literacy and RLII be applied? The default (FALSE) applies the "full" design when computing the variance components and the standard errors of the estimates.

graphs

Logical, shall graphs be produced? Default is FALSE. See details.

perc.x.label

String, custom label for the horizontal axis in percentage graphs. Ignored if graphs = FALSE. See details.

perc.y.label

String, custom label for the vertical axis in percentage graphs. Ignored if graphs = FALSE. See details.

mean.x.labels

List of strings, custom labels for the horizontal axis in means' graphs. Ignored if graphs = FALSE. See details.

mean.y.labels

List of strings, custom labels for the vertical axis in means' graphs. Ignored if graphs = FALSE. See details.

save.output

Logical, shall the output be saved in MS Excel file (default) or not (printed to the console or assigned to an object).

output.file

If save.output = TRUE (default), full path to the output file including the file name. If omitted, a file with a default file name "Analysis.xlsx" will be written to the working directory (getwd()). Ignored if save.output = FALSE.

open.output

Logical, shall the output be open after it has been written? The default (TRUE) opens the output in the default spreadsheet program installed on the computer. Ignored if save.output = FALSE.

Details

The function computes percentages of respondents specified by the categories of splitting variables. The percentages are computed within the groups specified by the last splitting variable. If a continuous variable(s) are provided (background or sets of plausible values), their means (as arithmetic means, medians or modes) will be computed by groups defined by one or more splitting variables. If no splitting variables are added, the results will be computed only by country.

Either data.file or data.object shall be provided as source of data. If both of them are provided, the function will stop with an error message.

Multiple continuous background variables can be provided to compute their means (as arithmetic means, medians or modes). Please note that in this case the results will slightly differ compared to using each of the same background continuous variables in separate analyses. This is because the cases with the missing values on bckg.avg.vars are removed in advance and the more variables are provided to bckg.avg.vars, the more cases are likely to be removed.

Computation of means involving plausible values requires providing a root of the plausible values names in PV.root.avg. All studies (except CivED, TEDS-M, SITES, TALIS and TALIS Starting Strong Survey) have a set of PVs per construct (e.g. in TIMSS five for overall mathematics, five for algebra, five for geometry, etc.). In some studies (say TIMSS and PIRLS) the names of the PVs in a set always start with character string and end with sequential number of the PV. For example, the names of the set of PVs for overall mathematics in TIMSS are BSMMAT01, BSMMAT02, BSMMAT03, BSMMAT04 and BSMMAT05. The root of the PVs for this set to be added to PV.root.avg will be "BSMMAT". The function will automatically find all the variables in this set of PVs and include them in the analysis. In other studies like OECD PISA and IEA ICCS and ICILS the sequential number of each PV is included in the middle of the name. For example, in ICCS the names of the set of PVs are PV1CIV, PV2CIV, PV3CIV, PV4CIV and PV5CIV. The root PV name has to be specified in PV.root.avg as "PV#CIV". More than one set of PVs can be added. Note, however, that providing continuous variable(s) for the bckg.avg.vars argument and root PV for the PV.root.avg argument will affect the results for the PVs because the cases with missing on bckg.avg.vars will be removed and this will also affect the results from the PVs. On the other hand, using more than one set of PVs at the same time should not affect the results on any PV estimates because PVs shall not have any missing values.

If no variables are specified for bckg.avg.vars, and no PV root names for PV.root.avg, the output will contain only percentages of cases in groups specified by the splitting variables, if any. If they are, their means will be computed either as arithmetic means, medians or modes. This can be controlled by setting the central.tendency argument to mean (default), median or mode. Note that if central.tendency = "mode" and the variables passed to bckg.avg.vars or the sets of PVs passed to PV.root.avg have more than one mode, the value for the lowest value will be included in the output. As a conseequence, the standard errors may be inflated.

If include.missing = FALSE (default), all cases with missing values on the splitting variables will be removed and only cases with valid values will be retained in the statistics. Note that the data from the studies can be exported in two different ways: (1) setting all user-defined missing values to NA; and (2) importing all user-defined missing values as valid ones and adding their codes in an additional attribute to each variable. If the include.missing is set to FALSE (default) and the data used is exported using option (2), the output will remove all values from the variable matching the values in its missings attribute. Otherwise, it will include them as valid values and compute statistics for them.

The shortcut argument is valid only for TIMSS, eTIMSS PSI, TIMSS Advanced, TIMSS Numeracy, PIRLS, ePIRLS, PIRLS Literacy and RLII. Previously, in computing the standard errors, these studies were using 75 replicates because one of the schools in the 75 JK zones had its weights doubled and the other one has been taken out. Since TIMSS 2015 and PIRLS 2016 the studies use 150 replicates and in each JK zone once a school has its weights doubled and once taken out, i.e. the computations are done twice for each zone. For more details see Foy & LaRoche (2016) and Foy & LaRoche (2017). If replication of the tables and figures is needed, the shortcut argument has to be changed to TRUE.

If graphs = TRUE, the function will produce graphs. If only split.vars are specified, bar plots of percentages of respondents (population estimates) per group will be produced with error bars (95% confidence) for these percentages. If bckg.avg.vars and/or PV.root.avg are specified, plots with 95% confidence intervals of the averages (means, medians or modes) will be produced for each average analysis variable. All plots are produced per country. If bckg.avg.vars and/or PV.root.avg are specified, but no split.vars at the end there will be plots for each of the analysis average variables for all countries together. By default the percentage graphs horizontal axis is labeled with the name of the last splitting variable, and the vertical is labeled as "Percentages XXXXX" where XXXXX is the last splitting variable the percentages are computed for. For the means' plots the horizontal axis is labeled as the name of the last splitting variable for whose categories the means are computed by, and the vertical axis is labeled as "Mean XXXXX" where XXXXX is the name of the variable for which means are computed. These defaults can be overriden by supplying values to perc.x.label, perc.y.label, mean.x.labels and mean.y.labels. The perc.x.label and perc.y.label arguments accept vectors of length 1, and if longer vectors are supplied, error is thrown. The mean.x.labels and mean.y.labels accept lists with number of components equal to the number of variables (background or PVs) for which means are computed, longer or shorter lists throw errors. See the examples.

row and column variable names are used for labeling the axes of the heatmaps, unless graph.row.label and/or graph.col.label arguments are supplied. These two arguments accept strings which will be used to label the axes.

Value

If save.output = FALSE, a list containing the estimates and analysis information. If graphs = TRUE, the plots will be added to the list of estimates.

If save.output = TRUE (default), an MS Excel (.xlsx) file (which can be opened in any spreadsheet program), as specified with the full path in the output.file. If the argument is missing, an Excel file with the generic file name "Analysis.xlsx" will be saved in the working directory (getwd()). The workbook contains three spreadsheets. The first one ("Estimates") contains a table with the results by country and the final part of the table contains averaged results from all countries' statistics. The following columns can be found in the table, depending on the specification of the analysis:

The second sheet contains some additional information related to the analysis per country in the following columns:

The third sheet contains the call to the function with values for all parameters as it was executed. This is useful if the analysis needs to be replicated later.

If graphs = TRUE there will be an additional "Graphs" sheet containing all plots.

If any warnings resulting from the computations are issued, these will be included in an additional "Warnings" sheet in the workbook as well.

References

LaRoche, S., Joncas, M., & Foy, P. (2016). Sample Design in TIMSS 2015. In M. O. Martin, I. V. S. Mullis, & M. Hooper (Eds.), Methods and Procedures in TIMSS 2015 (pp. 3.1-3.37). Chestnut Hill, MA: TIMSS & PIRLS International Study Center.

LaRoche, S., Joncas, M., & Foy, P. (2017). Sample Design in PIRLS 2016. In M. O. Martin, I. V. S. Mullis, & M. Hooper (Eds.), Methods and Procedures in PIRLS 2016 (pp. 3.1-3.34). Chestnut Hill, MA: Lynch School of Education, Boston College.

See Also

lsa.convert.data

Examples

# Compute percentages of female and male students in TIMSS 2015 grade 8 using data file, omit
# missing from the splitting variable (female and male as answered by the students), without
# shortcut, and open the output after the computations are done
## Not run: 
lsa.pcts.means(data.file = "C:/Data/TIMSS_2015_G8_Student_Miss_to_NA.RData",
split.vars = "BSBG01", include.missing = FALSE,
output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)

# Compute the arithmetic average of the complex background scale "Students like learning
# mathematics" by student sex and frequency of using computer or tablet at home using TIMSS
# 2015 grade 8 data loaded in memory, using the shortcut, include the missing values in
# the splitting variables, and use the senate weights
## Not run: 
lsa.pcts.means(data.object = T15_G8_student_data, split.vars = c("BSBG01", "BSBG13A"),
bckg.avg.vars = "BSBGSLM", weight.var = "SENWGT", include.missing = FALSE, shortcut = TRUE,
output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)

# Repeat the analysis from above, adding a second continuous variable to compute the arithmetic
# average for, the "Students Like Learning Science" complex scale
## Not run: 
lsa.pcts.means(data.object = T15_G8_student_data, split.vars = c("BSBG01", "BSBG13A"),
bckg.avg.vars = c("BSBGSLM", "BSBGSLS"), weight.var = "SENWGT", include.missing = FALSE,
shortcut = TRUE, output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)
# Same as above, but add graphs with custom labels for the percentages and means
## Not run: 
lsa.pcts.means(data.object = T15_G8_student_data, split.vars = c("BSBG01", "BSBG13A"),
bckg.avg.vars = c("BSBGSLM", "BSBGSLS"), weight.var = "SENWGT", include.missing = FALSE,
shortcut = TRUE, graphs = TRUE,
perc.x.label = "Using computer or tables for schoolwork at home",
perc.y.label = "Percentage of students",
mean.x.labels = list("Books at home", "Books at home"),
mean.y.labels = list("Average like learning math", "Average like learning science"),
output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)

# Compute the arithmetic average of student overall reading achievement scores
# (i.e. using a set of PVs), using PIRLS 2016 student data file, split the output by student
# sex, use the full design, include the missing values od the splitting variable
# (i.e. student sex), and do not open the output after the computations are finished
## Not run: 
lsa.pcts.means(data.file = "C:/Data/PIRLS_2016_Student_Miss_to_NA.RData", split.vars = "ASBG01",
PV.root.avg = "ASRREA", include.missing = TRUE,
output.file = "C:/temp/test.xlsx", open.output = FALSE)

## End(Not run)

# Same as above, this time compute the median instead of the arithmetic average
## Not run: 
lsa.pcts.means(data.file = "C:/Data/PIRLS_2016_Student_Miss_to_NA.RData", split.vars = "ASBG01",
PV.root.avg = "ASRREA", include.missing = TRUE,
central.tendency = "median",
output.file = "C:/temp/test.xlsx", open.output = FALSE)

## End(Not run)
[Package RALSA version 1.4.7 Index]