lsa.corr {RALSA}R Documentation

Compute correlations between variables within specified groups

Description

lsa.corr computes correlation coefficients between variables within groups defined by one or more variables.

Usage

lsa.corr(
  data.file,
  data.object,
  split.vars,
  bckg.corr.vars,
  PV.root.corr,
  corr.type,
  weight.var,
  include.missing = FALSE,
  shortcut = FALSE,
  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.corr.vars

Names of continuous background or contextual variables to compute the correlation coefficients for. The results will be computed by all groups specified by the splitting variables. See details.

PV.root.corr

The root names for the sets of plausible values to compute the correlation coefficients for. See details.

corr.type

String of length one, specifying the type of the correlations to compute, either "Pearson" (default) or "Spearman".

weight.var

The name of the variable containing the weights. If no name of a weight variable is provide, 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.

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

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.

The function computes correlation coefficients by the categories of the splitting variables. The percentages of respondents in each group are computed within the groups specified by the last splitting variable. If no splitting variables are added, the results will be computed only by country.

Multiple continuous background variables and/or sets of plausible values can be provided to compute correlation coefficients for. Please note that in this case the results will slightly differ compared to using each pair of the same background continuous variables or PVs in separate analysis. This is because the cases with the missing values are removed in advance and the more variables are provided to compute correlations for, the more cases are likely to be removed. That is, the function support only listwisie deletion.

Computation of correlation coefficients involving plausible values requires providing a root of the plausible values names in PV.root.corr. 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.corr 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.corr as "PV#CIV". More than one set of PVs can be added. Note, however, that providing multiple continuous variables for the bckg.avg.corr argument and multiple PV roots for the PV.root.corr argument will affect the results for the correlation coefficients for the PVs because the cases with missing on bckg.corr.vars will be removed and this will also affect the results from the PVs (i.e. listwise deletion). On the other hand, using only sets of PVs to correlate should not affect the results on any PV estimates because PVs shall not have any missing values.

A sufficient number of variable names (background/contextual) or PV roots have to be provided - either two background variables, or two PV roots, or mixture of them with total length of two (i.e. one background/contextual variable and one PV root).

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. The function provides two-tailed t-test and p-values for the correlation coefficients.

Value

If save.output = FALSE, a list containing the estimates and analysis information. 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 results are presented as a correlation matrices by the splitting variables. 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 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.

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 correlations between the complex student background scales
# "Home Educational Resources/SCL", "Students Sense of School Belonging/SCL" and
# "Students Value Mathematics/SCL" by sex of 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.corr(data.file = "C:/Data/TIMSS_2015_G8_Student_Miss_to_NA.RData", split.vars = "BSBG01",
bckg.corr.vars = c("BSBGHER", "BSBGSSB", "BSBGSVM"), include.missing = FALSE,
output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)

# Compute correlations between the complex student background scales
# "Home Educational Resources/SCL" and "Students Sense of School Belonging/SCL"
# and the plausible values in overall mathematics and overall science 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.corr(data.object = T15_G8_student_data, split.vars = c("BSBG01", "BSBG13A"),
bckg.corr.vars = c("BSBGHER", "BSBGSSB"), PV.root.corr = c("BSMMAT", "BSSSCI"),
weight.var = "SENWGT", include.missing = FALSE, shortcut = TRUE,
output.file = "C:/temp/test.xlsx", open.output = TRUE)

## End(Not run)

# Compute the correlations between student overall reading achievement, overall mathematics
# scores (i.e. using a set of PVs) and student family wealth, using PISA 2018 student data
# loaded as object in the memory, by country, and do not open the output after the computations
# are finished
## Not run: 
lsa.corr(data.object = CY07_MSU_STU_QQQ, bckg.corr.vars = "WEALTH",
PV.root.corr = c("PV#MATH", "PV#READ"), include.missing = TRUE,
output.file = "C:/temp/test.xlsx", open.output = FALSE)

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