News

May 28, 2021

RALSA was updated to version 1.0.1. This is a maintenance version following the update of base R to v4.1.0 where some functions’ behavior has changed and cause crashes in the analysis functions

Bug fixes

  • All analysis functions. Some functions in base R were updated and their behavior has changed causing crashes in the analysis functions with first argument has length \> 1 error messages.

  • lsa.vars.dict function. When there are just two levels for a factor variable, the second level in ?Variable levels? ends without a single quote.

  • Occasionally, the lsa.data.diag freezes R with a question mark in the console and eventually crashes the R session when split.vars are provided.

Miscellaneous

  • Various optimizations in the lsa.corrlsa.lin.reg and lsa.bin.log.reg functions.

  • The lsa.data.diag function now has user interruption handling with a message.

 

 

 

April 28, 2021

We are happy to announce that RALSA (the R Analyzer for Large-Scale Assessments) has received an update. The package version was incremented to 1.0.0 because it  has reached maturity and stability. It brings a new function, many improvements and bug fixes. Here is what’s new:

New functionality

  • RALSA has a new data preparation function, lsa.data.diag, a quick automated production of weighted or unweighted frequency tables for categorical variables and descriptive statistics for continuous variables. These tables are for data diagnostic purposes prior to analysis and are not for reporting results from large-scale assessments.

Miscellaneous

  • After updating R to v4.0.5 and the packages RALSA depends on, the warning messages in the GUI console were not displayed, although displayed in RStudio console. The warning messages are now back in the GUI console as well.
  • Showing and hiding elements in the GUI when using the benchmarks analysis type was improved.
  • The version number was incremented to 1.0.0 since the package has already achieved stability and maturity.

Bug fixes

  • All analysis functions. Any analysis function crashes when TALIS 3S data is used and the weight variable is specified explicitly.
  • All analysis functions. When ICCS teacher and school data are merged, the default weight, jakknifing zone and jakknifing replication indicator are not automatically detected and the function crashes, while specifying the weight manually passes.
  • lsa.prctls function. The order of the columns for the percentiles, their SE, SVR and MVR is scrambled.
  • All data preparation and analysis functions. When using data.object to provide data and the object is in quotes, the function crashes with uninterpretable error message. Added custom handle and error message.
  • lsa.lin.reg function. The function crashes when using specific variable combinations.
  • GUI. The filtering of the tables with country ISOs, country names, variable names and labels is very slow and oftentimes unresponsive.
  • GUI with lsa.recode.vars. Occasionally, when the lsa.recode.vars function is executed by pressing the “Execute syntax” button, the interface crashes. This is because there is a factor level for which no actual values exist in the data. Now handled with custom error message.
  • lsa.merge.data function. The school weight was dropped when merging school and teacher data in TALIS which prevented the use of the merged file in multilevel models. Thank to Jelena Veletic.

For questions, feature requests, training requests, and bug reports, please write to ralsa@ineri.org.

 

 

 

March 15, 2021

We are happy to announce that RALSA (the R Analyzer for Large-Scale Assessments) has received an update (v0.90.3). It brings bug fixes and improvements. Here is what’s new:

Bug fixes

  • lsa.convert.data function. The function crashes when the different cycles of the study change the case of the file names and their extensions. Thank to Yuan-Ling (Linda) Liaw.
  • lsa.recode.vars function. If the recoding instruction ends with semicolon, the function crashes.
  • lsa.bin.log.reg function. The function crashes if some specific patterns are found in the binary dependent variable names.
  • lsa.pcts.means function. Some missing estimates in columns Mean, Mean_SE, Mean_SVR, Mean_MVR, SD_SE and SD_MVR when the groups are too small.
  • All analysis functions crash using SITES 2006 data when only mathematics teacher or only science teacher data are used.
  • lsa.pcts.means function. Some estimates using CivED data were incorrect.
  • lsa.pcts.means function. Missing estimates in some columns for specific combination of splitting variables.

Miscellaneous

  • The ISO argument in lsa.convert.data is now case-insensitive.
  • file.types and ISO arguments in lsa.merge.data are now case insensitive.
  • The error messages in lsa.recode.vars are improved.

For questions, feature requests, training requests, and bug reports, please write to ralsa@ineri.org.

 

 

 

January 2, 2021

We are happy to announce that RALSA (the R Analyzer for Large-Scale Assessments) has received an update (v0.90.2). Here is what’s new:

New study cycles added

  • TIMSS 2019 is now fully supported.

New study added

  • PISA for Development is now supported, as requested by David Joseph Rutkowski.

Bug fixes

  • lsa.convert.data function. Unrecognized characters in factor levels are now fixed. Such were, for example the levels of the number of books variable in PIRLS 2016 and other cycles which displayed unrecognized characters instead of instead of “-”.
  • lsa.convert.data function. For some variables categories have the same labels as the missing ones in other variables and are improperly converted as missing.
  • GUI. When categorical variables are added in the list of Independent background categorical variables in linear and logistic regression, the number of categories (N cat. column) and the drop-down menu for the reference categories (Ref. cat. column) include the missing values as well.
  • When loading or switching to a tab in the GUI, it is scrolled to the position where the previous tab was scrolled to.

Miscellaneous

  • Various improvements for the GUI elements location.
  • Improved documentation.
  • Links to the documentation for each functionality RALSA supports were added to the Help section of the GUI.
  • Improved messages, warnings and error messages.

The binary packages for the new version will be available tomorrow (January 4, 2021), the impatient ones can install from source now. To update to the new version, just update your R packages:

update.packages(ask = FALSE)

Those who do not have RALSA installed yet, follow the installation instructions.

For questions, feature requests, training requests, and bug reports, please write to ralsa@ineri.org.

 

 

 

November 10, 2020

RALSA v.0.90.1 was released!

We are proud to announce the first release of RALSA. After all the hard work of development, the first version of the R Analyzer for Large-Scale Assessments (RALSA) came to life. RALSA targets both the experienced R users, as well as those who do not have technical skills. Thus, along with the traditional command-line R interface, a Graphical User Interface is made available.

Note that this is a “first release” version, so some bugs are expected.

The R Analyzer for Large-Scale Assessments (RALSA) is an R package for preparation and analysis of data from large-scale assessments and surveys which use complex sampling and assessment design. Currently, RALSA supports a number of studies with different design and a number of analysis types (see below). The number for both of these will increase in future.

RALSA is a free and open source software licensed under GPL v2.0.

In addition to the traditional command-line R interface, RALSA introduces a Graphical User Interface for the users who lack the technical skills.

Currently, RALSA supports the following functionality:

  • Prepare data for analysis
    • Convert data (SPSS, or text in case of PISA prior 2015)
    • Merge study data files from different countries and/or respondents
    • View variable properties (name, class, variable label, response categories/unique values, user-defined missing values)
    • Recode variables
  • Perform analyses (more analysis types will be added in future)
    • Percentages of respondents in certain groups and averages on variables of interest, per group
    • Percentiles of variables within groups of respondents
    • Percentages of respondents reaching or surpassing benchmarks of achievement
    • Correlations (Pearson or Spearman)
    • Linear regression
    • Binary logistic regression

All data preparation and analysis functions automatically recognize the study design and apply the appropriate techniques to handle the complex sampling assessment design issues, while giving freedom to tweak the analysis (e.g. change the default weight, apply the “shortcut” method in TIMSS and PIRLS, and so on).

Currently, RALSA can work with data from all cycles of the following studies (more will be added in future):

  • CivED
  • ICCS
  • ICILS
  • RLII
  • PIRLS (including PIRLS Literacy and ePIRLS)
  • TIMSS (including TIMSS Numeracy, eTIMSS will be added with the upcoming release of TIMSS 2019)
  • TiPi (TIMSS and PIRLS joint study)
  • TIMSS Advanced
  • SITES
  • TEDS-M
  • PISA
  • TALIS
  • TALIS Starting Strong Survey (a.k.a. TALIS 3S)

For questions, feature requests, training requests, and bug reports, please write to ralsa@ineri.org or use the contact web-form.