/SGP

Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data. Functions use quantile regression to estimate the conditional density associated with each student's achievement history. Percentile growth projections/trajectories are calculated using the coefficient matrices derived from the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets. SystemRequirements: (PDF)LaTeX (http://www.latex-project.org/) with pdfpages package for studentGrowthPlot option in visualizeSGP to bind together student growth plots into school catalogs.

Primary LanguageROtherNOASSERTION

The SGP Package

Student Growth Percentiles & Percentile Growth Trajectories/Projections

The SGP Package (SGP, 2012) is an open source package built for the open source R software environment (R Development Core Team, 2012). The classes, functions and data within the SGP package are used to calculate student growth percentiles and percentile growth projections/trajectories using large scale, longitudinal assessment data. The methodology uses quantile regression to estimate the conditional density associated associated with each student's achievement history. Percentile growth projections/trajectories are calculated using the coefficient matrices derived from the quantile regression analyses and specify the percentile growth required for students to reach future achievement targets.

Install stable CRAN release

install.packages("SGP")
require(SGP)

Install latest development release from Github

install.packages("devtools")
require(devtools)
install_github("SGP", "SchoolView", args="--byte-compile")
require(SGP)

To install from Github you might need: Windows: Rtools (http://cran.r-project.org/bin/windows/Rtools/), OS X: xcode (from the app store), Linux: apt-get install r-base-dev (or similar).

Analysis of data utilizing the SGP methodology requires, broadly, two steps: Data Preparation and Data Analysis with these two steps consisting of several substeps:

  • Data Preparation
    • Preparation of data into LONG format.
  • Data Analysis
    • prepareSGP
    • analyzeSGP
    • combineSGP
    • summarizeSGP
    • visualizeSGP
    • outputSGP

Data Preparation

The following provides thorough SGP data formatting/preparation specifications for utilizing the utility function of the package (SGP:2012). To help illustrate these specifications there is an embedded data set, , within the package. The development team uses this data set frequently to test new features included in the package. Ensuring your data is set up in the proper format will minimize later efforts to run analyses. The goal, if you supply properly formatted data, is to make generation of student growth data and associated visualizations as easy as abc.

Data must be in long format

The first and most fundamental requirement for the data is that it must be in long, as opposed to wide, format. For our purposes, this means that each row represents a unique student by content area by year combination. This uniqueness is relaxed somewhat with the addition of a VALID_CASE variable that defines which case is unique if there are duplicate records by student, content area and year. Thus, in the final long file, each student, by content area by year by valid case identifier must be unique. By contrast, a row in the wide data format would contain all available information for a single student. For example, here are the first four rows (only the first 8 columns) of the sample data:

> sgpData_LONG[1:4,1:7]
ID LAST_NAME FIRST_NAME CONTENT_AREA   YEAR GRADE SCALE_SCORE
1 1000079   Nixon  Daniela MATHEMATICS 2006_2007   8     463
2 1000079   Nixon  Daniela MATHEMATICS 2007_2008   9     519
3 1000079   Nixon  Daniela   READING 2006_2007   8     587
4 1000079   Nixon  Daniela   READING 2007_2008   9     614

Notice that the same student is in each row, but that the rows represent different grades and content area combinations. This is what is meant by long formatted data.

Required Variables

The following list gives the columns that are requires for the calculation of Student Growth Percentiles and how they should be formatted (if applicable).

  • ID This column contains the unique student identifiers. Values will be set to class character.

  • CONTENT_AREA This column describes the content area for a given row. Most datasets would presumably contain MATHEMATICS and READING, but other values are possible. These values must be capitalized and match the states’ assessment information contained in the SGPstateData object that comes embedded within the SGP package. Please contact @dbetebenner to have assessment data added to this object. These values should be of class character.

  • YEAR This column gives either the academic year (e.g., 2006_2007 as in the sample data) or the year in which the assessment took place (e.g., 2007). If the latter form is used, the class of this column should be set to integer. Hyphens may NOT be used (e.g., 2006-07).

  • GRADE The grade in which the assessment was administered. The column of this class should be set to integer.

  • SCALE_SCORE The assessment scale score for each observation. This column’s class should be set to integer or numeric.

  • VALID_CASE This column identifies those students who should be included in subsequent analyses (value set to VALID_CASE) and those that should not be included (value set to INVALID_CASE. Duplicate cases are often left in the data and flagged as an INVALID_CASE.

Secondary Columns

Although these columns are not required for Growth Percentile analyses, they are required for Growth to Standard analyses, and/or the visualization and reporting functionality:

  • ACHIEVEMENT_LEVEL The achievement or proficiency category associated with each observed scale score. Values in this column should be set to factor, and should match the assessment program information included in contained in the SGPstateData object. ACHIEVEMENT_LEVEL should be a factor.

  • FIRST_NAME Student first name. A character or a factor.

  • LAST_NAME Student last name. A character or a factor.

  • SCHOOL_NUMBER Unique identifier for the school/institution in which a student is enrolled in a given year. Either an integer or factor.

  • SCHOOL_NAME Name of the school/institution in which a student is enrolled in a given year. A character string or factor.

  • DISTRICT_NUMBER A unique identifier for the district/educational authority in which a student is enrolled in a given year. Either a factor or integer.

  • DISTRICT_NAME District/educational authority name in which a student is enrolled in a given year. A character string or factor.

  • STATE_ENROLLMENT_STATUS Binary indicator of whether the student was continuously enrolled in the state and should be included in summary statistics. Indicator must be a factor, preferably with informative labels such as those in ; Enrolled State: Yes and Enrolled State: No.

  • DISTRICT_ENROLLMENT_STATUS Binary indicator of whether the student was continuously enrolled and should be included in district summary statistics. Indicator must be a factor, preferably with informative labels such as those in ; Enrolled District: Yes and Enrolled District: No.

  • SCHOOL_ENROLLMENT_STATUS Binary indicator of whether the student was continuously enrolled and should be included in school summary statistics. Indicator must be a factor, preferably with informative labels such as those in ; Enrolled School: Yes and Enrolled School: No.

  • GENDER DO WE WANT THIS AND OTHER DEMOGRAPHICS? USER CAN CHANGE SUMMARY GROUPS IN summarizeSGP.

  • ETHNICITY Ethnicity needed for randomNames, so maybe these two...

A number of other demographic variables can be provided when creating summaries.

Variable Name Lookup Table

If a user does not wish to rename the columns in their data to match the conventions used in the SGP package listed above, the Variable_Lookup_Table option can be used. The user must supply an appropriate list of variable names in the var.names argument of the prepareSGP function. For example, if a state has a unique student identifier named ``My_Student_ID" and an assessment subject variable named ``My_Subject" (all other variable names match), an example call to prepareSGP would include this argument:

My_State_Data <- prepareSGP(..., 
   var.names=list(ID="MyStudent_ID", CONTENT_AREA="My_Subject"))

The var.names list must include all required columns that do not match the conventions, as well as all secondary columns needed for summarization and reporting.

Data Analysis

Once a dataset is properly formatted, a comprehensive analysis can be conducted using abcSGP. An example of the call using the sample data is below.

Demonstration_SGP <- abcSGP(sgpData_LONG)

This call returns an object of class SGP named Demonstration_SGP object which contains student growth percentiles and other information, but it also produces goodness of fit and visualization folders containing files on those two topics. The function accepts multiple arguments detailed in the documentation.

Bibliography

Betebenner, D. W., VanIwaarden, A., and Domingue, B. (2012). SGP: An R Package for the Calculation and Visualization of Student Growth Percentiles & Percentile Growth Trajectories.

R Development Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 3-900051-07-0.