# SGP Data Preparation

#### Damian W Betebenner & Adam R Van Iwaarden

#### September 26th, 2017

# Introduction

There a two common formats for representing longitudinal (time dependent) student assessment data: WIDE and LONG format. For WIDE format data, each case/row represents a unique student and columns represent variables associated with the student at different times. For LONG format data, time dependent data for the student is spread out across multiple rows in the data set. The SGPdata package, installed when one installs the SGP package, includes exemplar WIDE and LONG data sets (sgpData and sgpData_LONG, respectively) to assist in setting up your data.

Deciding whether to format in WIDE or LONG format is driven by many conditions. In terms of the analyses that can be performed using the SGP package, the WIDE data format is used by the lower level functions `studentGrowthPercentiles` and `studentGrowthProjections` whereas the higher level wrapper functions utilize the LONG data format. For all but the simplest, one-off, analyses, you’re likely better off formatting your data in the LONG format and using the higher level functions. This is particularly true is you plan on running SGP analyses operationally year after year where LONG data has numerous preparation and storage benefits over WIDE data.

# WIDE data format: sgpData

Longitudinal data in WIDE format is usually the most “intuitive” longitudinal format for those new to longitudinal/time-dependent data. Each row of the data set provides all the data for the individual case with the variable names indicating what time period the data is from. Though intuitive, the data is often difficult to work with, particularly in situations where data is frequently added to the

The data set `sgpData` is an anonymized, panel data set comprisong 5 years of annual, vertically scaled, assessment data in WIDE format. This exemplar data set models the format for data used with the lower level `studentGrowthPercentiles` and `studentGrowthProjections` functions.

    > head(sgpData)
            ID GRADE_2016 GRADE_2017 GRADE_2018 GRADE_2019 GRADE_2020 SS_2016 SS_2017 SS_2018 SS_2019 SS_2020
    1: 1000185         NA         NA         NA         NA          7      NA      NA      NA      NA     520
    2: 1000486          3          4          5          6          7     524     548     607     592     656
    3: 1000710          8         NA         NA         NA         NA     713      NA      NA      NA      NA
    4: 1000715         NA         NA          4          5          6      NA      NA     469     492     551
    5: 1000803         NA          5         NA         NA         NA      NA     558      NA      NA      NA
    6: 1000957          5          6          7          8         NA     651     660     666     663      NA

The Wide data format illustrated by `sgpData` and utilized by the SGP package can accomodate any number of occurrences but must follow a specific column order. Variable names are irrelevant, position in the data set is what’s important:

- The first column must provide a unique student identifier.
- The next set of columns must provide the grade level/time associated with the students assessment occurrences.
- The next set of columns must provide the numeric scores associated with the students assessment occurrences.

In `sgpData` above, the first column, ID, provides the unique student identifier. The next 5 columns, GRADE_2013, GRADE_2014, GRADE_2015, GRADE_2016, and GRADE_2017, provide the grade level of the student assessment score in each of the 5 years. The last 5 columns, SS_2013, SS_2014, SS_2015, SS_2016, and SS_2017, provide the scale scores associated with the student in each of the 5 years. In most cases the student does not have 5 years of test data so the data shows the missing value (NA).

Using wide-format data like `sgpData` with the SGP package is, in general, straight forward.

    > sgp_g4 <- studentGrowthPercentiles(
    +       panel.data=sgpData,
    +       sgp.labels=list(my.year=2015, my.subject="Reading"),
    +       percentile.cuts=c(1,35,65,99),
    +       grade.progression=c(3,4))

Please consult the SGP data analysis vignette for more comprehensive documentation on how to use `sgpData` (and WIDE data formats in general) for SGP analyses.

# LONG data format: sgpData_LONG

The data set `sgpData_LONG` is an anonymized, panel data set comprising 5 years of annual, vertcially scaled, assessment data in LONG format for two content areas (ELA and Mathematics). This exemplar data set models the format for data used with the higher level functions `abcSGP`, `prepareSGP`, `analyzeSGP`, `combineSGP`, `summarizeSGP`, `visualizeSGP`, and `outputSGP`

    > head(sgpData_LONG)
       VALID_CASE CONTENT_AREA      YEAR      ID LAST_NAME FIRST_NAME GRADE SCALE_SCORE    ACHIEVEMENT_LEVEL       GENDER ETHNICITY FREE_REDUCED_LUNCH_STATUS ELL_STATUS IEP_STATUS GIFTED_AND_TALENTED_PROGRAM_STATUS SCHOOL_NUMBER                  SCHOOL_NAME  EMH_LEVEL DISTRICT_NUMBER                DISTRICT_NAME SCHOOL_ENROLLMENT_STATUS DISTRICT_ENROLLMENT_STATUS STATE_ENROLLMENT_STATUS
    1: VALID_CASE  MATHEMATICS 2017_2018 1000372   Daniels      Corey     3         435           Proficient Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes
    2: VALID_CASE  MATHEMATICS 2018_2019 1000372   Daniels      Corey     4         461           Proficient Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes
    3: VALID_CASE  MATHEMATICS 2019_2020 1000372   Daniels      Corey     5         444 Partially Proficient Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes
    4: VALID_CASE      READING 2017_2018 1000372   Daniels      Corey     3         523 Partially Proficient Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes
    5: VALID_CASE      READING 2018_2019 1000372   Daniels      Corey     4         540 Partially Proficient Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes
    6: VALID_CASE      READING 2019_2020 1000372   Daniels      Corey     5         473       Unsatisfactory Gender: Male  Hispanic   Free Reduced Lunch: Yes   ELL: Yes    IEP: No    Gifted and Talented Program: No          1851 Silk-Royal Elementary School Elementary             470 Apple Valley School District     Enrolled School: Yes     Enrolled District: Yes     Enrolled State: Yes

We recommend LONG formated data for use with operational analyses. Managing data in long format is more simple than data in the wide format. For example, when updating analyses with another year of data, the data is appended onto the bottom of the currently existing long data set. All higher level functions in the SGP package are designed for use with LONG format data. In addition, these functions often assume the existence of state specific meta-data in the embedded SGPstateData meta-data. See the SGP package documentation for more comprehensive documentation on how to use `sgpData` for SGP calculations.

There are 7 required variables when using LONG data with SGP analyses: `VALID_CASE`, `CONTENT_AREA`, `YEAR`, `ID`, `SCALE_SCORE`, `GRADE` and `ACHIEVEMENT_LEVEL` (on required if running student growth projections). `LAST_NAME` and `FIRST_NAME` are required if creating individual level student growth and achievement plots. All other variables are demographic/student categorization variables used for creating student aggregates by the `summarizeSGP` function.

The `sgpData_LONG` data set contains data for 5 years across 2 content areas (ELA and Mathematics)

# LONG data format with time: sgptData_LONG

The data set `sgptData_LONG` is an anonymized, panel data set comprising 8 windows (3 windows annually) of assessment data in LONG format for 3 content areas (Early Literacy, Mathematics, and Reading). This data set is similar to the `sgpData_LONG` data set without the demographic variables and with an additional `DATE` variable indicating the date associated with the student assessment record.

    > head(sgptData_LONG)
       VALID_CASE   CONTENT_AREA        YEAR        ID GRADE       DATE SCALE_SCORE SCALE_SCORE_RASCH COUNTRY STATE SEM ACHIEVEMENT_LEVEL
    1: VALID_CASE EARLY_LITERACY 2014_2015.2  ANON_130   K.2 2015-01-14         622            0.3449      US    OH  55              <NA>
    2: VALID_CASE EARLY_LITERACY 2014_2015.2 ANON_1314   1.2 2015-01-08         500           -0.6556      US    NJ  49              <NA>
    3: VALID_CASE EARLY_LITERACY 2014_2015.2  ANON_133   K.2 2015-01-17         566           -0.1010      US    OH  57              <NA>
    4: VALID_CASE EARLY_LITERACY 2014_2015.2 ANON_1429   2.2 2015-03-12         621            0.3368      US    WI  58              <NA>
    5: VALID_CASE EARLY_LITERACY 2014_2015.2 ANON_1498   K.2 2015-01-09         577           -0.0129      US    IL  57              <NA>
    6: VALID_CASE EARLY_LITERACY 2014_2015.2 ANON_1533   K.2 2015-01-23         443           -1.2131      US    IL  38              <NA>

# LONG teacher-student lookup: sgpData_INSTRUCTOR_NUMBER

The data set `sgpData_INSTRUCTOR_NUMBER` is an anonymized, student-instructor lookup table that provides insturctor information associated with each students test record. Note that just as each teacher can (and will) have more than 1 student associated with them, a student can have more than one teacher associated with their test record. That is, multiple teachers could be assigned to the student in a single content area for a given year.

    > head(sgpData_INSTRUCTOR_NUMBER)
            ID CONTENT_AREA      YEAR INSTRUCTOR_NUMBER INSTRUCTOR_LAST_NAME INSTRUCTOR_FIRST_NAME INSTRUCTOR_WEIGHT INSTRUCTOR_ENROLLMENT_STATUS
    1: 1000372  MATHEMATICS 2017_2018         185103004                 Kang                Alexis               1.0     Enrolled Instructor: Yes
    2: 1000372  MATHEMATICS 2018_2019         185104002                Mills                  Karl               1.0     Enrolled Instructor: Yes
    3: 1000372  MATHEMATICS 2019_2020         185105002             Intavong               Michael               0.2     Enrolled Instructor: Yes
    4: 1000372  MATHEMATICS 2019_2020         185105004                Price                 Angel               0.8     Enrolled Instructor: Yes
    5: 1000372      READING 2017_2018         185103003               Mccord             Guadalupe               1.0     Enrolled Instructor: Yes
    6: 1000372      READING 2018_2019         185104001               Rivera               Kailynn               0.7     Enrolled Instructor: Yes

# Plotly test

# Contributions & Requests

If you have a contribution or topic request for this vignette, don’t hesitate to write or set up an issue on GitHub.