Theresa_Szczepanski
hw2
MCAS
Author

Theresa Szczepanski

Published

October 11, 2022

Code 
library(tidyverse)
library(lubridate)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Homework Assignment

To complete today’s challenge I tried to:

  • Read in a data set

  • Clean the data as needed using dplyr and related tidyverse packages.

  • provide a narrative about the data set and the variables in your data set, including what type of data each variable is in a visually appealing way to non-experts

  • Identify potential research questions that your data set can help answer.

MCAS_2022.csv

To read in the data, I used the following process:

  • Filter out Student Names and then Read in the data
  • Examine the summary
  • Identify information to filter, mutate, and rename on the read in
  • note variables that require further investigation

After examining the summary (see tab), I chose to

Filter:

  • SchoolID : There are several variables that identify our school, I removed all but one, testschoolcode.

  • StudentPrivacy: I left the sasid variable which is a student identifier number, but eliminated all values corresponding to students’ names.

  • dis: We are a charter school within our own unique district, therefore any “district level” data is identical to our “school level” data.

Rename

I currently have not renamed variables, but I have a list of variables for which I need to talk with my administration to access a key to understand what they represent. Ideally, after this, I would put

  • an E_ before all ELA MCAS student performance metric variables
  • an M_ before all Math MCAS student performance metric variables
  • an S_ before all Science MCAS student performance metric variables
  • an SI_ before all student demographic characteristic identifying variables

Mutate

I left as doubles

  • variables that measured scores on specific MCAS items e.g., mitem1
  • variables that measured student growth percentiles (sgp)
  • variables that counted a student’s years in the school system or state.

Recode to char

  • variables that are nominal, e.g., town

Refactor as ord

  • variables that are ordinal, e.g., mperflev.

Recode to date

-dob using lubridate.

Code 
#Filter, rename variables, and mutate values of variables on read-in

MCAS_2022<-read_csv("_data/PrivateSpring2022_MCAS_full_preliminary_results_04830305.csv",
                    skip=1)%>%
  select(-c("sprp_dis", "sprp_sch", "sprp_dis_name", "sprp_sch_name", "sprp_orgtype",
  "schtype", "testschoolname", "yrsindis", "conenr_dis"))%>%

#Recode all nominal variables as characters
  mutate(testschoolcode = as.character(testschoolcode))%>%
  # mutate(sasid = as.character(sasid))%>%
  mutate(highneeds = as.character(highneeds))%>%
  mutate(lowincome = as.character(lowincome))%>%
  mutate(title1 = as.character(title1))%>%
  mutate(ever_EL = as.character(ever_EL))%>%
  mutate(EL = as.character(EL))%>%
  mutate(EL_FormerEL = as.character(EL_FormerEL))%>%
  mutate(FormerEL = as.character(FormerEL))%>%
  mutate(ELfirstyear = as.character(ELfirstyear))%>%
  mutate(IEP = as.character(IEP))%>%
  mutate(plan504 = as.character(plan504))%>%
  mutate(firstlanguage = as.character(firstlanguage))%>%
  mutate(nature0fdis = as.character(natureofdis))%>%
  mutate(spedplacement = as.character(spedplacement))%>%
  mutate(town = as.character(town))%>%
  mutate(ssubject = as.character(ssubject))%>%


#Recode all ordinal variable as factors

  mutate(grade = as.factor(grade))%>%
  mutate(levelofneed = as.factor(levelofneed))%>%
  mutate(eperf2 = recode_factor(eperf2,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                  .ordered = TRUE))%>%
  mutate(eperflev = recode_factor(eperflev,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                   "DNT" = "DNT",
                                   "ABS" = "ABS",
                                  .ordered = TRUE))%>%
    mutate(mperf2 = recode_factor(mperf2,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                  .ordered = TRUE))%>%
  mutate(mperflev = recode_factor(mperflev,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                   "INV" = "INV",
                                   "ABS" = "ABS",
                                  .ordered = TRUE))%>%

  # The science variables contain a mixture of legacy performance levels and
  # next generation performance levels which needs to be addressed in the ordering
  # of these factors.
  mutate(sperf2 = recode_factor(sperf2,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                  .ordered = TRUE))%>%
  mutate(sperflev = recode_factor(sperflev,
                                   "E" = "E",
                                   "M" = "M",
                                   "PM" = "PM",
                                   "NM"= "NM",
                                   "INV" = "INV",
                                   "ABS" = "ABS",
                                  .ordered = TRUE))%>%
  #recode DOB using lubridate
  mutate(dob = mdy(dob,
  quiet = FALSE,
  tz = NULL,
  locale = Sys.getlocale("LC_TIME"),
  truncated = 0
))





MCAS_2022
Code 
# examine the summary to decide how to best set up our data frame

 print(summarytools::dfSummary(MCAS_2022,
                         varnumbers = FALSE,
                         plain.ascii  = FALSE,
                         style        = "grid",
                         graph.magnif = 0.70,
                        valid.col    = FALSE),
       method = 'render',
       table.classes = 'table-condensed')

Data Frame Summary

MCAS_2022

Dimensions: 495 x 255
Duplicates: 0
Variable Stats / Values Freqs (% of Valid) Graph Missing
adminyear [numeric] 1 distinct value
2022:495(100.0%)
 0 (0.0%)
testschoolcode [character] 1. 4830305
495(100.0%)
 0 (0.0%)
grade [factor]
1. 5
2. 6
3. 7
4. 8
5. 9
6. 10
89(18.0%)
91(18.4%)
92(18.6%)
91(18.4%)
69(13.9%)
63(12.7%)
 0 (0.0%)
gradesims [numeric]
Mean (sd) : 7.3 (1.6)
min ≤ med ≤ max:
5 ≤ 7 ≤ 10
IQR (CV) : 3 (0.2)
5:89(18.0%)
6:91(18.4%)
7:92(18.6%)
8:91(18.4%)
9:69(13.9%)
10:63(12.7%)
 0 (0.0%)
dob [Date]
min : 2005-02-08
med : 2008-11-29
max : 2011-10-17
range : 6y 8m 9d
 427 distinct values 0 (0.0%)
gender [character]
1. F
2. M
3. N
242(48.9%)
251(50.7%)
2(0.4%)
 0 (0.0%)
race [character]
1. A
2. B
3. H
4. M
5. N
6. W
8(1.6%)
6(1.2%)
25(5.1%)
41(8.3%)
5(1.0%)
410(82.8%)
 0 (0.0%)
yrsinmass [character]
1. 1
2. 2
3. 3
4. 4
5. 5+
11(2.2%)
18(3.6%)
19(3.8%)
16(3.2%)
431(87.1%)
 0 (0.0%)
yrsinmass_num [numeric]
Mean (sd) : 7.3 (2.4)
min ≤ med ≤ max:
1 ≤ 8 ≤ 12
IQR (CV) : 3 (0.3)
 12 distinct values 0 (0.0%)
yrsinsch [numeric]
Mean (sd) : 2.6 (1.5)
min ≤ med ≤ max:
1 ≤ 2 ≤ 6
IQR (CV) : 3 (0.6)
1:159(32.1%)
2:116(23.4%)
3:80(16.2%)
4:77(15.6%)
5:31(6.3%)
6:32(6.5%)
 0 (0.0%)
highneeds [character]
1. 0
2. 1
290(58.6%)
205(41.4%)
 0 (0.0%)
lowincome [character]
1. 0
2. 1
369(74.5%)
126(25.5%)
 0 (0.0%)
title1 [character]
1. 0
2. 1
393(79.4%)
102(20.6%)
 0 (0.0%)
ever_EL [character] 1. 1
20(100.0%)
 475 (96.0%)
EL [character]
1. 0
2. 1
488(98.6%)
7(1.4%)
 0 (0.0%)
EL_FormerEL [character]
1. 0
2. 1
480(97.0%)
15(3.0%)
 0 (0.0%)
FormerEL [character]
1. 0
2. 1
487(98.4%)
8(1.6%)
 0 (0.0%)
ELfirstyear [character]
All NA's
 495 (100.0%)
IEP [character]
1. 0
2. 1
381(77.0%)
114(23.0%)
 0 (0.0%)
plan504 [character]
1. 0
2. 1
443(89.5%)
52(10.5%)
 0 (0.0%)
firstlanguage [character]
1. 2
2. 267
3. 415
4. 6
5. 630
6. 7
7. 759
1(0.2%)
481(97.2%)
2(0.4%)
8(1.6%)
1(0.2%)
1(0.2%)
1(0.2%)
 0 (0.0%)
natureofdis [numeric]
Mean (sd) : 6.9 (1.9)
min ≤ med ≤ max:
2 ≤ 7 ≤ 12
IQR (CV) : 3 (0.3)
2:1(0.9%)
3:9(7.8%)
4:1(0.9%)
5:19(16.5%)
7:40(34.8%)
8:38(33.0%)
11:5(4.3%)
12:2(1.7%)
 380 (76.8%)
levelofneed [factor]
1. 1
2. 2
3. 3
4. 4
3(2.6%)
14(12.2%)
97(84.3%)
1(0.9%)
 380 (76.8%)
spedplacement [character]
1. 0
2. 1
3. 10
4. 20
380(76.8%)
1(0.2%)
104(21.0%)
10(2.0%)
 0 (0.0%)
town [character]
1. 239
2. 310
3. 52
4. 145
5. 182
6. 36
7. 20
8. 261
9. 171
10. 231
[ 11 others ]
257(51.9%)
54(10.9%)
33(6.7%)
30(6.1%)
23(4.6%)
20(4.0%)
18(3.6%)
12(2.4%)
11(2.2%)
8(1.6%)
29(5.9%)
 0 (0.0%)
county [character]
1. Barnstable
2. Plymouth
56(11.3%)
439(88.7%)
 0 (0.0%)
octenr [numeric]
Min : 0
Mean : 1
Max : 1
0:13(2.6%)
1:482(97.4%)
 0 (0.0%)
conenr_sch [numeric] 1 distinct value
1:55(100.0%)
 440 (88.9%)
conenr_sta [numeric] 1 distinct value
1:61(100.0%)
 434 (87.7%)
access_part [numeric] 1 distinct value
1:7(100.0%)
 488 (98.6%)
ealt [logical]
All NA's
 495 (100.0%)
ecomplexity [logical]
All NA's
 495 (100.0%)
emode [character] 1. O
422(100.0%)
 73 (14.7%)
eteststat [character]
1. NTA
2. NTO
3. T
4(0.9%)
1(0.2%)
421(98.8%)
 69 (13.9%)
wptopdev [logical]
All NA's
 495 (100.0%)
wpcompconv [logical]
All NA's
 495 (100.0%)
eitem1 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:95(22.6%)
1:326(77.4%)
 74 (14.9%)
eitem2 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:132(31.4%)
1:289(68.6%)
 74 (14.9%)
eitem3 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:91(21.6%)
1:330(78.4%)
 74 (14.9%)
eitem4 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:79(18.8%)
1:342(81.2%)
 74 (14.9%)
eitem5 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:109(25.9%)
1:246(58.4%)
2:66(15.7%)
 74 (14.9%)
eitem6 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:97(23.0%)
1:324(77.0%)
 74 (14.9%)
eitem7 [numeric]
Mean (sd) : 0.8 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:95(22.6%)
1:307(72.9%)
2:19(4.5%)
 74 (14.9%)
eitem8 [numeric]
Mean (sd) : 0.8 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:102(24.2%)
1:292(69.4%)
2:27(6.4%)
 74 (14.9%)
eitem9 [numeric]
Mean (sd) : 1.3 (1.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 7
IQR (CV) : 0 (1.2)
0:79(18.8%)
1:285(67.7%)
2:10(2.4%)
4:20(4.8%)
6:20(4.8%)
7:7(1.7%)
 74 (14.9%)
eitem10 [numeric]
Mean (sd) : 1.2 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 2 (0.7)
0:107(25.4%)
1:124(29.5%)
2:190(45.1%)
 74 (14.9%)
eitem11 [numeric]
Mean (sd) : 1.2 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.5)
0:54(12.8%)
1:208(49.4%)
2:159(37.8%)
 74 (14.9%)
eitem12 [numeric]
Mean (sd) : 2.5 (2.3)
min ≤ med ≤ max:
0 ≤ 1 ≤ 8
IQR (CV) : 3 (0.9)
0:69(16.4%)
1:152(36.1%)
2:33(7.8%)
3:6(1.4%)
4:80(19.0%)
5:7(1.7%)
6:50(11.9%)
7:18(4.3%)
8:6(1.4%)
 74 (14.9%)
eitem13 [numeric]
Mean (sd) : 1.4 (1.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 7
IQR (CV) : 1 (1)
0:88(21.0%)
1:218(51.9%)
2:56(13.3%)
3:8(1.9%)
4:27(6.4%)
5:3(0.7%)
6:18(4.3%)
7:2(0.5%)
 75 (15.2%)
eitem14 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:104(24.6%)
1:318(75.4%)
 73 (14.7%)
eitem15 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.7)
0:101(23.9%)
1:260(61.6%)
2:61(14.5%)
 73 (14.7%)
eitem16 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:76(18.0%)
1:346(82.0%)
 73 (14.7%)
eitem17 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:122(28.9%)
1:300(71.1%)
 73 (14.7%)
eitem18 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:110(26.1%)
1:312(73.9%)
 73 (14.7%)
eitem19 [numeric]
Mean (sd) : 0.9 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:110(26.1%)
1:234(55.5%)
2:78(18.5%)
 73 (14.7%)
eitem20 [numeric]
Mean (sd) : 1 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:61(14.5%)
1:281(66.6%)
2:80(19.0%)
 73 (14.7%)
eitem21 [numeric]
Mean (sd) : 1 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.5)
0:64(15.2%)
1:309(73.2%)
2:49(11.6%)
 73 (14.7%)
eitem22 [numeric]
Mean (sd) : 1.4 (1.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 7
IQR (CV) : 0 (1.1)
0:51(12.1%)
1:310(73.5%)
2:10(2.4%)
4:23(5.5%)
6:19(4.5%)
7:9(2.1%)
 73 (14.7%)
eitem23 [numeric]
Mean (sd) : 0.8 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:124(29.4%)
1:252(59.7%)
2:46(10.9%)
 73 (14.7%)
eitem24 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:81(19.2%)
1:287(68.0%)
2:54(12.8%)
 73 (14.7%)
eitem25 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:84(19.9%)
1:285(67.5%)
2:53(12.6%)
 73 (14.7%)
eitem26 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:121(28.7%)
1:301(71.3%)
 73 (14.7%)
eitem27 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:89(21.1%)
1:272(64.5%)
2:61(14.5%)
 73 (14.7%)
eitem28 [numeric]
Mean (sd) : 0.9 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:86(20.4%)
1:283(67.1%)
2:53(12.6%)
 73 (14.7%)
eitem29 [numeric]
Mean (sd) : 0.8 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:123(29.1%)
1:256(60.7%)
2:43(10.2%)
 73 (14.7%)
eitem30 [numeric]
Mean (sd) : 1.2 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.6)
0:67(15.9%)
1:219(51.9%)
2:136(32.2%)
 73 (14.7%)
eitem31 [numeric]
Mean (sd) : 3.2 (2.2)
min ≤ med ≤ max:
0 ≤ 3 ≤ 8
IQR (CV) : 4 (0.7)
0:25(6.9%)
1:70(19.4%)
2:81(22.5%)
3:21(5.8%)
4:69(19.2%)
5:14(3.9%)
6:55(15.3%)
7:17(4.7%)
8:8(2.2%)
 135 (27.3%)
eitem32 [numeric]
Mean (sd) : 3.2 (1.7)
min ≤ med ≤ max:
0 ≤ 3.5 ≤ 8
IQR (CV) : 2 (0.5)
0:5(5.4%)
1:5(5.4%)
2:32(34.8%)
3:4(4.3%)
4:34(37.0%)
5:1(1.1%)
6:10(10.9%)
8:1(1.1%)
 403 (81.4%)
eitem33 [logical]
All NA's
 495 (100.0%)
eitem34 [logical]
All NA's
 495 (100.0%)
eitem35 [logical]
All NA's
 495 (100.0%)
eitem36 [logical]
All NA's
 495 (100.0%)
eitem37 [logical]
All NA's
 495 (100.0%)
eitem38 [logical]
All NA's
 495 (100.0%)
eitem39 [logical]
All NA's
 495 (100.0%)
eitem40 [logical]
All NA's
 495 (100.0%)
erawsc [numeric]
Mean (sd) : 33 (8.2)
min ≤ med ≤ max:
6 ≤ 34 ≤ 47
IQR (CV) : 10 (0.2)
 39 distinct values 73 (14.7%)
emcpts [numeric]
Mean (sd) : 18.3 (4.1)
min ≤ med ≤ max:
3 ≤ 19 ≤ 26
IQR (CV) : 5 (0.2)
 24 distinct values 73 (14.7%)
eorpts [numeric]
Mean (sd) : 14.7 (5.4)
min ≤ med ≤ max:
1 ≤ 15 ≤ 28
IQR (CV) : 8 (0.4)
 28 distinct values 73 (14.7%)
eperpospts [numeric]
Mean (sd) : 66.3 (16.3)
min ≤ med ≤ max:
12 ≤ 69 ≤ 94
IQR (CV) : 20 (0.2)
 63 distinct values 73 (14.7%)
escaleds [numeric]
Mean (sd) : 501.3 (18.5)
min ≤ med ≤ max:
442 ≤ 502 ≤ 545
IQR (CV) : 25 (0)
 74 distinct values 74 (14.9%)
eperflev [ordered, factor]
1. E
2. M
3. PM
4. NM
5. DNT
6. ABS
24(5.6%)
206(48.4%)
169(39.7%)
22(5.2%)
1(0.2%)
4(0.9%)
 69 (13.9%)
eperf2 [ordered, factor]
1. E
2. M
3. PM
4. NM
24(5.7%)
206(48.9%)
169(40.1%)
22(5.2%)
 74 (14.9%)
enumin [numeric] 1 distinct value
1:421(100.0%)
 74 (14.9%)
eassess [numeric]
Min : 0
Mean : 1
Max : 1
0:4(0.9%)
1:421(99.1%)
 70 (14.1%)
esgp [numeric]
Mean (sd) : 52.6 (29.6)
min ≤ med ≤ max:
1 ≤ 54 ≤ 99
IQR (CV) : 48.5 (0.6)
 96 distinct values 109 (22.0%)
idea1 [character]
1. 0
2. 1
3. 2
4. 3
5. 4
6. 5
7. BL
8. OT
70(16.4%)
79(18.5%)
138(32.4%)
97(22.8%)
27(6.3%)
6(1.4%)
7(1.6%)
2(0.5%)
 69 (13.9%)
conv1 [character]
1. 0
2. 1
3. 2
4. 3
5. BL
6. OT
34(8.0%)
121(28.4%)
140(32.9%)
122(28.6%)
7(1.6%)
2(0.5%)
 69 (13.9%)
idea2 [character]
1. 0
2. 1
3. 2
4. 3
5. 4
6. 5
7. BL
8. OT
21(4.9%)
121(28.4%)
146(34.3%)
96(22.5%)
27(6.3%)
9(2.1%)
4(0.9%)
2(0.5%)
 69 (13.9%)
conv2 [character]
1. 0
2. 1
3. 2
4. 3
5. BL
6. OT
33(7.7%)
121(28.4%)
145(34.0%)
121(28.4%)
4(0.9%)
2(0.5%)
 69 (13.9%)
idea3 [logical]
All NA's
 495 (100.0%)
conv3 [logical]
All NA's
 495 (100.0%)
eattempt [character]
1. F
2. N
3. P
421(98.8%)
4(0.9%)
1(0.2%)
 69 (13.9%)
malt [logical]
All NA's
 495 (100.0%)
mcomplexity [logical]
All NA's
 495 (100.0%)
mmode [character] 1. O
424(100.0%)
 71 (14.3%)
mteststat [character]
1. NTA
2. NTO
3. T
2(0.5%)
1(0.2%)
423(99.3%)
 69 (13.9%)
mitem1 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:94(22.3%)
1:328(77.7%)
 73 (14.7%)
mitem2 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:127(30.1%)
1:295(69.9%)
 73 (14.7%)
mitem3 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:174(41.2%)
1:248(58.8%)
 73 (14.7%)
mitem4 [numeric]
Mean (sd) : 1.1 (1.1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 2 (1)
0:156(37.1%)
1:148(35.2%)
2:55(13.1%)
3:42(10.0%)
4:19(4.5%)
 75 (15.2%)
mitem5 [numeric]
Min : 0
Mean : 0.4
Max : 1
0:237(56.3%)
1:184(43.7%)
 74 (14.9%)
mitem6 [numeric]
Mean (sd) : 0.9 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:151(35.8%)
1:219(51.9%)
2:19(4.5%)
3:22(5.2%)
4:11(2.6%)
 73 (14.7%)
mitem7 [numeric]
Mean (sd) : 0.6 (0.7)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1 (1.1)
0:213(50.5%)
1:159(37.7%)
2:50(11.8%)
 73 (14.7%)
mitem8 [numeric]
Mean (sd) : 0.8 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:182(43.4%)
1:167(39.9%)
2:54(12.9%)
3:7(1.7%)
4:9(2.1%)
 76 (15.4%)
mitem9 [numeric]
Mean (sd) : 0.8 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:150(35.5%)
1:225(53.3%)
2:27(6.4%)
3:8(1.9%)
4:12(2.8%)
 73 (14.7%)
mitem10 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:183(43.4%)
1:239(56.6%)
 73 (14.7%)
mitem11 [numeric]
Mean (sd) : 0.7 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:123(29.1%)
1:288(68.2%)
2:11(2.6%)
 73 (14.7%)
mitem12 [numeric]
Mean (sd) : 0.8 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:161(38.2%)
1:222(52.6%)
2:23(5.5%)
3:9(2.1%)
4:7(1.7%)
 73 (14.7%)
mitem13 [numeric]
Mean (sd) : 1.2 (1.3)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:156(37.0%)
1:164(38.9%)
2:24(5.7%)
3:34(8.1%)
4:44(10.4%)
 73 (14.7%)
mitem14 [numeric]
Mean (sd) : 1.1 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 0 (0.9)
0:102(24.2%)
1:229(54.3%)
2:47(11.1%)
3:16(3.8%)
4:28(6.6%)
 73 (14.7%)
mitem15 [numeric]
Mean (sd) : 0.5 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 3
IQR (CV) : 1 (1.3)
0:242(57.8%)
1:153(36.5%)
2:20(4.8%)
3:4(1.0%)
 76 (15.4%)
mitem16 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:223(53.0%)
1:198(47.0%)
 74 (14.9%)
mitem17 [numeric]
Mean (sd) : 0.5 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1 (1.1)
0:219(52.0%)
1:187(44.4%)
2:15(3.6%)
 74 (14.9%)
mitem18 [numeric]
Mean (sd) : 0.5 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1 (1.1)
0:221(52.4%)
1:186(44.1%)
2:15(3.6%)
 73 (14.7%)
mitem19 [numeric]
Min : 0
Mean : 0.3
Max : 1
0:285(67.7%)
1:136(32.3%)
 74 (14.9%)
mitem20 [numeric]
Min : 0
Mean : 0.4
Max : 1
0:242(57.3%)
1:180(42.7%)
 73 (14.7%)
mitem21 [numeric]
Min : 0
Mean : 0.8
Max : 1
0:82(19.4%)
1:340(80.6%)
 73 (14.7%)
mitem22 [numeric]
Mean (sd) : 1 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 0 (0.8)
0:81(19.2%)
1:291(69.1%)
2:19(4.5%)
3:20(4.8%)
4:10(2.4%)
 74 (14.9%)
mitem23 [numeric]
Mean (sd) : 0.8 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:157(37.2%)
1:223(52.8%)
2:16(3.8%)
3:6(1.4%)
4:20(4.7%)
 73 (14.7%)
mitem24 [numeric]
Mean (sd) : 0.9 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:165(39.1%)
1:187(44.3%)
2:46(10.9%)
3:12(2.8%)
4:12(2.8%)
 73 (14.7%)
mitem25 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:179(42.6%)
1:241(57.4%)
 75 (15.2%)
mitem26 [numeric]
Mean (sd) : 1 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:158(37.4%)
1:172(40.7%)
2:58(13.7%)
3:24(5.7%)
4:11(2.6%)
 72 (14.5%)
mitem27 [numeric]
Mean (sd) : 0.8 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.3)
0:194(46.1%)
1:181(43.0%)
2:16(3.8%)
3:14(3.3%)
4:16(3.8%)
 74 (14.9%)
mitem28 [numeric]
Mean (sd) : 0.7 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (1)
0:182(43.2%)
1:190(45.1%)
2:49(11.6%)
 74 (14.9%)
mitem29 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:208(49.4%)
1:213(50.6%)
 74 (14.9%)
mitem30 [numeric]
Mean (sd) : 0.6 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (1)
0:192(45.5%)
1:195(46.2%)
2:35(8.3%)
 73 (14.7%)
mitem31 [numeric]
Mean (sd) : 0.9 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:133(31.6%)
1:241(57.2%)
2:19(4.5%)
3:17(4.0%)
4:11(2.6%)
 74 (14.9%)
mitem32 [numeric]
Mean (sd) : 0.5 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1 (1.2)
0:240(56.9%)
1:170(40.3%)
2:12(2.8%)
 73 (14.7%)
mitem33 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:216(51.2%)
1:206(48.8%)
 73 (14.7%)
mitem34 [numeric]
Mean (sd) : 0.7 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.2)
0:190(45.1%)
1:191(45.4%)
2:20(4.8%)
3:15(3.6%)
4:5(1.2%)
 74 (14.9%)
mitem35 [numeric]
Mean (sd) : 0.8 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:168(39.8%)
1:200(47.4%)
2:33(7.8%)
3:15(3.6%)
4:6(1.4%)
 73 (14.7%)
mitem36 [numeric]
Min : 0
Mean : 0.4
Max : 1
0:238(56.5%)
1:183(43.5%)
 74 (14.9%)
mitem37 [numeric]
Mean (sd) : 1.1 (1.2)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1.1)
0:153(36.3%)
1:187(44.3%)
2:13(3.1%)
3:36(8.5%)
4:33(7.8%)
 73 (14.7%)
mitem38 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:216(51.3%)
1:205(48.7%)
 74 (14.9%)
mitem39 [numeric]
Mean (sd) : 0.3 (0.6)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1 (1.6)
0:296(70.1%)
1:106(25.1%)
2:20(4.7%)
 73 (14.7%)
mitem40 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:221(52.4%)
1:201(47.6%)
 73 (14.7%)
mitem41 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:31(49.2%)
1:32(50.8%)
 432 (87.3%)
mitem42 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:31(49.2%)
1:32(50.8%)
 432 (87.3%)
mrawsc [numeric]
Mean (sd) : 27.6 (11.2)
min ≤ med ≤ max:
0 ≤ 27 ≤ 58
IQR (CV) : 15 (0.4)
 51 distinct values 72 (14.5%)
mmcpts [numeric]
Mean (sd) : 10.5 (4)
min ≤ med ≤ max:
0 ≤ 10 ≤ 21
IQR (CV) : 5 (0.4)
 22 distinct values 72 (14.5%)
morpts [numeric]
Mean (sd) : 17.2 (8.1)
min ≤ med ≤ max:
0 ≤ 16 ≤ 38
IQR (CV) : 12 (0.5)
 38 distinct values 72 (14.5%)
mperpospts [numeric]
Mean (sd) : 50.3 (20.3)
min ≤ med ≤ max:
0 ≤ 50 ≤ 97
IQR (CV) : 28 (0.4)
 67 distinct values 72 (14.5%)
mscaleds [numeric]
Mean (sd) : 497.3 (17.6)
min ≤ med ≤ max:
440 ≤ 498 ≤ 555
IQR (CV) : 20 (0)
 80 distinct values 72 (14.5%)
mperflev [ordered, factor]
1. E
2. M
3. PM
4. NM
5. INV
6. ABS
13(3.1%)
168(39.4%)
209(49.1%)
33(7.7%)
1(0.2%)
2(0.5%)
 69 (13.9%)
mperf2 [ordered, factor]
1. E
2. M
3. PM
4. NM
13(3.1%)
168(39.7%)
209(49.4%)
33(7.8%)
 72 (14.5%)
mnumin [numeric] 1 distinct value
1:423(100.0%)
 72 (14.5%)
massess [numeric]
Min : 0
Mean : 1
Max : 1
0:2(0.5%)
1:423(99.5%)
 70 (14.1%)
msgp [numeric]
Mean (sd) : 43.7 (27.6)
min ≤ med ≤ max:
1 ≤ 40 ≤ 99
IQR (CV) : 46 (0.6)
 97 distinct values 107 (21.6%)
mattempt [character]
1. F
2. N
424(99.5%)
2(0.5%)
 69 (13.9%)
salt [logical]
All NA's
 495 (100.0%)
scomplexity [logical]
All NA's
 495 (100.0%)
smode [character]
1. O
2. P
248(96.9%)
8(3.1%)
 239 (48.3%)
steststat [character]
1. NTA
2. NTO
3. T
4. TR
2(0.6%)
54(17.3%)
250(80.1%)
6(1.9%)
 183 (37.0%)
ssubject [character]
1. 1
2. 2
3. 3
4. 6
3(2.3%)
8(6.1%)
51(38.6%)
70(53.0%)
 363 (73.3%)
sitem1 [numeric]
Min : 0
Mean : 0.9
Max : 1
0:36(14.1%)
1:220(85.9%)
 239 (48.3%)
sitem2 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:109(42.6%)
1:147(57.4%)
 239 (48.3%)
sitem3 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:110(43.0%)
1:146(57.0%)
 239 (48.3%)
sitem4 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:102(40.0%)
1:153(60.0%)
 240 (48.5%)
sitem5 [numeric]
Mean (sd) : 1 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 2 (0.7)
0:66(25.8%)
1:125(48.8%)
2:65(25.4%)
 239 (48.3%)
sitem6 [numeric]
Mean (sd) : 0.9 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.8)
0:77(30.1%)
1:119(46.5%)
2:60(23.4%)
 239 (48.3%)
sitem7 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:113(44.1%)
1:143(55.9%)
 239 (48.3%)
sitem8 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:131(51.2%)
1:125(48.8%)
 239 (48.3%)
sitem9 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:65(25.4%)
1:191(74.6%)
 239 (48.3%)
sitem10 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:85(33.2%)
1:171(66.8%)
 239 (48.3%)
sitem11 [numeric]
Mean (sd) : 0.6 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (1)
0:113(44.1%)
1:139(54.3%)
2:2(0.8%)
3:1(0.4%)
4:1(0.4%)
 239 (48.3%)
sitem12 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:102(40.0%)
1:153(60.0%)
 240 (48.5%)
sitem13 [numeric]
Mean (sd) : 0.9 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:42(16.4%)
1:186(72.7%)
2:28(10.9%)
 239 (48.3%)
sitem14 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:101(39.5%)
1:155(60.5%)
 239 (48.3%)
sitem15 [numeric]
Mean (sd) : 1.4 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 1 (0.6)
0:45(17.6%)
1:86(33.6%)
2:100(39.1%)
3:25(9.8%)
 239 (48.3%)
sitem16 [numeric]
Mean (sd) : 1.1 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 2 (0.7)
0:65(25.7%)
1:110(43.5%)
2:72(28.5%)
3:6(2.4%)
 242 (48.9%)
sitem17 [numeric]
Mean (sd) : 1 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 1 (0.8)
0:68(26.7%)
1:126(49.4%)
2:49(19.2%)
3:12(4.7%)
 240 (48.5%)
sitem18 [numeric]
Mean (sd) : 0.9 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.7)
0:70(27.3%)
1:133(52.0%)
2:53(20.7%)
 239 (48.3%)
sitem19 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:110(43.0%)
1:146(57.0%)
 239 (48.3%)
sitem20 [numeric]
Mean (sd) : 1 (0.9)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 1 (0.9)
0:78(30.6%)
1:132(51.8%)
2:24(9.4%)
3:17(6.7%)
4:4(1.6%)
 240 (48.5%)
sitem21 [numeric]
Mean (sd) : 0.8 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 0 (0.7)
0:62(24.6%)
1:175(69.4%)
2:11(4.4%)
3:4(1.6%)
 243 (49.1%)
sitem22 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:76(29.7%)
1:180(70.3%)
 239 (48.3%)
sitem23 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:95(37.3%)
1:160(62.7%)
 240 (48.5%)
sitem24 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:73(28.5%)
1:183(71.5%)
 239 (48.3%)
sitem25 [numeric]
Mean (sd) : 0.7 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.9)
0:105(41.0%)
1:127(49.6%)
2:24(9.4%)
 239 (48.3%)
sitem26 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:104(40.6%)
1:152(59.4%)
 239 (48.3%)
sitem27 [numeric]
Mean (sd) : 1.5 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 1 (0.6)
0:24(9.4%)
1:112(43.8%)
2:90(35.2%)
3:30(11.7%)
 239 (48.3%)
sitem28 [numeric]
Mean (sd) : 1.2 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 2 (0.9)
0:78(30.6%)
1:83(32.5%)
2:61(23.9%)
3:33(12.9%)
 240 (48.5%)
sitem29 [numeric]
Mean (sd) : 1 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1.2 (0.7)
0:64(25.0%)
1:124(48.4%)
2:68(26.6%)
 239 (48.3%)
sitem30 [numeric]
Mean (sd) : 0.6 (0.5)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.9)
0:108(42.2%)
1:147(57.4%)
2:1(0.4%)
 239 (48.3%)
sitem31 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:95(37.1%)
1:161(62.9%)
 239 (48.3%)
sitem32 [numeric]
Min : 0
Mean : 0.7
Max : 1
0:88(34.4%)
1:168(65.6%)
 239 (48.3%)
sitem33 [numeric]
Mean (sd) : 0.8 (0.4)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 0 (0.6)
0:58(22.7%)
1:194(76.1%)
2:3(1.2%)
 240 (48.5%)
sitem34 [numeric]
Min : 0
Mean : 0.5
Max : 1
0:137(53.5%)
1:119(46.5%)
 239 (48.3%)
sitem35 [numeric]
Min : 0
Mean : 0.4
Max : 1
0:141(55.1%)
1:115(44.9%)
 239 (48.3%)
sitem36 [numeric]
Mean (sd) : 0.9 (0.7)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.8)
0:75(29.4%)
1:135(52.9%)
2:45(17.6%)
 240 (48.5%)
sitem37 [numeric]
Mean (sd) : 0.7 (0.8)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 1 (1.1)
0:112(43.8%)
1:109(42.6%)
2:26(10.2%)
3:9(3.5%)
 239 (48.3%)
sitem38 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:107(41.8%)
1:149(58.2%)
 239 (48.3%)
sitem39 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:109(42.6%)
1:147(57.4%)
 239 (48.3%)
sitem40 [numeric]
Min : 0
Mean : 0.6
Max : 1
0:90(35.2%)
1:166(64.8%)
 239 (48.3%)
sitem41 [numeric]
Mean (sd) : 0.7 (0.6)
min ≤ med ≤ max:
0 ≤ 1 ≤ 2
IQR (CV) : 1 (0.9)
0:95(37.1%)
1:133(52.0%)
2:28(10.9%)
 239 (48.3%)
sitem42 [numeric]
Mean (sd) : 1.2 (1)
min ≤ med ≤ max:
0 ≤ 1 ≤ 4
IQR (CV) : 2 (0.8)
0:22(28.6%)
1:27(35.1%)
2:24(31.2%)
3:2(2.6%)
4:2(2.6%)
 418 (84.4%)
sitem43 [numeric]
Min : 0
Mean : 0.1
Max : 1
0:7(87.5%)
1:1(12.5%)
 487 (98.4%)
sitem44 [numeric]
Mean (sd) : 1.3 (1.4)
min ≤ med ≤ max:
0 ≤ 1 ≤ 3
IQR (CV) : 2.5 (1.1)
0:3(42.9%)
1:1(14.3%)
2:1(14.3%)
3:2(28.6%)
 488 (98.6%)
sitem45 [numeric]
Mean (sd) : 0.7 (1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 1.5 (1.3)
0:4(57.1%)
1:1(14.3%)
2:2(28.6%)
 488 (98.6%)
srawsc [numeric]
Mean (sd) : 31.6 (9.4)
min ≤ med ≤ max:
8 ≤ 32.5 ≤ 57
IQR (CV) : 14 (0.3)
 43 distinct values 239 (48.3%)
smcpts [numeric]
Mean (sd) : 14.1 (4.9)
min ≤ med ≤ max:
2 ≤ 14 ≤ 29
IQR (CV) : 6.2 (0.3)
 26 distinct values 239 (48.3%)
sorpts [numeric]
Mean (sd) : 17.6 (6.4)
min ≤ med ≤ max:
0 ≤ 18 ≤ 32
IQR (CV) : 9 (0.4)
 33 distinct values 239 (48.3%)
sperpospts [numeric]
Mean (sd) : 56.9 (17.3)
min ≤ med ≤ max:
13 ≤ 57 ≤ 95
IQR (CV) : 26 (0.3)
 59 distinct values 239 (48.3%)
sscaleds [numeric]
Mean (sd) : 447.9 (105.2)
min ≤ med ≤ max:
214 ≤ 493 ≤ 558
IQR (CV) : 41 (0.2)
 91 distinct values 185 (37.4%)
sperflev [ordered, factor]
1. E
2. M
3. PM
4. NM
5. ABS
6. F
7. PAS
8. NI
9. P
17(5.4%)
102(32.7%)
112(35.9%)
17(5.4%)
2(0.6%)
3(1.0%)
54(17.3%)
3(1.0%)
2(0.6%)
 183 (37.0%)
sperf2 [ordered, factor]
1. E
2. M
3. PM
4. NM
5. F
6. P
7. A
8. NI
14(5.8%)
81(33.6%)
73(30.3%)
10(4.1%)
3(1.2%)
28(11.6%)
8(3.3%)
24(10.0%)
 254 (51.3%)
snumin [numeric] 1 distinct value
1:241(100.0%)
 254 (51.3%)
sassess [numeric]
Min : 0
Mean : 1
Max : 1
0:2(0.8%)
1:241(99.2%)
 252 (50.9%)
sattempt [character]
1. F
2. N
256(82.1%)
56(17.9%)
 183 (37.0%)
ela_cd [numeric]
Min : 0
Mean : 0.9
Max : 2
0:71(53.8%)
2:61(46.2%)
 363 (73.3%)
math_cd [numeric]
Mean (sd) : 0.9 (1)
min ≤ med ≤ max:
0 ≤ 0 ≤ 2
IQR (CV) : 2 (1.1)
0:71(53.8%)
1:6(4.5%)
2:55(41.7%)
 363 (73.3%)
sci_cd [numeric]
Min : 0
Mean : 0.9
Max : 1
0:10(7.6%)
1:122(92.4%)
 363 (73.3%)
accom_e [numeric] 1 distinct value
1:76(100.0%)
 419 (84.6%)
accom_m [numeric] 1 distinct value
1:78(100.0%)
 417 (84.2%)
accom_s [numeric] 1 distinct value
1:47(100.0%)
 448 (90.5%)
accom_readaloud [character]
1. H
2. T
1(33.3%)
2(66.7%)
 492 (99.4%)
accom_scribe [character] 1. H
2(100.0%)
 493 (99.6%)
accom_calculator [numeric] 1 distinct value
1:2(100.0%)
 493 (99.6%)
grade2018 [numeric]
Mean (sd) : 4.3 (1.1)
min ≤ med ≤ max:
3 ≤ 4 ≤ 7
IQR (CV) : 2 (0.3)
3:77(28.4%)
4:80(29.5%)
5:62(22.9%)
6:51(18.8%)
7:1(0.4%)
 224 (45.3%)
grade2019 [numeric]
Mean (sd) : 4.8 (1.3)
min ≤ med ≤ max:
3 ≤ 5 ≤ 8
IQR (CV) : 2 (0.3)
3:74(20.5%)
4:79(21.9%)
5:90(24.9%)
6:65(18.0%)
7:52(14.4%)
8:1(0.3%)
 134 (27.1%)
grade2021 [numeric]
Mean (sd) : 5.9 (1.3)
min ≤ med ≤ max:
4 ≤ 6 ≤ 8
IQR (CV) : 2 (0.2)
4:74(18.5%)
5:87(21.7%)
6:90(22.4%)
7:88(21.9%)
8:62(15.5%)
 94 (19.0%)
escaleds2018 [numeric]
Mean (sd) : 504.3 (18.2)
min ≤ med ≤ max:
442 ≤ 504 ≤ 560
IQR (CV) : 23 (0)
 61 distinct values 229 (46.3%)
escaleds2019 [numeric]
Mean (sd) : 503.4 (18.4)
min ≤ med ≤ max:
443 ≤ 503 ≤ 555
IQR (CV) : 22 (0)
 71 distinct values 138 (27.9%)
escaleds2021 [numeric]
Mean (sd) : 502.8 (21.1)
min ≤ med ≤ max:
441 ≤ 503 ≤ 560
IQR (CV) : 26 (0)
 83 distinct values 96 (19.4%)
mscaleds2018 [numeric]
Mean (sd) : 502.9 (19.2)
min ≤ med ≤ max:
440 ≤ 503.5 ≤ 560
IQR (CV) : 27 (0)
 71 distinct values 229 (46.3%)
mscaleds2019 [numeric]
Mean (sd) : 502.8 (18.2)
min ≤ med ≤ max:
450 ≤ 501 ≤ 559
IQR (CV) : 25 (0)
 77 distinct values 138 (27.9%)
mscaleds2021 [numeric]
Mean (sd) : 495 (19.2)
min ≤ med ≤ max:
440 ≤ 495 ≤ 560
IQR (CV) : 23 (0)
 83 distinct values 95 (19.2%)
esgp2018 [numeric]
Mean (sd) : 48.9 (29.1)
min ≤ med ≤ max:
1 ≤ 48 ≤ 99
IQR (CV) : 53.5 (0.6)
 81 distinct values 316 (63.8%)
esgp2019 [numeric]
Mean (sd) : 43.2 (27.9)
min ≤ med ≤ max:
1 ≤ 39.5 ≤ 99
IQR (CV) : 48.2 (0.6)
 91 distinct values 231 (46.7%)
esgp2021 [numeric]
Mean (sd) : 41.6 (30.7)
min ≤ med ≤ max:
1 ≤ 34.5 ≤ 99
IQR (CV) : 51.5 (0.7)
 88 distinct values 201 (40.6%)
msgp2018 [numeric]
Mean (sd) : 52.9 (26.9)
min ≤ med ≤ max:
1 ≤ 55 ≤ 99
IQR (CV) : 45.5 (0.5)
 85 distinct values 316 (63.8%)
msgp2019 [numeric]
Mean (sd) : 49.6 (27.3)
min ≤ med ≤ max:
1 ≤ 52 ≤ 98
IQR (CV) : 46.2 (0.6)
 92 distinct values 231 (46.7%)
msgp2021 [numeric]
Mean (sd) : 28.7 (24.1)
min ≤ med ≤ max:
1 ≤ 23 ≤ 99
IQR (CV) : 33.5 (0.8)
 82 distinct values 200 (40.4%)
summarize [numeric]
Min : 0
Mean : 0.9
Max : 1
0:69(13.9%)
1:426(86.1%)
 0 (0.0%)
amend [character] 1. M
1(100.0%)
 494 (99.8%)
datachanged [numeric] 1 distinct value
8:1(100.0%)
 494 (99.8%)
eScaleForm [numeric] 1 distinct value
1:426(100.0%)
 69 (13.9%)
mScaleForm [numeric] 1 distinct value
1:426(100.0%)
 69 (13.9%)
sScaleForm [numeric] 1 distinct value
1:188(100.0%)
 307 (62.0%)
eFormType [character] 1. C
426(100.0%)
 69 (13.9%)
mFormType [character] 1. C
426(100.0%)
 69 (13.9%)
sFormType [character]
1. C
2. P
304(97.4%)
8(2.6%)
 183 (37.0%)
days_in_person [numeric]
Mean (sd) : 164.5 (12.3)
min ≤ med ≤ max:
86 ≤ 167 ≤ 179
IQR (CV) : 10 (0.1)
 53 distinct values 0 (0.0%)
member [numeric]
Mean (sd) : 175.6 (8.5)
min ≤ med ≤ max:
101 ≤ 176 ≤ 180
IQR (CV) : 4 (0)
 22 distinct values 0 (0.0%)
ssubject_prior [numeric]
Min : 1
Mean : 2.9
Max : 3
1:3(5.0%)
3:57(95.0%)
 435 (87.9%)
sscaleds_prior [numeric]
Mean (sd) : 240.1 (16.6)
min ≤ med ≤ max:
200 ≤ 240 ≤ 266
IQR (CV) : 26 (0.1)
 24 distinct values 435 (87.9%)
escaleds.legacy.equivalent [numeric]
Mean (sd) : 254.7 (9.6)
min ≤ med ≤ max:
206 ≤ 260 ≤ 268
IQR (CV) : 14 (0)
 14 distinct values 433 (87.5%)
mscaleds.legacy.equivalent [numeric]
Mean (sd) : 251.5 (14.2)
min ≤ med ≤ max:
212 ≤ 256 ≤ 278
IQR (CV) : 18 (0.1)
 24 distinct values 432 (87.3%)
sscaleds.legacy.equivalent [numeric]
Mean (sd) : 240.7 (14.3)
min ≤ med ≤ max:
204 ≤ 240 ≤ 276
IQR (CV) : 18 (0.1)
 26 distinct values 425 (85.9%)
sscaleds.highest.on.legacy.scale [numeric]
Mean (sd) : 240.9 (14.4)
min ≤ med ≤ max:
204 ≤ 240 ≤ 276
IQR (CV) : 20.5 (0.1)
 30 distinct values 363 (73.3%)
scpi [numeric]
Mean (sd) : 82.5 (23.2)
min ≤ med ≤ max:
25 ≤ 100 ≤ 100
IQR (CV) : 25 (0.3)
25:3(4.8%)
50:11(17.5%)
75:13(20.6%)
100:36(57.1%)
 432 (87.3%)
sscaleds.highest.on.nextGen.scale [numeric]
Mean (sd) : 495.4 (19.2)
min ≤ med ≤ max:
461 ≤ 492 ≤ 531
IQR (CV) : 33.5 (0)
 24 distinct values 432 (87.3%)
sperf2.highest.on.nextGen.scale [character]
1. E
2. M
3. NM
4. PM
2(3.2%)
20(31.7%)
3(4.8%)
38(60.3%)
 432 (87.3%)
nature0fdis [character]
1. 11
2. 12
3. 2
4. 3
5. 4
6. 5
7. 7
8. 8
5(4.3%)
2(1.7%)
1(0.9%)
9(7.8%)
1(0.9%)
19(16.5%)
40(34.8%)
38(33.0%)
 380 (76.8%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-12-21

  • gradesims
  • octenr
  • conenr_sch
  • conenr_state
  • access_part
  • ealt, malt, salt
  • massess, eassess, ssubject: I believe this is coding what test was taken
  • enumin, mnumin, snumin
  • ecomplexity, mcomplexity, scomplexity
  • emode, mmode, smode
  • eattempt, mattempt, sattempt
  • mteststat, eteststat, steststat
  • eperpospts, mperpospts, sperpospts
  • wptopdev: I believe this is a writing score and should be recoded to doubles
  • wpcompconv: I believe this is a writing score and should be recoded to doubles

In order to have tidy data, each row should be a unique observation. A unique case therefore should consist of all of the demographic information about the student tested and their scores for each of student performance metrics.

  • The demographic characteristics and the specific performance metric variable define a case.

  • The value for each case is the student’s performance on a given metric.

  • Like survey data, the MCAS student performance data will require a relatively wide data frame, clearly defined and coded variables, and then effective use of group by to provide appropriate summary statistics for a given performance criteria.

Code 
MCAS_2022

From our MCAS_2022 data frame summary, we can see that this data set contains performance results from 495 students from Rising Tide Charter Public School on the Spring 2022 Massachusetts Comprehensive Assessment System (MCAS) tests.

For each student, there are values reported for 256 different variables which consist of information from four broad categories

  • Demographic characteristics of the students themselves (e.g., race, gender, date of birth, town, grade level, years in school, years in Massachusetts, and low income, title1, IEP, 504m and EL status ). These variables were mostly nominal and of character type. Notable exceptions include the ordinal variables: grade, and level of need and the numerical variables years in mass, and years in school,
Code 
# I know this is HARDCODED and bad! I need to rename the demographic variables with
# a consistent prefix and then use "contains"
MCAS_2022_StudentDemo <- select(MCAS_2022, c(1:27))
MCAS_2022_StudentDemo
  • Key assessment features including subject, test format, and accommodations provided
Code 
# add label that says Math Performance Variables
MCAS_2022_AssessType<- select(MCAS_2022, contains('mode')|contains('alt')|
                                contains('attempt')|
                                contains('access')|
                                contains('testtat')|
                               contains('subject'))
#need to add caption
MCAS_2022_AssessType
  • Performance metrics: This includes a students score on individual item strands, e.g.,mitem1-mitem42. All of the the itemized performance metrics were of double data type.

There are also a collection of summary performance metrics, such as a student’s scaledscore, performance level, growth percentile, score on open response items, score on multiple choice items, and predicted scaled score in 2023 with updated performance criteria from DESE. All of these variables were of double type, with the exception of the perflev variables. These were of ordinal type and characterized a students performance as exceeding, meeting, partially meeting, or not meeting expectations.

Code 
# add label that says Math Performance Variables
MCAS_2022_MathPerf <- select(MCAS_2022, contains('mitem')|contains('mper')|contains('msca'))
MCAS_2022_MathPerf

  • We are regional school of choice; are students coming from all regions equitably served in our school?

  • How are we supporting our high needs students by subject area?

  • Using the MCAS item standards map, what areas of focus should our curriculum teams focus on by gradelevel and subject area?

I also have access to performance data on some of our students for STAR literacy and Mathematics test

  • Can I add this data into our MCAS data frame and identify ways to use the STAR screening data to identify appropriate interventions to support students in MCAS achievement?

I also have access to students course placement and grades by subject area.

  • Can I add this data into our data frame and compare teacher measured performance against standardized measured performance and search for trends in groups that demonstrate the greatest disparity in their teacher measured performance vs. standardized measured performance level.

Here I would complete a key for all of the variables that are included in my table. And link relevant decoding documents from DESE

esgp, msgp, ssgp continuous: The student’s growth percentile. by subject area (e: English, m: Math, s: Science)

eperf2, mperf2, ordinal: The student’s performance level in ELA and Math

value Key
Exceeds Expectations E
Meets Expectations M
Partially Meets Expectations PM
Does Not Meet Expectations NM

gender, nominal : the reported gender identify of the student.

value Key
Female F
Male M
Non binary N