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IRT Tools
⚙ IRT Calibration 📊 Test Scoring ⚖ Equating
CDM Tools
🧠 CDM Calibration 🎯 Skill Profiler
Template Library
File Format Reference

Template Library

Every file format PACER accepts, with example data you can view and download. Each tool expects a specific layout — use these as starting points for your own data. Some formats are flexible: column headers become identifiers within the analysis, so you can name them anything that makes sense for your data. Others are prescribed: PACER looks for specific column names to locate the right data, and the wrong name will trigger a validation error. Each file card is labeled so you know which rule applies before you start formatting your data.

📐 Item Analysis ⚙ IRT Calibration 📊 IRT Scoring ⚖ Equating 🧠 CDM Calibration 🎯 Skill Profiler
📐
Item Analysis
Classical test statistics · Point-biserial · DIF
Example DataResponse matrix · one file required
CSV / XLSX
Response Matrix
Wide format — each column is an item, each row is an examinee
✦ Flexible names

Column headers are item names. Values are numeric scores (0/1 for binary, 0–K for polytomous). An optional group column may be included anywhere for DIF analysis. Leave missing values blank or code as NA.

Q1Q2Q3Q4Q5group
Item columnItem columnItem columnItem columnItem columnOptional — DIF grouping
10110Reference
11101Reference
01011Focal
10111Focal
00100Reference
First row must be a header row — column names become item identifiers
Binary items: 0 or 1. Polytomous items: integer scores starting at 0
For DIF: include a group column with at least two distinct values (e.g. Reference / Focal)
IRT Calibration
1PL · 2PL · 3PL · GRM · GPCM · RSM
Example DataResponse matrix · one file required
CSV / XLSX
Response Matrix
Wide format — examinees as rows, items as columns
✦ Flexible names

Same wide-format layout as Item Analysis. Binary items use 0/1; polytomous items use integer scores 0–K. Binary and polytomous items may be mixed in the same file — PACER auto-detects: any column with a value > 1 is treated as polytomous. Leave missing responses blank or code as NA.

ITEM01ITEM02ITEM03ITEM04POLY01POLY02
Binary itemBinary itemBinary itemBinary itemPolytomous (0–3)Polytomous (0–4)
101123
110112
011034
101101
00012
Column names become item identifiers used throughout the calibration output
You can override the model per item in the UI (e.g. force GRM vs GPCM)
📊
IRT Scoring
MLE · MAP · EAP · TCC

Scoring requires two files: item parameters and response data. If you ran calibration in PACER, both are loaded automatically — you only need to upload manually when working with external parameter estimates.

Example DataFile 1 of 2 · Item parameters
CSV / XLSX
Item Parameters File 1 of 2
One row per item — binary and polytomous items may be mixed
⬡ Names required

Binary items use a, b, c. Polytomous items (GRM / GPCM) use a plus threshold columns d1, d2, d3…. Leave unused cells blank. The name column may also be called itemKey, item, key, itemId, or id — PACER accepts all of these.

columnNamemodelabcd1d2d3
Item name (or itemKey, item, key, itemId, id)1PL · 2PL · 3PL · GRM · GPCMDiscriminationDifficultyGuessingThreshold 1Threshold 2Threshold 3
ITEM012PL0.8794-0.9216
ITEM023PL1.7345-0.31420.1316
ITEM03GRM2.1200-1.0851-0.13001.4424
ITEM04GPCM0.9530-1.98180.1647
COL Required — exact name
COL Optional — include when applicable
Example DataFile 2 of 2 · Response data (long format)
CSV / XLSX
Response Data — Long Format File 2 of 2
One row per examinee–item pair
⬡ Names required
💡 If your data is wide-format, run IRT Calibration first — PACER saves your responses in the required long format automatically, so you never need to convert manually.

Three required columns: testID (examinee identifier — also accepted: personId, id), key (the item's name — must match a value in columnName of the parameter file), score (0/1 for binary; integer for polytomous). Sparse data is supported.

testIDkeyscore
Examinee ID (or personId, id)Matches columnName value in params0/1 binary · 0–K poly
EX001ITEM011
EX001ITEM020
EX001ITEM032
EX002ITEM011
EX002ITEM021
EX002ITEM041
The key value must match a value in columnName of the parameter file — the column name is key here but the values must be identical to those in the params. Also accepted: itemKey, itemId, item
Sparse data is fine — items not administered to a given examinee can be omitted
Equating / Linking
Stocking-Lord · Haebara · Mean-Sigma · Mean-Mean

Equating requires two parameter files — the Item Bank (old scale parameters) and the New Form (new form parameters). Both files use the same column layout. Items shared between forms serve as anchors; their item_id values must match exactly across both files.

Example DataBinary parameters · upload two files (Item Bank + New Form)
CSV / XLSX
Binary Item Parameters 2 files
Same column layout for Item Bank and New Form
⬡ Names required
Binary Items — one row per item
item_idmodelabc
Unique identifier1PL · 2PL · 3PLDiscriminationDifficultyGuessing (blank = 0)
ITEM0012PL0.8794-0.9216
ITEM0022PL0.6494-0.5570
ITEM0033PL1.7345-0.31420.1316
ITEM0043PL1.6109-0.97190.1766
COL Required — exact name
COL Optional — include when applicable
Upload the old-scale parameters as "Item Bank" and the new-form parameters as "New Form"
Anchor items must have identical item_id values in both files — only items appearing in both are used as anchors
🧠
CDM Calibration
DINA · DINO · EM Algorithm

CDM Calibration requires two files: a binary response matrix and a Q-matrix that maps items to skills.

Example DataFile 1 of 2 · Binary response matrix
CSV / XLSX
Response Matrix File 1 of 2
Examinees × Items · binary 0/1 only
✦ Flexible names

Wide format. Header row with item names required. Values must be 0 or 1. Item column names must correspond in order to rows of the Q-matrix.

Q1Q2Q3Q4Q5Q6
Item columnItem columnItem columnItem columnItem columnItem column
101101
111010
010111
101100
001011
Example DataFile 2 of 2 · Q-matrix
CSV / XLSX
Q-Matrix File 2 of 2
Items × Skills · specifies which skills each item requires
✦ Flexible names

One row per item, in the same order as the response matrix columns. One column per skill. A value of 1 means the item requires that skill; 0 means it does not. Column headers become skill names throughout the output.

Skill1Skill2Skill3
Skill (0 or 1)Skill (0 or 1)Skill (0 or 1)
100
010
110
001
101
011
Number of rows must equal the number of item columns in the response matrix
DINA: examinee must master ALL required skills for high P(correct). DINO: examinee needs ANY one required skill
🎯
Skill Profiler
CDM Scoring · MLE · MAP · EAP skill profiles

The Skill Profiler loads parameters from a saved CDM Calibration session automatically. If uploading manually, three files are needed: item parameters, a Q-matrix, and a response matrix.

Example DataFile 1 of 3 · CDM item parameters
CSV / XLSX
CDM Item Parameters File 1 of 3
Slip and guess parameters — output from CDM Calibration
⬡ Names required

Three columns: itemName (item identifier), guess (probability of correct response without required skills), slip (probability of incorrect response despite having all required skills).

itemNameguessslip
Item identifierP(correct | no skills)P(incorrect | all skills)
Q10.18240.0951
Q20.21030.1240
Q30.08830.1571
Q40.15670.0724
Q50.22910.1138
Q60.16400.0873
CSV / XLSX
Q-Matrix & Response Matrix Files 2 & 3 of 3
Identical format to CDM Calibration
↑ See formats above

The Q-matrix and response matrix use the exact same format as CDM Calibration above — same column layout, same ordering requirements.