conjoint-cleaning

Name: conjoint-cleaning
Author: scdenney

34stars0forks

How to install conjoint-cleaning?

scdenney/open-science-skills/conjoint-cleaning

$npx -y skills add scdenney/open-science-skills --skill conjoint-cleaning

Installs into the current project.

Summary

Clean and reshape Qualtrics conjoint exports to analysis-ready long format.

Files · 1

View on GitHub

SKILL.md

1	# Conjoint Data Cleaning Expert
2
3	## Instructions
4
5	### 1. Qualtrics Export Settings and Metadata
6
7	Export format: The right Qualtrics export choice depends on the pipeline. `cjoint::read.qualtrics()` (Method A) hard-errors when the choice-response columns contain text — export "Use numeric values" for it. `projoint::reshape_projoint()` parses the chosen profile from a label at the end of the outcome string (default `A`/`B`), and manual reshaping benefits from readable attribute labels — "Use choice text" works for those. When unsure, export both versions; they differ only in response coding. If working with non-Latin scripts (Chinese, Korean, Arabic), export as XLSX rather than CSV to avoid UTF-8/ANSI encoding issues. On Windows with East Asian locales, `read.csv()` may still require `Sys.setlocale()` to match the file encoding before import (see `?cjoint::read.qualtrics` East Asian Language Support).
8
9	Metadata rows: Current Qualtrics CSV exports include 3 header rows before respondent data: (1) variable identifiers, (2) question text/descriptions, (3) ImportId JSON. Legacy exports have 2 rows. The `cjoint::read.qualtrics()` parameter `new.format = TRUE` (set explicitly; default is `FALSE`) handles the 3-row format. For manual import via `readxl::read_excel()` or `readr::read_csv()`, skip the appropriate number of metadata rows after reading headers.
10
11	Randomization order columns: If "Export viewing order data" is enabled, Qualtrics adds `_DO_` columns (e.g., `Block1_DO`) containing pipe-separated integers showing element display order. These are useful for task-order robustness checks but are not needed for the core reshape.
12
13	### 2. Qualtrics Conjoint Implementation Methods
14
15	Qualtrics conjoint experiments use one of three implementation methods, each producing different column naming conventions:
16
17	Method A — Conjoint Survey Design Tool (Strezhnev): Generates JavaScript that Qualtrics executes to randomize profiles. Column naming follows `F-{task}-{profile}-{attribute}` for attribute levels and `F-{task}-{attribute}` for attribute names. The `cjoint` R package's `read.qualtrics()` function is purpose-built for this format.
18
19	Method B — Custom JavaScript + Embedded Data: Researchers write JavaScript to randomize attributes and store values in Qualtrics embedded data fields. Column naming is researcher-defined. Two common conventions: (i) `C{x}-F-{task}-{idx}` for attribute names and `C{x}-F-{task}-{profile}-{idx}` for profile values; (ii) the Graham (2020) convention, `choice{task}_{attr}{profile}` with fixed attribute order (e.g., `choice1_bread1`) or `c{task}_attrib{pos}_name` / `c{task}_attrib{pos}_sand{profile}` when attribute order is also randomized. Requires manual reshaping (Section 4).
20
21	Method C — Loop & Merge: Each loop iteration represents one conjoint task. Embedded data fields are referenced via `${e://Field/variable_name}` and displayed with `${lm://Field/N}`. Column names reflect the embedded data field structure. Requires manual reshaping.
22
23	Before writing any cleaning code: Inspect the actual column headers, the QSF survey definition file, or any JavaScript in the survey to determine which method was used. Do not assume a column naming convention.
24
25	### 3. Existing R Packages for Conjoint Data Import
26
27	Before writing custom reshaping code, check whether an existing package handles the data format:
28
29	`cjoint::read.qualtrics()` — Purpose-built for Conjoint SDT exports (Method A). Reads Qualtrics CSV directly, handles metadata rows, outputs one row per profile with a `selected` column. Parameters: `responses` (choice column names), `covariates` (respondent-level variables), `respondentID`, `new.format` (TRUE for 3-row headers), `ranks` (for rank/rating/top-L designs). Supports binary forced choice, profile ranks, per-profile ratings, and top-L choices; see `?cjoint::read.qualtrics` Details for the four response types. Requires PHP/JS output from the Conjoint Survey Design Tool.
30
31	`cjdata::reshape_conjoint()` — Lightweight alternative. Functions: `read_Qualtrics()` + `reshape_conjoint()`. Handles basic wide-to-long conversion. Requires the terminal character of each outcome string to be {"1","2"} or {"A","B"} (so "Candidate A" works; Japanese zenkaku digits supported). Respondent covariates merged separately.
32
33	`projoint::reshape_projoint()` — For measurement-error-corrected analysis per Clayton, Horiuchi, Kaufman, King, and Komisarchik (2023). Built-in support for repeated tasks (IRR estimation), missing-agreement imputation (`.fill = TRUE`), and bias-corrected AMCEs. Outcome column names must contain task-ID digits, and the repeated-task outcome must be the last element of `.outcomes`. Expects wide columns named `K-{task}-{attribute}` and `K-{task}-{profile}-{attribute}` by default (`.alphabet = "K"`); selected profile is pa

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conjoint-cleaning

34stars0forks

View on GitHub

How to install conjoint-cleaning?

scdenney/open-science-skills/conjoint-cleaning

$npx -y skills add scdenney/open-science-skills --skill conjoint-cleaning

Installs into the current project.

Summary

Clean and reshape Qualtrics conjoint exports to analysis-ready long format.

Files · 1

View on GitHub

SKILL.md

1	# Conjoint Data Cleaning Expert
2
3	## Instructions
4
5	### 1. Qualtrics Export Settings and Metadata
6
7	Export format: The right Qualtrics export choice depends on the pipeline. `cjoint::read.qualtrics()` (Method A) hard-errors when the choice-response columns contain text — export "Use numeric values" for it. `projoint::reshape_projoint()` parses the chosen profile from a label at the end of the outcome string (default `A`/`B`), and manual reshaping benefits from readable attribute labels — "Use choice text" works for those. When unsure, export both versions; they differ only in response coding. If working with non-Latin scripts (Chinese, Korean, Arabic), export as XLSX rather than CSV to avoid UTF-8/ANSI encoding issues. On Windows with East Asian locales, `read.csv()` may still require `Sys.setlocale()` to match the file encoding before import (see `?cjoint::read.qualtrics` East Asian Language Support).
8
9	Metadata rows: Current Qualtrics CSV exports include 3 header rows before respondent data: (1) variable identifiers, (2) question text/descriptions, (3) ImportId JSON. Legacy exports have 2 rows. The `cjoint::read.qualtrics()` parameter `new.format = TRUE` (set explicitly; default is `FALSE`) handles the 3-row format. For manual import via `readxl::read_excel()` or `readr::read_csv()`, skip the appropriate number of metadata rows after reading headers.
10
11	Randomization order columns: If "Export viewing order data" is enabled, Qualtrics adds `_DO_` columns (e.g., `Block1_DO`) containing pipe-separated integers showing element display order. These are useful for task-order robustness checks but are not needed for the core reshape.
12
13	### 2. Qualtrics Conjoint Implementation Methods
14
15	Qualtrics conjoint experiments use one of three implementation methods, each producing different column naming conventions:
16
17	Method A — Conjoint Survey Design Tool (Strezhnev): Generates JavaScript that Qualtrics executes to randomize profiles. Column naming follows `F-{task}-{profile}-{attribute}` for attribute levels and `F-{task}-{attribute}` for attribute names. The `cjoint` R package's `read.qualtrics()` function is purpose-built for this format.
18
19	Method B — Custom JavaScript + Embedded Data: Researchers write JavaScript to randomize attributes and store values in Qualtrics embedded data fields. Column naming is researcher-defined. Two common conventions: (i) `C{x}-F-{task}-{idx}` for attribute names and `C{x}-F-{task}-{profile}-{idx}` for profile values; (ii) the Graham (2020) convention, `choice{task}_{attr}{profile}` with fixed attribute order (e.g., `choice1_bread1`) or `c{task}_attrib{pos}_name` / `c{task}_attrib{pos}_sand{profile}` when attribute order is also randomized. Requires manual reshaping (Section 4).
20
21	Method C — Loop & Merge: Each loop iteration represents one conjoint task. Embedded data fields are referenced via `${e://Field/variable_name}` and displayed with `${lm://Field/N}`. Column names reflect the embedded data field structure. Requires manual reshaping.
22
23	Before writing any cleaning code: Inspect the actual column headers, the QSF survey definition file, or any JavaScript in the survey to determine which method was used. Do not assume a column naming convention.
24
25	### 3. Existing R Packages for Conjoint Data Import
26
27	Before writing custom reshaping code, check whether an existing package handles the data format:
28
29	`cjoint::read.qualtrics()` — Purpose-built for Conjoint SDT exports (Method A). Reads Qualtrics CSV directly, handles metadata rows, outputs one row per profile with a `selected` column. Parameters: `responses` (choice column names), `covariates` (respondent-level variables), `respondentID`, `new.format` (TRUE for 3-row headers), `ranks` (for rank/rating/top-L designs). Supports binary forced choice, profile ranks, per-profile ratings, and top-L choices; see `?cjoint::read.qualtrics` Details for the four response types. Requires PHP/JS output from the Conjoint Survey Design Tool.
30
31	`cjdata::reshape_conjoint()` — Lightweight alternative. Functions: `read_Qualtrics()` + `reshape_conjoint()`. Handles basic wide-to-long conversion. Requires the terminal character of each outcome string to be {"1","2"} or {"A","B"} (so "Candidate A" works; Japanese zenkaku digits supported). Respondent covariates merged separately.
32
33	`projoint::reshape_projoint()` — For measurement-error-corrected analysis per Clayton, Horiuchi, Kaufman, King, and Komisarchik (2023). Built-in support for repeated tasks (IRR estimation), missing-agreement imputation (`.fill = TRUE`), and bias-corrected AMCEs. Outcome column names must contain task-ID digits, and the repeated-task outcome must be the last element of `.outcomes`. Expects wide columns named `K-{task}-{attribute}` and `K-{task}-{profile}-{attribute}` by default (`.alphabet = "K"`); selected profile is pa

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