Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/ijmeisner/einerlei/llms.txt

Use this file to discover all available pages before exploring further.

The MPADQ admin dashboard makes it easy to export all collected questionnaire data as CSV files. Two export formats are available — raw answers and computed scores — suited to different research and analysis workflows. Both files are generated fresh on each page load and can be downloaded immediately. This page walks through the full export process, explains the data tables behind the exports, and covers archiving and privacy best practices.

​
Export Workflow

1

Log in to the admin dashboard

Navigate to /admin/ and sign in with your admin credentials. You will be redirected to /admin/dashboard/ on successful login.
2

Open Review Answers

Click Review Answers on the dashboard menu, or navigate directly to /admin/dashboard/data-view/. The page lists two available data views.
3

Choose your export format

Select View 1 for a wide-format CSV of raw answers (one row per respondent, one column per question), or View 2 for a long-format CSV (one row per question-answer pair).
4

Download the file

The server generates the CSV immediately when the page loads. Click the “Click here to download the latest data in this view” link to save the file to your computer.
5

Archive the file

Rename or move the downloaded file before your next export. Both View1.csv and View2.csv are overwritten on every page load — older exports are not retained by the server automatically.
Data privacy: Exported CSV files contain individual questionnaire responses. Handle exported data in accordance with your IRB approval, ethics agreement, or institutional data-handling policy. Do not store or share export files in unsecured locations.

​
Available Export Formats

FormatURLBest For
View 1 — Wide Format/admin/dashboard/data-view/view-1/Statistical software expecting one row per participant (SPSS, R, Python pandas)
View 2 — Long Format/admin/dashboard/data-view/view-2/Normalized workflows, pivot tables, tidy-data tools
See Data Views for a detailed description of each format’s column structure and SQL generation logic.

​
Database Tables Behind the Exports

Both views draw from the following MySQL tables in the Quiz database:

DemographicAnswer

Each respondent’s answers to the 15 demographic questions. Columns: license_id, demographic_question_id, answer.

LikertAnswer

Each respondent’s numerical answers to the 157 Likert statements. Columns: license_id, question_id, answer (0, 25, 50, 75, or 100; 6 = N/A).

SubCategoryResult

Computed average scores for each of the 24 sub-categories per respondent. Columns: license_id, sub_category_id, result.

BroadCategoryResult

Computed broad-category scores (Symptomatology, Contributing Factors) per respondent. Columns: license_id, broad_category_id, result.

License

Maps license_id to licenseKey (MD5) and active flag. Only active = false records (completed quizzes) appear in the exports.

DemographicQuestion / LikertQuestion

Question text lookup tables, used to populate CSV column headers and question labels.

​
Key Archive

Historically generated license key batches are stored as dated CSV files in the admin/dashboard/keygen/keys/keyArchive/ directory on the server. These files are named by generation date (for example, keys_04-11-2016.csv) and contain the unhashed plain-text keys from each batch. Unlike the data views, key archive files are not overwritten — each batch creates a new dated file.
Establish a regular export schedule and maintain an off-server archive of dated CSV snapshots. Because View1.csv and View2.csv are overwritten on every generation, you will lose previous exports if you do not save them locally.

​
Using Exported Data

The View 1 CSV can be loaded directly into most statistical software:
R
data <- read.csv("View1.csv", stringsAsFactors = FALSE)
Python
import pandas as pd
data = pd.read_csv("View1.csv")
SPSS
GET DATA /TYPE=TXT /FILE='View1.csv' /DELIMITERS=',' /QUALIFIER='"' /FIRSTCASE=2 /VARIABLES=...
The View 2 long-format CSV is best reshaped before analysis:
Python
import pandas as pd
long = pd.read_csv("View2.csv")
wide = long.pivot(index="User", columns="Question", values="Answer")
Demographic answer values in the CSV may be prefixed with a single quote character (') — a convention from the PHP CSV writer. Strip this character before numeric analysis if present.

​
Seeding Test Data

The file mysql/fake_quiz_data.php can be run on the server to insert synthetic quiz responses into the database. This is useful for verifying that the data view exports work correctly before real respondent data is collected. Remove or restrict access to this file in a production environment.

Build docs developers (and LLMs) love

Get started for freeTalk to us