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A simple tool for scoring psychological self-report questionnaires.


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Scorify: A simple tool for scoring psychological self-report questionnaires.



Many psychology studies use one or more self-report questionnaires to understand their participants. These responses go into CSV files with one question per column, one participant per row.

Scoring these files is a bunch of work. Oftentimes, many questionnaires (or sub-scales) are included in one CSV file. Often, half of the questions are "reverse-scored" to combat the tendancy people have to agree with questions. Scoring these files usually means spending a whole bunch of time in Excel, and no one likes doing that.

Scorify aims to fix this.

Try scorify on the web!

If you want to build an automatic pipeline to score your data, you'll want the python version of the tool. But if you just want to give it a try in your browser, try our web-based tool!


scorify requires Python 3.5.

pip install scorify

should have you set up.


See examples/ for some test files. To run the neurohack data and scoresheet, do something like:

score_data neurohack_scoresheet.csv neurohack_April+2,+2019_11.05.csv

Getting started

Given an example CSV file, let's say you want to score 5 columns, the answers can be 1 to 5, where the third and fifth are reversed.

ppt happy1 happy2 happy3 happy4 happy5
3001 1 2 1 3 4
3002 4 1 5 1 2
3003 1 3 2 3 1
... ... ... ... ... ...

Create a scoresheet that looks like:

layout header
layout data
transform normal map(1:5,1:5)
transform reverse map(1:5,5:1)
score ppt
score happy1 happy normal
score happy2 happy normal
score happy3 happy reverse
score happy4 happy normal
score happy5 happy reverse
measure happy mean(happy)

Then you call score_data with that scoresheet and datafile, like:

score_data scoresheet.csv datafile.csv

Your output just goes to STDOUT, and you should see it renaming columns. To save the output if it looks good, just pipe it to a file:

score_data scoresheet.csv datafile.csv > output.csv

Other common operations

Excluding participants

If some participant data is particularly messy, you can exclude it using your scoresheet like this:

exclude ppt_id_column_name 3001

Keeping second row headers

If your question headers have a second row with verbose question text in them, you can keep that in the scored data by adding a layout keep instruction:

layout header
layout keep
layout data

Repeat the layout keep instruction if you want to keep more than one row.

Scoresheet reference

The main input to scorify is a comma or tab-delimited "scoresheet" that has many rows and four columns. The first column tells what kind of command the row will be, and will be one of: layout, exclude, transform, score, or measure.


The layout section tells scorify what your input data looks like. It must contain a header and data, but skip and keep are also valid. data tells scorify that the rest of your input file is data. So:

layout header
layout skip
layout data

would tell scorify to expect a header row, skip a line, and then read the rest of the file as data.

layout header
layout keep
layout data

would result in scorify expecting a header row, keeping the next line as-is, and reading the rest of the file as data.


The rename section renames a header column, and looks like:

rename original_name new_name

Columns can only be renamed once, and must use a new, unique name. You must use the column's new name everywhere in the scoresheet.


The format of an exclude line is:

exclude column value

which will, as you might expect, exclude rows where column == value.


Sometimes, you'll want to reverse-score a column or otherwise change its value for scoring. And you'll want to give that some kind of sane name. Transforms let you do this. They look like:

transform name mapper

Right now, you can apply two transformations.


A linear mapping. Example:

transform reverse map(1:5,5:1)

which will map the values 1,2,3,4,5 to 5,4,3,2,1. This will happily map values outside its input domain.


A mapping for discrete values. Useful to map a numbers to human-meaningful values.

transform score_gender discrete_map("1":"f", "2":"m")

Unmapped values will return a blank.

This transform can be useful when combined with join() (below) to combine an array of checkboxes into one column.


Like discrete_map(), though unmapped values will be unchanged. So, if you have:

transform score_gender passthrough_map("1":"f", "2":"m")

a value of "999" will still be "999".


The score section is where you tell scorify which columns you want in your output, what measure (if any) they belong to, and what transform (again, if any) you want to apply. These look like

score column measure_name transform

measure_name and transform are both optional. So, to reverse score (using the reverse we defined up above) a column called happy_1 and add it to the happy measure, use:

score happy_1 happy reverse

You can optionally pass a 5th value, which will define the output column name:

score happy_1 happy reverse ReverseHappy1


The measure section computes aggregate measures of your scored data. These lines look like:

measure final_name aggregator(measure_1, measure_2, ..., measure_n)

We support the following aggregators:


As you might expect, this calculates the mean of the measure or measures listed. Example:

measure happy mean(happy)

If any values in the measures are non-numeric, returns NaN.


Computes the mean of the measure. However, if any of the values in the measures are non-numeric, this fills in the mean of the numeric values. For example, mean_imputed(1, '', 3, 5) is 3.


Computes the sum fo the listed measures. Example:

measure sad sum(sad)

If any values in the measures are non-numeric, returns NaN.


Computes the sum of the measure. However, if any of the values in the measures are non-numeric, this fills in the mean of the numeric values. For example, sum_imputed(1, '', 3, 5) is 12.


The fraction of the data that is non-zero and would have a value imputed for it. imputed_fraction(1, '', 3, 5) is 0.25.


join() is a little trickier. It collects all the non-blank values in the listed measures, and joins them with the | character. Useful if you have a set of values selected by checkbox. For example, if you had three measures that would either be blank or not for things participants might endorse, you could collate them into one column with:

measure liked_pets join(likes_cats, likes_dogs, likes_horses)

If a participant had cats for likes_cats and horses for likes_horses, you'd get:



ratio(a, b) will compute the ratio of two columns; in other words: a / b. Notably, this works on other measures, so you can take the ratio of sums or means. In those cases, the ratio line needs to come after the other measures' lines do.


min(measure_1, measure_2) will output the minimum numeric value in the given measures. Non-numeric values will cause NaN.


max(measure_1, measure_2) will output the maximum numeric value in the given measures. Non-numeric values will cause NaN.

Complete example

If you take a scoresheet that looks like:

layout header
layout data
exclude PPT_COL bad_ppt1
exclude PPT_COL bad_ppt2
transform normal map(1:5,1:5)
transform reverse map(1:5,5:1)
score PPT_COL
score HAPPY_Q1 happy normal
score SAD_Q1 happy normal
score HAPPY_Q2 happy reverse
measure happy_score mean(happy)
measure sad_score mean(sad)
measure happiness_ratio ratio(happy_score, sad_score)

and run it on data that looks like:

ppt1 foo 4 2 2
ppt2 bar 2 5 5

... you'll get output like:

PPT_COL HAPPY_Q1: happy SAD_Q1: sad HAPPY_Q2: happy happy_score sad_score happiness_ratio
ppt1 4 2 3 3.5 2 1.75
ppt2 2 5 1 1.5 5 0.3

Running multiple scoresheets

Scorify now ships with a tool called score_multi that takes a CSV file, and for each row in the file (except headers), runs score_data. The input, scoresheet, and output options are templates formatted with python's format_map() function with the current row of the CSV file as a map. In addition, the output headers may similarly be formatted with format_map().

TODO: More documentation here! For now, run score_multi -h

Reliability tool

The reliability command reads a scoresheet and a datafile and outputs Cronbach's alpha for each measure, Cronbach's alpha for each measure omitting each question for that measure, the Mahalanobis distance for each participant, and the p value for each Mahalanobis distance.

$ reliability examples/test_alpha_scoresheet.csv examples/test_alpha_data.csv

By default, any missing answers are handled by ignoring all of that participant's data (list-wise deletion). Give the --imputation flag to instead fill in any missing response with the average (across participants) response to the question. If you get NaNs for the Mahalanobis distance, it's probably because numpy failed to compute an inverse for the covariance matrix.


Scorify was written by Nate Vack and Dan Fitch dfitch@wisce.du. Scorify is copyright 2023 by the Boards of Regents of the University of Wisconsin System.

Scorify uses several excellent libraries: