psython 0.0.9

A package for SPSS methods

Psython

This package include SPSS related calculations done using python.

Installation

pip install psython

Importing

import psython as psy

Cronbach's alpha - with "if deleted"

This package is for calculating Cronbach's alpha of an entire dataset with a "if deleted" table for finding items that should be removed.

The package is using the pingouin package for the actuall calculation of Cronbach's alpha.

Usage

Here is an example of the SAQ DataFrame (q3r = q3 reversed):

	q1	q2	q4	q5	q6	q7	q8	q9	q10	q11	...	q15	q16	q17	q18	q19	q20	q21	q22	q23	q3r
0	2	1	2	2	2	3	1	1	2	1	...	2	3	1	2	3	2	2	2	5	2
1	1	1	3	2	2	2	2	5	2	2	...	4	3	2	2	3	4	4	4	2	2
2	2	3	2	4	1	2	2	2	2	3	...	2	3	2	3	1	4	3	2	2	4
3	3	1	4	3	3	4	2	2	4	2	...	3	3	2	4	2	4	4	4	3	5
4	2	1	2	2	3	3	2	4	2	2	...	2	2	2	3	3	4	2	4	4	3

5 rows × 23 columns

psy.cronbach_alpha_scale_if_deleted(df)

Where df is the items dataframe (each item as a column) and the function will return two objects - the Cronbach's alpha of the entire DataFrame at position 0 and the table of the "if delete" items in position 1.

	Item	Scale Mean if Item Deleted	Scale Variance if Item Deleted	Corrected Item-Total Correlation	Cronbach's Alpha if Item Deleted
0	q1	59.892260	90.121072	0.520928	0.791887
1	q2	60.642940	101.063899	-0.163158	0.819978
2	q4	59.480358	87.967999	0.569241	0.788177
3	q5	59.544146	89.303401	0.480579	0.792419
4	q6	60.039284	87.605071	0.482416	0.791397
5	q7	59.342668	85.655685	0.594245	0.785032
6	q8	60.029560	89.900293	0.503704	0.792141
7	q9	59.420070	100.881838	-0.137191	0.828613
8	q10	59.985609	92.232867	0.355784	0.798693
9	q11	60.011280	88.790145	0.568318	0.789112
10	q12	59.106962	88.451979	0.562942	0.788889
11	q13	59.817192	87.839720	0.576902	0.787798
12	q14	59.390121	87.491716	0.562476	0.787931
13	q15	59.500194	88.766051	0.484296	0.791916
14	q16	59.387009	88.329154	0.570772	0.788520
15	q17	59.799689	88.441961	0.587849	0.788165
16	q18	59.697783	85.993065	0.608925	0.784771
17	q19	59.974329	104.442142	-0.295795	0.832243
18	q20	58.642163	91.699140	0.313782	0.800711
19	q21	59.095683	87.678779	0.561128	0.788157
20	q22	59.378452	101.109248	-0.152704	0.823798
21	q23	58.831972	98.820783	-0.044039	0.818680
22	q3r	58.851809	89.021221	0.434762	0.794258

Reliability: Split-Half

result = psy.split_half_reliability(df)

Will return an array of values:

pearson

spearman_brown

a_croncha_1

a_croncha_2

N1

N2

table

print(result.table)

Will print the output in the familiar SPSS format.