Yet Another D&D Alignment Test

Web App: YADDAT
Questions/Items: 10
Unidimensional Constructs: Ethics and Morality
Ethics Scale Cronbach's alpha: 77.2%
Morality Scale Cronbach's alpha: 78.4%
Cumulative Variance: 42.12%

To take the test, click on the link above. What follows is a walkthrough of YADDAT's development.

Introduction

As my starting point, I searched through the AB5C subset of the IPIP to find potential questions for YADDAT. I took all items that felt appropriate to measure ethic and moral scales, as defined by my interpretation of D&D alignment. Item response data from the Eugene-Springfield Community Sample was then downloaded for data analysis.

Now that's established, I'll load the data and clean it. Once properly cleaned and formatted, I will test the validity and internal consistency of YADDAT's ethic and moral scales via Cronbach's alpha and Factor Analysis.

Cleaning the Data

First, all of the required packages need to be imported

#Required Imports
import numpy as np
import pandas as pd
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
import matplotlib.pyplot as plt
import tabulate
from IPython.display import display, HTML, Markdown, Latex

Next, the question and response files are loaded

#Load the raw data

#1:The Questions

#Read in the excel file
xls = pd.ExcelFile("ScalesFor967items.xls")

#Parse the sheet of interest into a dataframe
df_q = xls.parse(0) 


#2:The Answers

# Read the tab delimited file into a dataframe
filename = 'IPIP2539-1.tab'
df_a = pd.read_csv(filename, sep='\t', engine='python')

#Preview Data.
display(df_q, df_a)

	Item Num	Item	N_Scales	Big5	B5sgn	Seven Factor	SEVsgn	AB5C	AB5sgn	NEO Domain	...	The 16PF	PFsgn	JPIR	JPIsgn	Six FPQ	FPQsgn	MPQ	MPQsgn	Emot_Int	EMOTsgn
0	H133	Accept apologies easily.	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	H515	Accept challenging tasks.	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	H139	Accept others' weaknesses.	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	H106	Accept people as they are.	6	NaN	NaN	NaN	NaN	IV+II+	NaN	A	...	NaN	NaN	NaN	NaN	Agreeableness	NaN	NaN	NaN	NaN	NaN
4	H554	Accomplish a lot of work.	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	Energy Level	NaN	Achievement	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
962	E71	Would never make a high risk investment.	3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	Risk Taking	-	NaN	NaN	HA	NaN	NaN	NaN
963	P367	Would never take things that aren't mine.	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	Responsibility	NaN	NaN	NaN	AG	-	NaN	NaN
964	P485	Would not be a good comedian.	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	Exhibition	-	NaN	NaN	NaN	NaN
965	H142	Wouldn't harm a fly.	1	NaN	NaN	NaN	NaN	II+V-	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
966	H809	Yell at people.	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

967 rows × 31 columns

	ID	A1	A10	A100	A101	A102	A103	A104	A105	A106	...	X89	X90	X91	X92	X93	X95	X96	X97	X98	X99
0	1001	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	2.0	3.0	1.0	4.0	2.0	2.0	4.0	2.0	5.0	4.0
1	1002	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	1.0	4.0	1.0	3.0	1.0	2.0	4.0	4.0	5.0	3.0
2	1003	3.0	2.0	4.0	3.0	3.0	1.0	1.0	1.0	2.0	...	5.0	3.0	2.0	3.0	4.0	3.0	3.0	2.0	4.0	2.0
3	1004	5.0	2.0	4.0	4.0	2.0	1.0	1.0	2.0	1.0	...	4.0	4.0	2.0	3.0	1.0	2.0	4.0	2.0	5.0	4.0
4	1005	5.0	4.0	4.0	4.0	2.0	1.0	1.0	1.0	2.0	...	3.0	3.0	1.0	4.0	1.0	2.0	4.0	2.0	5.0	5.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1137	2462	3.0	4.0	2.0	3.0	2.0	1.0	1.0	1.0	2.0	...	3.0	3.0	3.0	4.0	4.0	2.0	5.0	1.0	3.0	1.0
1138	2463	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1139	2464	5.0	2.0	3.0	4.0	4.0	3.0	1.0	1.0	5.0	...	2.0	2.0	1.0	4.0	2.0	3.0	4.0	3.0	3.0	4.0
1140	2465	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1141	2466	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

1142 rows × 2540 columns

Create lists of question item numbers that represent the constructs of ethics and morality. Also, define the items that are negatively keyed.

#Define YADDAT 0.01 through ethic (Law-Chaos) and moral (Good-Evil) constructs

#Create lists for the chosen ethics and morality items. Also, create a combined list.
ethicItems_0 = ['X136','X150','H296','H575','H1346','H498','H579','E122','X118','H309','H215','H941','H250','H1327','H400','E7','E146','X274','X146','H925','X77','X204','H1140','H294','H170','E52','X263','E35','H88','H1351','X173','H870','E46','H1350','H303','X122','H926']
moralItems_0 = ['H794','H1328', 'H1100', 'X253', 'H714', 'H29','H1130','X259','H183','H198','E115','X70','H126','X203','X244','X210','H33','X113','H159','H105','H792','E166','H22','X177','H153','H435','E121','H98','H109', 'H721', 'X227', 'X185']
YADDATItems_0 = ethicItems_0+moralItems_0

#Define which items are negative
negativeEthicItems = ['H575','H1346','H498','H579','E122','H941','H1327','H400','E7','E146','H925','X204','H1140','E35','H88','H870','E46','X122','H926']
negativeMoralItems = ['H794','H1328','H714','X70','X203','X244','X210','X113','H792','E166','H435','E121','H98','H721','X227','X185']
negativeItems = negativeEthicItems+negativeMoralItems

Now, we'll make the initial YADDAT item pool from appropriate questions taken from the AB5C subset of the question data.

#Create a list of columns for the AB5C data
AB5CList = ['Item Num', 'Item', 'AB5C']

#Create the AB5C dataframe, dropping rows with missing data
AB5C = df_q[AB5CList].dropna(axis=0, how='any')

#Create YADDAT 0.01 by filtering the items using the YADDATItems_0 list
YADDAT_0 = AB5C[AB5C['Item Num'].isin(YADDATItems_0)]

#Set the 'Item Num' column as the dataframe index
YADDAT_0 = YADDAT_0.set_index('Item Num')

#Display YADDAT 0.01
display(YADDAT_0)

	Item	AB5C
Item Num
H794	Act at the expense of others.	II+III+
H1100	Am concerned about others.	II+III-
X253	Am deeply moved by others' misfortunes.	II+III-
H941	Am guided by my moods.	IV+III+
X203	Am indifferent to the feelings of others.	II+II+
...	...	...
X150	Try to follow the rules.	II+III+
H170	Wait for my turn.	I+II-
H303	Want things to proceed according to plan.	III+IV-
H198	Will do anything for others.	II+V-
X136	Would never cheat on my taxes.	II+III+

69 rows × 2 columns

Following that, a dataframe is created for item responses of YADDAT.

#Create a combined dataframe for ethical and moral constructs
combinedDF_0 = df_a[YADDATItems_0]

#Delete rows that have at least 1 missing value
combinedDF_1 = combinedDF_0.dropna(axis=0, how='any')

#Convert float to int
combinedDF_1 = combinedDF_1[YADDATItems_0].astype(int)

#Preview the dataframes
display(combinedDF_1)

	X136	X150	H296	H575	H1346	H498	H579	E122	X118	H309	...	H22	X177	H153	H435	E121	H98	H109	H721	X227	X185
2	3	3	2	2	4	3	3	3	4	2	...	3	1	4	1	2	4	4	1	1	2
3	4	4	4	1	1	3	1	2	4	4	...	4	4	5	2	2	1	5	1	1	2
4	5	5	5	1	1	4	1	1	5	4	...	5	5	4	4	1	1	5	2	1	2
6	4	4	4	3	4	3	1	4	4	4	...	4	4	5	4	3	4	5	3	4	4
7	4	4	4	2	2	3	2	2	4	3	...	4	4	5	2	2	2	4	4	2	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1124	2	4	3	2	2	2	1	3	2	3	...	5	5	4	4	1	3	5	2	1	2
1125	5	5	5	1	1	1	1	4	5	5	...	4	5	5	4	1	4	5	1	1	4
1129	2	3	2	2	2	5	3	2	2	2	...	3	5	5	1	1	1	4	3	1	3
1137	4	4	4	1	2	2	1	4	4	4	...	4	3	4	2	1	2	5	4	2	3
1139	4	3	5	2	2	3	1	4	4	4	...	4	4	4	1	2	1	5	2	2	4

570 rows × 69 columns

Since the data consists of raw item responses, scoring must commence. Like the AB5C, both of YADDAT's scales have positively and negatively framed questions. Positive item responses remain unchanged, but responses associated with negative items will need to be reversed (i.e. 1 → 5, 2 → 4, 3 → 3, 4 → 2, and 5 → 1)

#Define a key to negative items
revrs = {1: 5, 2: 4, 3: 3, 4: 2, 5: 1}

#Reverse score negative columns using the key
combinedDF_2 = combinedDF_1
combinedDF_2[negativeItems] = combinedDF_2[negativeItems].replace(revrs) 


#Preview the new moral dataframe
display(combinedDF_2)

	X136	X150	H296	H575	H1346	H498	H579	E122	X118	H309	...	H22	X177	H153	H435	E121	H98	H109	H721	X227	X185
2	3	3	2	4	2	3	3	3	4	2	...	3	1	4	5	4	2	4	5	5	4
3	4	4	4	5	5	3	5	4	4	4	...	4	4	5	4	4	5	5	5	5	4
4	5	5	5	5	5	2	5	5	5	4	...	5	5	4	2	5	5	5	4	5	4
6	4	4	4	3	2	3	5	2	4	4	...	4	4	5	2	3	2	5	3	2	2
7	4	4	4	4	4	3	4	4	4	3	...	4	4	5	4	4	4	4	2	4	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1124	2	4	3	4	4	4	5	3	2	3	...	5	5	4	2	5	3	5	4	5	4
1125	5	5	5	5	5	5	5	2	5	5	...	4	5	5	2	5	2	5	5	5	2
1129	2	3	2	4	4	1	3	4	2	2	...	3	5	5	5	5	5	4	3	5	3
1137	4	4	4	5	4	4	5	2	4	4	...	4	3	4	4	5	4	5	2	4	3
1139	4	3	5	4	4	3	5	2	4	4	...	4	4	4	5	4	5	5	4	4	2

570 rows × 69 columns

Now the constructs will be purified with an initial factor analysis, assuming only two factors exist.

# Create a factor analysis object and perform factor analysis to divide items into 2 factors (i.e. the 2 constructs)
combinedfa = FactorAnalyzer(rotation="varimax", n_factors=2)

# Get the factor loadings
combinedfa.fit(combinedDF_2)

# New headings
H = ["Item Num","factor1", "factor2", "factor3", "factor4", "factor5", "factor6", "factor7", "factor8", "factor9", "factor10","factor11", "factor12", "factor13", "factor14", "factor15", "factor16", "factor17"]
display(HTML(tabulate.tabulate(combinedfa.loadings_, headers=H,showindex=YADDATItems_0, tablefmt='html')))

Item Num	factor1	factor2
X136	0.395672	0.204668
X150	0.603549	0.107178
H296	0.584861	0.174011
H575	0.488488	0.161693
H1346	0.621517	0.207049
H498	0.544435	0.190837
H579	0.475155	0.258822
E122	0.413292	-0.102627
X118	0.442331	-0.0952163
H309	0.434376	-0.129043
H215	0.545807	0.102813
H941	0.298774	0.0804836
H250	0.563901	-0.126527
H1327	0.430348	0.218139
H400	0.0853222	0.129469
E7	0.292359	-0.0306845
E146	0.212229	-0.190461
X274	0.324381	0.0520667
X146	0.270303	0.134647
H925	0.385936	0.199028
X77	0.249887	-0.265739
X204	0.343854	-0.129443
H1140	0.409736	0.108018
H294	0.562237	0.116925
H170	0.240549	0.189156
E52	0.28352	-0.210298
X263	0.420976	-0.0323712
E35	0.400984	0.138706
H88	0.461907	0.164924
H1351	0.384341	-0.0584686
X173	0.147667	-0.363188
H870	0.442654	-0.00566036
E46	0.451923	0.0356888
H1350	0.305014	-0.0499739
H303	0.344134	-0.178281
X122	0.459761	-0.00292155
H926	0.310746	0.142814
H794	0.350682	0.324515
H1328	0.21744	0.134178
H1100	0.10565	0.583757
X253	0.022114	0.522004
H714	0.280332	0.294009
H29	0.0438235	0.280241
H1130	0.0701168	0.599271
X259	-0.111705	0.485313
H183	0.127353	0.468109
H198	0.115477	0.278378
E115	-0.0545162	0.471778
X70	-0.1333	0.3148
H126	0.272085	0.364245
X203	0.0427275	0.575583
X244	0.0470974	0.640086
X210	-0.00802314	0.421597
H33	0.0666102	0.20144
X113	0.0461261	0.491368
H159	0.100371	0.482613
H105	0.268681	0.467005
H792	0.190279	0.457251
E166	0.00340179	0.481038
H22	0.0804482	0.420936
X177	0.0658016	0.457669
H153	0.240701	0.358694
H435	-0.010003	0.423121
E121	0.0071875	0.473733
H98	0.155329	0.150402
H109	0.0847288	0.496804
H721	0.175745	0.301933
X227	-0.0567792	0.642986
X185	-0.218897	0.296432

Using the factor loadings, we'll filter out questions that have an absolute correlation at or below 55%.

# Create a dataframe from the factor loadings
dataframe=pd.DataFrame(combinedfa.loadings_,index=YADDATItems_0) 

# Only include items that have a factor coorelation greater than 50%.
highcorr = dataframe[abs(dataframe) >.55]

#Create a list to hold the high correlation items
highcorrItems = []

# Iterate over the index range from o to max number of factors in dataframe, in this case 2
for index in range(highcorr.shape[1]):
    print('Factor Number : ', index+1)
   # Select column by index position using iloc[]
    columnSeriesObj = highcorr.iloc[: , index].dropna()
   #Append to highcorrItems list
    highcorrItems.append(list(columnSeriesObj.index))
   #Display the factor
    display(YADDAT_0.loc[list(columnSeriesObj.index),:])
    print()
#Display item list
display(highcorrItems)

Factor Number :  1

	Item	AB5C
Item Num
X150	Try to follow the rules.	II+III+
H296	Respect authority.	II+III+
H1346	Break rules.	II+III+
H250	Do things by the book.	III+V-
H294	Stick to the rules.	III+II+

Factor Number :  2

	Item	AB5C
Item Num
H1100	Am concerned about others.	II+III-
H1130	Sympathize with others' feelings.	II+II+
X203	Am indifferent to the feelings of others.	II+II+
X244	Feel little concern for others.	II+II+
X227	Am not interested in other people's problems.	II+II+

[['X150', 'H296', 'H1346', 'H250', 'H294'],
 ['H1100', 'H1130', 'X203', 'X244', 'X227']]

The purified dataframes can now be made.

#Seperate item lists
ethicItems_1 = highcorrItems[0]
moralItems_1 = highcorrItems[1]
YADDATItems_1 = ethicItems_1+moralItems_1

#Create new dataframes
ethicDF_3 = combinedDF_2[ethicItems_1]
moralDF_3 = combinedDF_2[moralItems_1]
combinedDF_3 = combinedDF_2[YADDATItems_1]

#Display the combined dataframe
display(combinedDF_3)

	X150	H296	H1346	H250	H294	H1100	H1130	X203	X244	X227
2	3	2	2	2	2	5	5	5	5	5
3	4	4	5	4	4	5	4	5	5	5
4	5	5	5	4	5	5	5	5	5	5
6	4	4	2	4	4	4	4	4	4	2
7	4	4	4	2	4	5	4	4	4	4
...	...	...	...	...	...	...	...	...	...	...
1124	4	3	4	2	4	5	4	5	5	5
1125	5	5	5	3	4	5	5	5	5	5
1129	3	2	4	1	3	5	5	5	5	5
1137	4	4	4	4	4	5	4	4	4	4
1139	3	5	4	3	2	5	5	3	4	4

570 rows × 10 columns

Adequacy Tests

Adequacy tests must now verify that the purified dataframes are suitable for the official factor analysis.

Bartlett’s Test

Bartlett’s test of sphericity checks whether or not the observed variables intercorrelate using the observed correlation matrix against the identity matrix. If the test is statistically insignificant, greater than 0.05, you should not employ a factor analysis.

#Bartlett’s Test

ethic_chi_square_value,ethic_p_value=calculate_bartlett_sphericity(ethicDF_3)
moral_chi_square_value,moral_p_value=calculate_bartlett_sphericity(moralDF_3)
combined_chi_square_value,combined_p_value=calculate_bartlett_sphericity(combinedDF_3)

H = ["Data","Chi Square", "p-value"]

table = [["Ethic",ethic_chi_square_value,ethic_p_value],
         ["Moral",moral_chi_square_value,moral_p_value],
         ["Combined",combined_chi_square_value,combined_p_value]]

display(HTML(tabulate.tabulate(table,headers=H, tablefmt='html')))

Data	Chi Square	p-value
Ethic	649.064	2.6386e-133
Moral	764.173	5.11968e-158
Combined	1460.73	4.93788e-277

Kaiser-Meyer-Olkin Test

Kaiser-Meyer-Olkin (KMO) Test measures the suitability of data for factor analysis. It determines the adequacy for each observed variable and for the complete model. KMO estimates the proportion of variance among all the observed variable. KMO values range between 0 and 1.

0.00 to 0.49 unacceptable.
0.50 to 0.59 miserable.
0.60 to 0.69 mediocre.
0.70 to 0.79 middling.
0.80 to 0.89 meritorious.
0.90 to 1.00 marvelous.

Ref:
Stephanie Glen. "Kaiser-Meyer-Olkin (KMO) Test for Sampling Adequacy" From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/kaiser-meyer-olkin/

ethic_kmo_all,ethic_kmo_model=calculate_kmo(ethicDF_3)
moral_kmo_all,moral_kmo_model=calculate_kmo(moralDF_3)
combined_kmo_all,combined_kmo_model=calculate_kmo(combinedDF_3)

H = ["Data","KMO Model"]
table = [["Ethic",ethic_kmo_model],
         ["Moral",moral_kmo_model],
         ["Combined",combined_kmo_model]]

display(HTML(tabulate.tabulate(table,headers = H, tablefmt='html')))

Data	KMO Model
Ethic	0.819263
Moral	0.791343
Combined	0.812281

Factor Analysis

Choosing the Number of Factors

The number of factors can be determined numerically and visually.

# Create factor analysis object and perform factor analysis
ethicfa = FactorAnalyzer()
moralfa = FactorAnalyzer()
combinedfa = FactorAnalyzer()

ethicfa.fit(ethicDF_3)
moralfa.fit(moralDF_3)
combinedfa.fit(combinedDF_3)

# Check Eigenvalues
ethicValues, ethicVectors = ethicfa.get_eigenvalues()
ethicFactors = list(ethicValues[ethicValues>1])

moralValues, moralVectors = moralfa.get_eigenvalues()
moralFactors = list(moralValues[moralValues>1])

combinedValues, combinedVectors = combinedfa.get_eigenvalues()
combinedFactors = list(combinedValues[combinedValues>1])

table = [["Ethic Factors",ethicFactors], ["Moral Factors",moralFactors], ["Combined Factors",combinedFactors]]

display(HTML(tabulate.tabulate(table,tablefmt='html')))

Ethic Factors	[2.6183738117886386]
Moral Factors	[2.690500313222284]
Combined Factors	[3.1041044849418036, 2.244980092174235]

# Create Ethics scree plot using matplotlib
plt.scatter(range(1,ethicDF_3.shape[1]+1),ethicValues)
plt.plot(range(1,ethicDF_3.shape[1]+1),ethicValues)
plt.title('Ethic Scree Plot')
plt.xlabel('Factors')
plt.ylabel('Eigenvalues')
plt.grid()
plt.show()

# Create Moral scree plot using matplotlib
plt.scatter(range(1,moralDF_3.shape[1]+1),moralValues)
plt.plot(range(1,moralDF_3.shape[1]+1),moralValues)
plt.title('Moral Scree Plot')
plt.xlabel('Factors')
plt.ylabel('Eigenvalues')
plt.grid()
plt.show()

# Create Combined scree plot using matplotlib
plt.scatter(range(1,combinedDF_3.shape[1]+1),combinedValues)
plt.plot(range(1,combinedDF_3.shape[1]+1),combinedValues)
plt.title('Combined Scree Plot')
plt.xlabel('Factors')
plt.ylabel('Eigenvalues')
plt.grid()
plt.show()

Performing Factor Analysis

# Create factor analysis object and perform factor analysis, factors are orthogonal (varimax).

combinedfa_3 = FactorAnalyzer(rotation="varimax", n_factors=2)


combinedfa_3.fit(combinedDF_3)

H = ["factor1", "factor2", "factor3", "factor4", "factor5", "factor6", "factor7", "factor8", "factor9", "factor10","factor11", "factor12", "factor13", "factor14", "factor15", "factor16", "factor17"]

display(HTML(tabulate.tabulate(combinedfa_3.loadings_, headers=H, showindex = YADDATItems_1, tablefmt='html')))
print()

factor1	factor2	factor3
X150	0.07445	0.637913
H296	0.0960184	0.650397
H1346	0.146497	0.61641
H250	-0.0817952	0.585674
H294	0.104923	0.674859
H1100	0.576206	0.117012
H1130	0.526377	0.105627
X203	0.693681	0.0490706
X244	0.733686	0.0672382
X227	0.699648	-0.00668542

# Get variance of each factors
H = ["Measure","factor1", "factor2", "factor3", "factor4", "factor5"]
SI = ["SS Loadings","Proportion Var", "Cumulative Var"]
display(HTML(tabulate.tabulate(combinedfa_3.get_factor_variance(), headers=H, showindex = SI, tablefmt='html')))

Measure	factor1	factor2
SS Loadings	2.172	2.04018
Proportion Var	0.2172	0.204018
Cumulative Var	0.2172	0.421219

Cronbach's Alpha

#Define the cronbach's alpha function
def cronbachs_alpha(df):
    #Get the correlation matrix for the dataframe.
    df_corr = df.corr()
    
    #Calculate the number of items, N, and the mean correlation between the items, r.
    # N: number of dataframe columns
    N = df.shape[1]
    
    # r: Loop through the columns and append every relevant correlation to an array calles "rs". Then, calculate the mean of "r_s"
    rs = np.array([])
    for i, col in enumerate(df_corr.columns):
        sum_ = df_corr[col][i+1:].values
        rs = np.append(sum_, rs)
    r = np.mean(rs)
    
   # Use the formula to calculate Cronbach's Alpha 
    cronbachs_alpha = (N * r) / (1 + (N - 1) * r)
    return cronbachs_alpha




table = [["Ethics Alpha", str(cronbachs_alpha(ethicDF_3))],
        ["Moral Alpha", str(cronbachs_alpha(moralDF_3))],
        ["Combined Alpha",str(cronbachs_alpha(combinedDF_3))]]

display(HTML(tabulate.tabulate(table, tablefmt='html')))

Ethics Alpha	0.772026
Moral Alpha	0.783864
Combined Alpha	0.749925

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
IPIP2539-1.tab		IPIP2539-1.tab
README.ipynb		README.ipynb
README.md		README.md
ScalesFor967items.xls		ScalesFor967items.xls
YADDAT logo.svg		YADDAT logo.svg
app.css		app.css
end.html		end.html
end.js		end.js
game.css		game.css
game.html		game.html
game.js		game.js
highscores.css		highscores.css
highscores.html		highscores.html
highscores.js		highscores.js
index.html		index.html
output_24_0.png		output_24_0.png
output_25_0.png		output_25_0.png
output_26_0.png		output_26_0.png
questions.json		questions.json

Austin-Routt/YADDAT

Folders and files

Latest commit

History

Repository files navigation

Yet Another D&D Alignment Test

Introduction

Cleaning the Data

Adequacy Tests

Bartlett’s Test

Kaiser-Meyer-Olkin Test

Factor Analysis

Choosing the Number of Factors

Performing Factor Analysis

Cronbach's Alpha

About

Topics

Resources

Stars

Watchers

Forks

Languages