Data Preparation for Case 31

Complete Version

Author

Affiliation

Jae Jung

IBM 3302, Cal Poly Pomona

Published

January 5, 2025

1 Libraries

1.1 Loading common packages

# install.packages("haven")
library(tidyverse)
library(haven) 
library(labelled) 
library(sjlabelled)
library(janitor) 
library(sjPlot)
library(psych)
set_theme(theme_minimal())

2 Setting Up Data

2.1 Reading SPSS data

data <- read_sav("Case 31 Data_efcu_1.sav")
data <- data |> 
  clean_names()

# generate data dictionary
data |>
  view_df()

Data frame: data
ID	Name	Label	Values	Value Labels
1	q3	Employees are Courteous	1 5	Strongly Disagree Strongly Agree
2	q4	employees are helpful	1 5	Strongly Disagree Strongly Agree
3	q5	employees are professional	1 5	Strongly Disagree Strongly Agree
4	q6	employees are available	1 5	Strongly Disagree Strongly Agree
5	q7	savings rates	1 2 3 4 5 6	Very High High Average Low Very Low No Opinion
6	q8	statement frequency	1 2 3 4 5 6	Very High High Average Low Very Low No Opinion
7	q9	statement frequency	1 2 3 4 5	Too Often Very Often About Right Not Often Enough Never
8	q10	statement accuracy	1 2 3 4	Excellent Good Fair Poor
9	q11	statement understandable	1 2	Yes No
10	q12	Account Confidential	1 2	Yes No
11	q13	regular share account	1 2	Aware and Have Used Aware but Have Not Used
12	q14	special subaccounts	1 2	Aware and Have Used Aware but Have Not Used
13	q15	Christmas club account	1 2	Aware and Have Used Aware but Have Not Used
14	q16	IRA	1 2	Aware and Have Used Aware but Have Not Used
15	q17	Master credit card	1 2	Aware and Have Used Aware but Have Not Used
16	q18	signature loans	1 2	Aware and Have Used Aware but Have Not Used
17	q19	new car loans	1 2	Aware and Have Used Aware but Have Not Used
18	q20	late model car loans	1 2	Aware and Have Used Aware but Have Not Used
19	q21	older model car loans	1 2	Aware and Have Used Aware but Have Not Used
20	q22	household goods loans	1 2	Aware and Have Used Aware but Have Not Used
21	q23	recreational loans	1 2	Aware and Have Used Aware but Have Not Used
22	q24	share collateralized loan	1 2	Aware and Have Used Aware but Have Not Used
23	q25	IRA loans	1 2	Aware and Have Used Aware but Have Not Used
24	q26	line of credit loans	1 2	Aware and Have Used Aware but Have Not Used
25	q27	currently have loan	1 2	Yes No
26	q30	CU rates lower	1 5	Strongly Disagree Strongly Agree
27	q31	CU confidential	1 5	Strongly Disagree Strongly Agree
28	q32	CU is prompt	1 5	Strongly Disagree Strongly Agree
29	q33	CU meets needs of members	1 5	Strongly Disagree Strongly Agree
30	q34	CU loan applications simple	1 5	Strongly Disagree Strongly Agree
31	q37	overall rating	1 2 3 4 5 6	Excellent Good Average Poor Very Poor No Opinion
32	overall_employee_performace		range: 2.8-5.0
33	q38	headquarter	1 2	yes no
34	employeeperformance		range: 2.8-5.0

3 Data Preparation

3.1 Attitudes towards Employee Proficiency

3.1.1 q3

data |> # ctr + shift + M
  count(q3) |> 
  mutate(percent = n/sum(n))

# A tibble: 3 × 3
  q3                     n percent
  <dbl+lbl>          <int>   <dbl>
1 3                     13   0.102
2 4                     57   0.445
3 5 [Strongly Agree]    58   0.453

five_pt_likert <-  c("Strongly Disagree" = 1,
                      "Disagree" = 2,
                      "Uncertain" = 3,
                      "Agree" = 4,
                      "Strongly Agree" = 5)
data_clean <- data |> 
  set_value_labels(q3 = five_pt_likert) |> 
  set_variable_labels(q3 = "Employees are Courteous")

data_clean |> 
  select(q3) |> 
  var_label()

$q3
[1] "Employees are Courteous"

data_clean |> 
  select(q3) |> 
  val_labels() |> 
  unique()

# A tibble: 3 × 1
  q3                
  <dbl+lbl>         
1 4 [Agree]         
2 5 [Strongly Agree]
3 3 [Uncertain]

3.1.2 q4

data_clean <-  data_clean |> 
  set_variable_labels(q4 = "Employees are helpful") |> 
  set_value_labels(q4 = five_pt_likert) 

data_clean |> 
  select(q4) |>
  var_label()

$q4
[1] "Employees are helpful"

data_clean |> 
  select(q4) |> 
  val_labels()

# A tibble: 128 × 1
   q4                
   <dbl+lbl>         
 1 4 [Agree]         
 2 4 [Agree]         
 3 4 [Agree]         
 4 4 [Agree]         
 5 4 [Agree]         
 6 4 [Agree]         
 7 5 [Strongly Agree]
 8 5 [Strongly Agree]
 9 5 [Strongly Agree]
10 4 [Agree]         
# ℹ 118 more rows

3.1.3 q5 and q6

data_clean <- data_clean |> 
  set_variable_labels(q5 = "Employees are professional") |> 
  set_value_labels(q5 = five_pt_likert) |> 
  set_variable_labels(q6 = "Employees are available") |> 
  set_value_labels(q6 = five_pt_likert)

data_clean |> 
  select(q5, q6) |>
  var_label()

$q5
[1] "Employees are professional"

$q6
[1] "Employees are available"

data_clean |> 
  select(q5, q6) |> 
  val_labels()

# A tibble: 128 × 2
   q5                 q6                
   <dbl+lbl>          <dbl+lbl>         
 1 4 [Agree]          4 [Agree]         
 2 4 [Agree]          4 [Agree]         
 3 4 [Agree]          4 [Agree]         
 4 3 [Uncertain]      4 [Agree]         
 5 4 [Agree]          4 [Agree]         
 6 4 [Agree]          4 [Agree]         
 7 5 [Strongly Agree] 5 [Strongly Agree]
 8 4 [Agree]          4 [Agree]         
 9 5 [Strongly Agree] 5 [Strongly Agree]
10 4 [Agree]          4 [Agree]         
# ℹ 118 more rows

3.1.4 Reliability analysis

data_clean |> 
  select(q3:q6) |> 
  alpha() #.87: cutoff is .70


Reliability analysis   
Call: alpha(x = select(data_clean, q3:q6))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.87      0.87    0.85      0.62 6.6 0.019  4.2 0.59     0.65

    95% confidence boundaries 
         lower alpha upper
Feldt     0.82  0.87   0.9
Duhachek  0.83  0.87   0.9

 Reliability if an item is dropped:
   raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
q3      0.85      0.86    0.80      0.66 5.9    0.022 0.00056  0.67
q4      0.79      0.79    0.73      0.56 3.8    0.033 0.00800  0.57
q5      0.82      0.83    0.80      0.62 4.8    0.028 0.01894  0.67
q6      0.85      0.85    0.80      0.66 5.8    0.023 0.00610  0.68

 Item statistics 
     n raw.r std.r r.cor r.drop mean   sd
q3 128  0.80  0.81  0.73   0.66  4.4 0.66
q4 128  0.91  0.91  0.88   0.82  4.3 0.70
q5 128  0.85  0.85  0.78   0.73  4.1 0.66
q6 128  0.83  0.82  0.73   0.67  4.1 0.79

Non missing response frequency for each item
      2    3    4    5 miss
q3 0.00 0.10 0.45 0.45    0
q4 0.01 0.11 0.45 0.44    0
q5 0.00 0.18 0.56 0.26    0
q6 0.03 0.18 0.47 0.32    0

data_clean |> 
  select(q3:q6) |> 
  factanal(factors = 1, rotation = "none")


Call:
factanal(x = select(data_clean, q3:q6), factors = 1, rotation = "none")

Uniquenesses:
   q3    q4    q5    q6 
0.431 0.141 0.423 0.473 

Loadings:
   Factor1
q3 0.754  
q4 0.927  
q5 0.760  
q6 0.726  

               Factor1
SS loadings      2.531
Proportion Var   0.633

Test of the hypothesis that 1 factor is sufficient.
The chi square statistic is 11.35 on 2 degrees of freedom.
The p-value is 0.00344

data_clean |> 
  select(q3:q6) |> 
  fa(nfactors = 1, rotate = "none") |> 
  fa.diagram()

3.1.6 PCA (Principle Component Analysis)

data_clean |>
  select(q3:q6) |> 
  prcomp(scale. = TRUE) |> 
  summary() # % of variance explained by the first factor

Importance of components:
                          PC1    PC2    PC3     PC4
Standard deviation     1.6968 0.7450 0.5906 0.46601
Proportion of Variance 0.7198 0.1388 0.0872 0.05429
Cumulative Proportion  0.7198 0.8585 0.9457 1.00000

3.1.7 Scree plot

data_clean |> 
  select(q3:q6) |> 
  scree()

3.1.8 Creating a composite scale

data_clean <- data_clean |> 
  mutate(at_emp = rowMeans(across(q3:q6), na.rm = TRUE)) 

# from psych: descriptive statistics
describe(data_clean$at_emp)

   vars   n mean   sd median trimmed  mad  min max range  skew kurtosis   se
X1    1 128 4.21 0.59      4    4.25 0.74 2.75   5  2.25 -0.32    -0.69 0.05

3.2 q7r: Savings Rate

data_clean |> count(q7)

# A tibble: 5 × 2
  q7                n
  <dbl+lbl>     <int>
1  2 [High]        22
2  3 [Average]     83
3  4 [Low]         12
4  5 [Very Low]     1
5 NA               10

data_clean <- data_clean |> 
  mutate(q7r = case_when(q7 == 1 ~ 5,
                         q7 == 2 ~ 4,
                         q7 == 3 ~ 3,
                         q7 == 4 ~ 2,
                         q7 == 5 ~ 1,
                         TRUE    ~ NA)) |> 
  set_variable_labels(q7r = "Savings rates") |> 
  set_value_labels(q7r = c("Very Low" = 1,
                           "Low" = 2,
                           "Average" = 3,
                           "High" = 4,
                           "Very High" = 5))

data_clean |> count(q7r)

# A tibble: 5 × 2
  q7r               n
  <dbl+lbl>     <int>
1  1 [Very Low]     1
2  2 [Low]         12
3  3 [Average]     83
4  4 [High]        22
5 NA               10

3.3 q8r: Fund borrowing rates

data_clean |> count(q8)

# A tibble: 6 × 2
  q8                 n
  <dbl+lbl>      <int>
1  1 [Very High]     4
2  2 [High]         26
3  3 [Average]      59
4  4 [Low]          17
5  5 [Very Low]      1
6 NA                21

data_clean <- data_clean |> 
  mutate(q8r = case_when(q8 == 1 ~ 5,
                         q8 == 2 ~ 4,
                         q8 == 3 ~ 3,
                         q8 == 4 ~ 2,
                         q8 == 5 ~ 1,
                         TRUE    ~ NA)) |> 
  set_variable_labels(q8r = "Fund borrowing rates") |> 
  set_value_labels(q8r = c("Very Low" = 1,
                           "Low" = 2,
                           "Average" = 3,
                           "High" = 4,
                           "Very High" = 5))

data_clean |> count(q8r)

# A tibble: 6 × 2
  q8r                n
  <dbl+lbl>      <int>
1  1 [Very Low]      1
2  2 [Low]          17
3  3 [Average]      59
4  4 [High]         26
5  5 [Very High]     4
6 NA                21

3.4 q9r: Statement Frequency

data_clean |> count(q9)

# A tibble: 2 × 2
  q9                       n
  <dbl+lbl>            <int>
1 3 [About Right]         99
2 4 [Not Often Enough]    29

data_clean |> 
  select(q9) |> 
  class()

[1] "tbl_df"     "tbl"        "data.frame"

data_clean <- data_clean |> 
  mutate(q9r = 6 - q9) |> 
  set_variable_labels(q9r = "Statement Frequency") |> 
  set_value_labels(q9r = c("Never" = 1,
                           "Not Often Enough" = 2,
                           "About Right" = 3,
                           "Very Often" = 4,
                           "Too Often" = 5))
data_clean |> count(q9r)

# A tibble: 2 × 2
  q9r                      n
  <dbl+lbl>            <int>
1 2 [Not Often Enough]    29
2 3 [About Right]         99

3.5 q10r: Statement Accuracy

data_clean |> count(q10)

# A tibble: 4 × 2
  q10               n
  <dbl+lbl>     <int>
1 1 [Excellent]    68
2 2 [Good]         54
3 3 [Fair]          4
4 4 [Poor]          2

data_clean <- data_clean |> 
  mutate(q10r = 5 - q10) |> 
  set_variable_labels(q10r = "Statement Accuracy") |> 
  set_value_labels(q10r = c("Poor" = 1,
                           "Fair" = 2,
                           "Good" = 3,
                           "Excellent" = 4))
data_clean |> count(q10r)

# A tibble: 4 × 2
  q10r              n
  <dbl+lbl>     <int>
1 1 [Poor]          2
2 2 [Fair]          4
3 3 [Good]         54
4 4 [Excellent]    68

3.6 Awareness of Financial Services

aware_scale_3 <- c("Unaware" = 1,
                   "Aware but Have Not Used" = 2,
                   "Aware and Have Used" = 3)

3.6.1 q13r

data_clean |> count(q13)

# A tibble: 3 × 2
  q13                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        13
2 2 [Aware but Have Not Used]    32
3 3                              83

data_clean <- data_clean |> 
  mutate(q13r = 4 - q13) |> 
  set_variable_labels(q13r = "Regular share account") |> 
  set_value_labels(q13r = aware_scale_3)

data_clean |> count(q13r)

# A tibble: 3 × 2
  q13r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    83
2 2 [Aware but Have Not Used]    32
3 3 [Aware and Have Used]        13

3.6.2 Creating a function

library(rlang)


Attaching package: 'rlang'

The following objects are masked from 'package:sjlabelled':

    as_character, as_label

The following objects are masked from 'package:purrr':

    %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
    flatten_raw, invoke, splice

aware_recode <- function(tbl, var, var_label) {
  
  varr <- paste0(rlang::as_string(ensym(var)), "r")
  
  tbl <- tbl |> 
  mutate(!!varr := 4 - as.numeric({{var}})) |> 
  set_variable_labels(!!varr := var_label) |> 
  set_value_labels(!!varr := aware_scale_3)
  
  return(tbl)
 
}


data_clean <- data_clean |> 
  aware_recode(q15, "Special subaccounts")

data_clean |> 
  count(q15r)

# A tibble: 3 × 2
  q15r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    42
2 2 [Aware but Have Not Used]    75
3 3 [Aware and Have Used]        11

3.6.3 q14r

data_clean |> count(q14)

# A tibble: 3 × 2
  q14                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        12
2 2 [Aware but Have Not Used]    70
3 3                              46

data_clean <- data_clean |> 
  aware_recode(q14, "Special subaccounts")

data_clean |> count(q14r)

# A tibble: 3 × 2
  q14r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    46
2 2 [Aware but Have Not Used]    70
3 3 [Aware and Have Used]        12

3.6.4 q15r-q26

3.6.4.1 functions that didn’t work

3.6.5 q15r

data_clean |> count(q15)

# A tibble: 3 × 2
  q15                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        11
2 2 [Aware but Have Not Used]    75
3 3                              42

data_clean <- data_clean |> 
  aware_recode(q15, "Christmas club account")

data_clean |> count(q15r)

# A tibble: 3 × 2
  q15r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    42
2 2 [Aware but Have Not Used]    75
3 3 [Aware and Have Used]        11

3.6.6 q16r

data_clean |> count(q16)

# A tibble: 3 × 2
  q16                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        29
2 2 [Aware but Have Not Used]    64
3 3                              35

data_clean <- data_clean |> 
  aware_recode(q16, "IRA")
  
data_clean |> count(q16r)

# A tibble: 3 × 2
  q16r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    35
2 2 [Aware but Have Not Used]    64
3 3 [Aware and Have Used]        29

3.6.7 q17r

data_clean |> count(q17)

# A tibble: 3 × 2
  q17                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        56
2 2 [Aware but Have Not Used]    57
3 3                              15

data_clean <- data_clean |> 
  aware_recode(q17, "Master credit card")
  
data_clean |> count(q17r)

# A tibble: 3 × 2
  q17r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    15
2 2 [Aware but Have Not Used]    57
3 3 [Aware and Have Used]        56

3.6.8 q18r

data_clean |> count(q18)

# A tibble: 3 × 2
  q18                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        19
2 2 [Aware but Have Not Used]   104
3 3                               5

data_clean <- data_clean |> 
  aware_recode(q18, "Signature loans")

data_clean |> count(q18r)

# A tibble: 3 × 2
  q18r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                     5
2 2 [Aware but Have Not Used]   104
3 3 [Aware and Have Used]        19

3.6.9 q19r

data_clean |> count(q19)

# A tibble: 3 × 2
  q19                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        24
2 2 [Aware but Have Not Used]    99
3 3                               5

data_clean <- data_clean |> 
  aware_recode(q19, "New car loans")

data_clean |> count(q19r)

# A tibble: 3 × 2
  q19r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                     5
2 2 [Aware but Have Not Used]    99
3 3 [Aware and Have Used]        24

3.6.10 q20r

data_clean |> count(q20)

# A tibble: 3 × 2
  q20                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        14
2 2 [Aware but Have Not Used]   102
3 3                              12

data_clean <- data_clean |> 
  aware_recode(q20, "Late model car loans")

data_clean |> count(q20r)

# A tibble: 3 × 2
  q20r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    12
2 2 [Aware but Have Not Used]   102
3 3 [Aware and Have Used]        14

3.6.11 q21r

data_clean |> count(q21)

# A tibble: 3 × 2
  q21                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        12
2 2 [Aware but Have Not Used]    83
3 3                              33

data_clean <- data_clean |> 
  aware_recode(q21, "Older model car loans")

data_clean |> count(q21r)

# A tibble: 3 × 2
  q21r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    33
2 2 [Aware but Have Not Used]    83
3 3 [Aware and Have Used]        12

3.6.12 q22r

data_clean |> count(q22)

# A tibble: 3 × 2
  q22                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        11
2 2 [Aware but Have Not Used]    80
3 3                              37

data_clean <- data_clean |> 
  aware_recode(q22, "Household goods loans")

data_clean |> count(q22r)

# A tibble: 3 × 2
  q22r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    37
2 2 [Aware but Have Not Used]    80
3 3 [Aware and Have Used]        11

3.6.13 q23r

data_clean |> count(q23)

# A tibble: 3 × 2
  q23                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        13
2 2 [Aware but Have Not Used]    60
3 3                              55

data_clean <- data_clean |> 
  aware_recode(q23, "Recreational loans")

data_clean |> count(q23r)

# A tibble: 3 × 2
  q23r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    55
2 2 [Aware but Have Not Used]    60
3 3 [Aware and Have Used]        13

3.6.14 q24r

data_clean |> count(q24)

# A tibble: 3 × 2
  q24                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]         2
2 2 [Aware but Have Not Used]    57
3 3                              69

data_clean <- data_clean |> 
  aware_recode(q24, "Share collateralized loan")

data_clean |> count(q24r)

# A tibble: 3 × 2
  q24r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    69
2 2 [Aware but Have Not Used]    57
3 3 [Aware and Have Used]         2

3.6.15 q25r

data_clean |> count(q25)

# A tibble: 3 × 2
  q25                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]         4
2 2 [Aware but Have Not Used]    46
3 3                              78

data_clean <- data_clean |> 
  aware_recode(q25, "IRA loans")

data_clean |> count(q25r)

# A tibble: 3 × 2
  q25r                            n
  <dbl+lbl>                   <int>
1 1 [Unaware]                    78
2 2 [Aware but Have Not Used]    46
3 3 [Aware and Have Used]         4

3.6.16 q26r

data_clean |> count(q26)

# A tibble: 2 × 2
  q26                             n
  <dbl+lbl>                   <int>
1 1 [Aware and Have Used]        48
2 2 [Aware but Have Not Used]    80

data_clean <- data_clean |> 
  aware_recode(q26, "Line of credit loans")

data_clean |> count(q26r)

# A tibble: 2 × 2
  q26r                            n
  <dbl+lbl>                   <int>
1 2 [Aware but Have Not Used]    80
2 3 [Aware and Have Used]        48

3.7 q30 - q34

data_clean <- data_clean |> 
  set_value_labels(q30 = five_pt_likert,
                   q31 = five_pt_likert,
                   q32 = five_pt_likert,
                   q33 = five_pt_likert,
                   q34 = five_pt_likert)

3.8 q37r

data_clean |> count(q37)

# A tibble: 6 × 2
  q37                n
  <dbl+lbl>      <int>
1  1 [Excellent]    31
2  2 [Good]         71
3  3 [Average]      16
4  4 [Poor]          2
5  5 [Very Poor]     1
6 NA                 7

data_clean <- data_clean |> 
  mutate(q37r = case_when(q37 == 1 ~ 5,
                          q37 == 2 ~ 4,
                          q37 == 3 ~ 3,
                          q37 == 4 ~ 2,
                          q37 == 5 ~ 1,
                          q37 == 6 ~ NA)) |> 
  set_value_labels(q37r = c("Very Poor" = 1,
                            "Poor" = 2,
                            "Average" = 3,
                            "Good" = 4,
                            "Excellent" = 5))

data_clean |> count(q37r)

# A tibble: 6 × 2
  q37r               n
  <dbl+lbl>      <int>
1  1 [Very Poor]     1
2  2 [Poor]          2
3  3 [Average]      16
4  4 [Good]         71
5  5 [Excellent]    31
6 NA                 7

4 Renaming Clean Data

data_preped <- data_clean

data_preped |> 
  view_df()

Data frame: data_preped
ID	Name	Label	Values	Value Labels
1	q3	Employees are Courteous	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
2	q4	Employees are helpful	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
3	q5	Employees are professional	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
4	q6	Employees are available	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
5	q7	savings rates	1 2 3 4 5 6	Very High High Average Low Very Low No Opinion
6	q8	statement frequency	1 2 3 4 5 6	Very High High Average Low Very Low No Opinion
7	q9	statement frequency	1 2 3 4 5	Too Often Very Often About Right Not Often Enough Never
8	q10	statement accuracy	1 2 3 4	Excellent Good Fair Poor
9	q11	statement understandable	1 2	Yes No
10	q12	Account Confidential	1 2	Yes No
11	q13	regular share account	1 2	Aware and Have Used Aware but Have Not Used
12	q14	special subaccounts	1 2	Aware and Have Used Aware but Have Not Used
13	q15	Christmas club account	1 2	Aware and Have Used Aware but Have Not Used
14	q16	IRA	1 2	Aware and Have Used Aware but Have Not Used
15	q17	Master credit card	1 2	Aware and Have Used Aware but Have Not Used
16	q18	signature loans	1 2	Aware and Have Used Aware but Have Not Used
17	q19	new car loans	1 2	Aware and Have Used Aware but Have Not Used
18	q20	late model car loans	1 2	Aware and Have Used Aware but Have Not Used
19	q21	older model car loans	1 2	Aware and Have Used Aware but Have Not Used
20	q22	household goods loans	1 2	Aware and Have Used Aware but Have Not Used
21	q23	recreational loans	1 2	Aware and Have Used Aware but Have Not Used
22	q24	share collateralized loan	1 2	Aware and Have Used Aware but Have Not Used
23	q25	IRA loans	1 2	Aware and Have Used Aware but Have Not Used
24	q26	line of credit loans	1 2	Aware and Have Used Aware but Have Not Used
25	q27	currently have loan	1 2	Yes No
26	q30	CU rates lower	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
27	q31	CU confidential	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
28	q32	CU is prompt	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
29	q33	CU meets needs of members	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
30	q34	CU loan applications simple	1 2 3 4 5	Strongly Disagree Disagree Uncertain Agree Strongly Agree
31	q37	overall rating	1 2 3 4 5 6	Excellent Good Average Poor Very Poor No Opinion
32	overall_employee_performace		range: 2.8-5.0
33	q38	headquarter	1 2	yes no
34	employeeperformance		range: 2.8-5.0
35	at_emp		range: 2.8-5.0
36	q7r	Savings rates	1 2 3 4 5	Very Low Low Average High Very High
37	q8r	Fund borrowing rates	1 2 3 4 5	Very Low Low Average High Very High
38	q9r	Statement Frequency	1 2 3 4 5	Never Not Often Enough About Right Very Often Too Often
39	q10r	Statement Accuracy	1 2 3 4	Poor Fair Good Excellent
40	q13r	Regular share account	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
41	q15r	Christmas club account	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
42	q14r	Special subaccounts	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
43	q16r	IRA	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
44	q17r	Master credit card	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
45	q18r	Signature loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
46	q19r	New car loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
47	q20r	Late model car loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
48	q21r	Older model car loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
49	q22r	Household goods loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
50	q23r	Recreational loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
51	q24r	Share collateralized loan	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
52	q25r	IRA loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
53	q26r	Line of credit loans	1 2 3	Unaware Aware but Have Not Used Aware and Have Used
54	q37r		1 2 3 4 5	Very Poor Poor Average Good Excellent

5 Exporting Prepped Data

5.1 SPSS data

data_preped |> 
  write_sav("Case 31 Data_efcu_Prepped.sav")

5.2 Excel Data as Metadata

library(writexl)

data_preped |> 
  write_xlsx("Case 31 Data_efcu_Prepped.xlsx")

6 Appendix

6.1 alternative way using select(): in this case, we need to use anonymous function

data_clean |>
  (\(df) df |> 
    mutate(row_mean = rowMeans(select(df, q3, q4, q5, q6), na.rm = TRUE))
  )() |> 
  select(row_mean)

6.2 Alternative way to create a recode function for awareness

aware_recode <- function(tbl, var, var_label) {
  
  varr <- paste0(deparse(substitute(var)), "r")
  
  tbl <- tbl |> 
  mutate(!!sym(varr) := 4 - {{var}}
         ) |> 
  set_variable_labels(!!sym(varr) := var_label) |> 
  set_value_labels(!!sym(varr) := aware_scale_3)
  
  return(tbl)
 
}