Antecedence of Challenge / Hindrance Stressor Appraisal

.title[
# Antecedence of Challenge / Hindrance Stressor Appraisal
]
.author[
### B, Belanger; L, Gibson; T, Levenhagen; A, Obyat; D, Chatterjee
]
.institute[
### Salem State University & Barruch University
]
.date[
### Press ‘f’ to go full screen then click <a href="#first_slide">Begin</a> For Mobile: Tap side of screen in the direction you want to go
]

---

background-image: url(data:image/png;base64,#presentation/img/salem_logo_small.png)
background-size: 7%
background-position: 99% 1%

---

# Thank you for visiting our research!

- ### Links in the presentation will be clickable .orange[[orange text](#first_slide)] and will take you to the section it is labeled as.

- ### On all slides except the "Navigator", there will be a clickable link at the bottom of the page to navigate straight back to the Navigator page.

- ### Click "Go to Navigator" to start seeing the data analysis process and other analyses that did not make it on our poster.

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#title)
| [**Next Page >>**](#navigator1)
|
]
---
name: navigator1

# Navigator

### 1. [Getting Started](#starting)
### 2. [Data Cleaning](#data_cleaning1)
### 3. [Descriptive Statistics](#descriptive1)
### 4. [Reliabilty](#reliability)
### 5. [Correlations](#correlation1)

]

### 6. [Hierarchical Regressions](#regression1)
### Exploratory / Post Hoc Analysis
### 7. [Stress Categories](#efa)
### 8. [Item Response Theory](#irt1)
### 9. [References](#references)

]

---
name: starting

# Getting Started | *Loading Required Packages*

```r
library(tidyverse)   # General cleaning and quality-of-life functions
library(apaTables)   # Create APA7 formatted tables straight from RStudio 
library(psych)       # Many functions for analysis
library(Hmisc)       # 
library(ggplot2)     # More aesthetically pleasing data visualizations
library(mirt)        # Item Response Theory analysis
library(GPArotation) # 
library(lavaan)
library(sem)
```

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#navigator1)
| [**Next Page >>**](#data_cleaning1)
|
]

---
name: data_cleaning1

# Data Cleaning | *Selecting Variables*

```r
# Read in the data from my "data" folder
df <- read.csv("data/rawdata.csv",
 header = TRUE,
 sep = ",") # import the data

df <- df %>% 
 select(!1:16) # remove SurveyMonkey default columns

# Change the names of the variables to easily refer to them
colnames(df) <- colnames(df) <- c("E1", "A1r", "C1", "N1r", "I1", "E2r", "A2", "C2r", "N2", "I2r",
 "E3", "A3r", "C3", "N3r", "I3r", "E4r", "A4", "C4r", "N4", "I4r",
 "POS1", "POS2", "POS3r", "POS4", "POS5", "POS6", "POS7", "POS8r", 
 "PSS1", "PSS2", "AC1_2", "PSS3", "PSS4r", "PCS1", "PCS2","PCS3r", 
 "PCS4", "PCS5", "PCS6", "Ch1", "Ch2", "Ch3", "S1", "S2", "S3",
 "AC2_4", "S4", "S5", "S6", "S7", "S8", "S9", "S10", "S11", "Hi1",
 "Hi2", "Hi3", "Gen", "Gen_id", "Age", "Ethn", "Ethn_id", 
 "Industry", "Tenure", "Feedback")
```

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#starting)
| [**Next Page >>**](#data_cleaning2)
|
]

---
name: data_cleaning2

# Data Cleaning | *Characterize Variables*

```r
# Define the numeric items (Likert scale items are set as numeric for future analysis)
numeric_items <- c("E1", "A1r", "C1", "N1r", "I1", "E2r", "A2", "C2r", "N2", "I2r",
 "E3", "A3r", "C3", "N3r", "I3r", "E4r", "A4", "C4r", "N4", "I4r",
 "POS1", "POS2", "POS3r", "POS4", "POS5", "POS6", "POS7", "POS8r", 
 "PSS1", "PSS2", "AC1_2", "PSS3", "PSS4r", "PCS1", "PCS2", "PCS3r", 
 "PCS4", "PCS5", "PCS6", "Ch1", "Ch2", "Ch3", "S1", "S2", "S3",
 "AC2_4", "S4", "S5", "S6", "S7", "S8", "S9", "S10", "S11", "Hi1", 
 "Hi2", "Hi3", "Age", "Tenure")

# Define the character items
character_items <- c("Gen_id", "Ethn_id", "Feedback")

# Apply appropriate item type accordingly
df[, numeric_items] <- sapply(df[, numeric_items], as.numeric)
df[, character_items] <- sapply(df[, character_items], as.character)
```

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning1)
| [**Next Page >>**](#data_cleaning3)
|
]

---
name: data_cleaning3

# Data Cleaning | *Nominal Data*

```r
# Assign the different factors to the demographic questions
df$Gen <- factor(df$Gen,
 levels = c(0, 1, 2, 3, 4),
 labels = c("Self id", "Male", "Female", "Nonbinary", "Prefer no answer"))

df$Ethn <- factor(df$Ethn,
 levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9),
 labels = c("Asian", "Black", "Latino", "M. Eastern", "Multiracial", "N. American", 
 "N. Hawaiian", "White", "Prefer no answer"))

df$Industry <- factor(df$Industry,
 levels = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
 14, 15, 16, 17, 18, 19, 20, 21),
 labels = c("Marketing", "Agriculture", "Aerospace", "Automotive", 
 "Business Support", "Construction", "Education", "Entertainment",
 "Finance", "Food", "Government", "Healthcare", "Insurance", 
 "Manufacturing", "Nonprofit", "Retail", "Real Estate",
 "Technology", "Transportation", "Utilities", "Self-employed"))
```

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning2)
| [**Next Page >>**](#data_cleaning4)
|
]

---
name: data_cleaning4

# Data Cleaning | *Attention Check Filtering*

### Participants who did not pass the two attention checks were removed from the sample

]

```r
df_ch <- df %>% 
 mutate(AC1_2 = recode(AC1_2, "2" = 1, .default = 0, .missing = 0)) %>% 
 mutate(AC2_4 = recode(AC2_4, "4" = 1, .default = 0, .missing = 0)) %>% 
 rowwise() %>% 
 mutate(ACT = sum(AC1_2, AC2_4)) %>% 
 filter(ACT == 2)
```

### n *before* and *after* attention check filtering: 704 -> 567

```r
# Assign the variables that need to be reverse coded (r means reversed)
vars_to_reverse <- grep("r$", names(df_ch), value = TRUE)

# 5-point Likert scale subtracted by 6 reverse codes the item
df_ch[, vars_to_reverse] <- 6 - df_ch[, vars_to_reverse]
```

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning3)
| [**Next Page >>**](#data_cleaning5)
|
]
---
name: data_cleaning5

# Data Cleaning | *Assigning Items To Objects*

```r
# Assign support items to objects
pos_items <- c("POS1", "POS2", "POS3r", "POS4", "POS5", "POS6", "POS7", "POS8r")
pss_items <- c("PSS1", "PSS2", "PSS3", "PSS4r")
pcs_items <- c("PCS1", "PCS2", "PCS3r", "PCS4", "PCS5", "PCS6")
support_items <- c(pos_items, pss_items, pcs_items)
```

```r
# Assign personality items to objects
e_items <- c("E1", "E2r", "E3", "E4r")
a_items <- c("A1r", "A2", "A3r", "A4")
c_items <- c("C1", "C2r", "C3", "C4r")
n_items <- c("N1r", "N2", "N3r", "N4")
i_items <- c("I1", "I2r", "I3r", "I4r")
```

]

```r
# Assign appraisal items to objects
ch_items <- c("Ch1", "Ch2", "Ch3")
hi_items <- c("Hi1", "Hi2", "Hi3")
```

### Now we can easily refer to those items

]
.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning4)
| [**Next Page >>**](#data_cleaning6)
|
]

---
name: data_cleaning6

# Data Cleaning | *Composite Variables*

```r
# Create an average composite for each person for personality variables
df_ch$extraversion <- rowMeans(df_ch[, e_items], na.rm = TRUE)
df_ch$agreeable <- rowMeans(df_ch[, a_items], na.rm = TRUE)
df_ch$conscientious <- rowMeans(df_ch[, c_items], na.rm = TRUE)
df_ch$neurotic <- rowMeans(df_ch[, n_items], na.rm = TRUE)
df_ch$intellect <- rowMeans(df_ch[, i_items], na.rm = TRUE)

# Create an average composite for each person for support variables
df_ch$pos <- rowMeans(df_ch[, pos_items], na.rm = TRUE)
df_ch$pss <- rowMeans(df_ch[, pss_items], na.rm = TRUE)
df_ch$pcs <- rowMeans(df_ch[, pcs_items], na.rm = TRUE)

# Create an average composite for each person for appraisal variables
df_ch$chal <- rowMeans(df_ch[, ch_items], na.rm = TRUE)
df_ch$hind <- rowMeans(df_ch[, hi_items], na.rm = TRUE)
```

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning5)
| [**Next Page >>**](#descriptive1)
|
]

---
name: descriptive1

# Descriptives | *Gender Identification*

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#data_cleaning6)
| [**Next Page >>**](#descriptive2)
|
]

---
name: descriptive2

# Descriptives | *Age Distribution*

### .orange[Orange line] indicates mean age

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#descriptive1)
| [**Next Page >>**](#descriptive3)
|
]

---
name: descriptive3

# Descriptives | *Age by Gender Identification*

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#descriptive2)
| [**Next Page >>**](#descriptive4)
|
]

---
name: descriptive4

# Descriptives | *Industry Representation*

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#descriptive3)
| [**Next Page >>**](#reliability)
|
]

---
name: reliability

# Reliabilities | `$\omega_T$` , `$\alpha$` and Schid Leiman `$\lambda$`

.panelset.sideways[

#### Factor Loadings:

```

Loadings:
    F1   
E1  0.689
E2r 0.655
E3  0.592
E4r 0.782

F1
SS loadings    1.866
Proportion Var 0.467
```

]

### Omega.white[.]Total:.white[.]0.78

### Coefficient.white[.]Alpha:.white[.]0.77

]

]

#### Factor Loadings:

```

Loadings:
    F1   
A1r 0.507
A2  0.765
A3r 0.690
A4  0.705

F1
SS loadings    1.817
Proportion Var 0.454
```

]

### Omega.white[.]Total:.white[.]0.77

### Coefficient.white[.]Alpha:.white[.]0.76

]

]

#### Factor Loadings:

```

Loadings:
    F1   
C1  0.616
C2r 0.715
C3  0.344
C4r 0.720

F1
SS loadings    1.528
Proportion Var 0.382
```

#### Item 3 is loading poorly

]

### Omega.white[.]Total:.white[.]0.7

#### Item 3 removed: 0.73

### Coefficient.white[.]Alpha:.white[.]0.68

#### Item 3 removed: 0.72

]

]

#### Factor Loadings:

```

Loadings:
    F1   
N1r 0.526
N2  0.768
N3r 0.429
N4  0.747

F1
SS loadings    1.608
Proportion Var 0.402
```

]

### Omega.white[.]Total:.white[.]0.72

### Coefficient.white[.]Alpha:.white[.]0.71

]

]

#### Factor Loadings:

```

Loadings:
    F1   
I1  0.755
I2r 0.326
I3r 0.353
I4r 0.874

F1
SS loadings    1.565
Proportion Var 0.391
```

]

### Omega.white[.]Total:.white[.]0.7

#### Item 2 & 3 removed: 0.84

### Coefficient.white[.]Alpha:.white[.]0.67

#### Item 2 and 3 removed: 0.84

]

]

#### Factor Loadings:

```

Loadings:
      F1   
POS1  0.904
POS2  0.865
POS3r 0.869
POS4  0.778
POS5  0.769
POS6  0.782
POS7  0.592
POS8r 0.705

F1
SS loadings    4.975
Proportion Var 0.622
```

]

### Omega.white[.]Total:.white[.]0.93

### Coefficient.white[.]Alpha:.white[.]0.93

]

]

#### Factor Loadings:

```

Loadings:
      F1   
PSS1  0.886
PSS2  0.899
PSS3  0.829
PSS4r 0.866

F1
SS loadings    3.031
Proportion Var 0.758
```

]

### Omega.white[.]Total:.white[.]0.93

### Coefficient.white[.]Alpha:.white[.]0.93

]

]

#### Factor Loadings:

```

Loadings:
      F1   
PCS1  0.687
PCS2  0.589
PCS3r 0.520
PCS4  0.619
PCS5  0.712
PCS6  0.868

F1
SS loadings    2.734
Proportion Var 0.456
```

]

### Omega.white[.]Total:.white[.]0.83

### Coefficient.white[.]Alpha:.white[.]0.83

]

]

#### Factor Loadings:

```

Loadings:
    F1   
Ch1 0.835
Ch2 0.904
Ch3 0.906

F1
SS loadings    2.337
Proportion Var 0.779
```

]

### Omega.white[.]Total:.white[.]0.91

### Coefficient.white[.]Alpha:.white[.]0.91

]

]

#### Factor Loadings:

```

Loadings:
    F1   
Hi1 0.863
Hi2 0.946
Hi3 0.893

F1
SS loadings    2.437
Proportion Var 0.812
```

]

### Omega.white[.]Total:.white[.]0.93

### Coefficient.white[.]Alpha:.white[.]0.93

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#descriptive4)
| [**Next Page >>**](#correlation1)
|
]

---
name: correlation1
background-image: url(data:image/png;base64,#presentation/img/cor_table.png)
background-size: 
background-position:

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#reliability)
| [**Next Page >>**](#regression1)
|
]

---
name: regression1
background-image: url(data:image/png;base64,#presentation/img/hier_chal_person_sup.png)
background-size: 60%
background-position: 5%, 25%

### **Personality Traits** then **Support Measures** Predicting **Challenge Appraisal**

### Support measures uniquely explain **35.4%** of the variance in the Challenge Appraisal measure separate from personality trait measures.

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#correlation1)
| [**Next Page >>**](#regression2)
|
]

---
name: regression2
background-image: url(data:image/png;base64,#presentation/img/hier_chal_sup_person.png)
background-size: 60%
background-position: 5%, 25%

### **Support Measures** then **Personality Traits** Predicting **Challenge Appraisal**

### Personality measures uniquely explain **1.5%** of the variance in Challenge Appraisal measure separate from support measures.

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#regression1)
| [**Next Page >>**](#regression3)
|
]

---
name: regression3
background-image: url(data:image/png;base64,#presentation/img/hier_hind_person_sup.png)
background-size: 60%
background-position: 5%, 25%

### **Personality Traits** then **Support Measures** Predicting **Hindrance Appraisal**

### Support measures uniquely explain **27.2%** of the variance in the Challenge Appraisal measure separate from personality trait measures.

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#regression2)
| [**Next Page >>**](#regression4)
|
]

---
name: regression4
background-image: url(data:image/png;base64,#presentation/img/hier_hind_sup_person.png)
background-size: 60%
background-position: 5%, 25%

### **Support Measures** then **Personality Traits** Predicting **Hindrance Appraisal**

### Personality trait measures uniquely explain **1.6%** of the variance in the Challenge Appraisal measure separate from support measures.

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#regression3)
| [**Next Page >>**](#efa)
|
]

---
name: efa

## Exploratory Factor Analysis on Stress Questions

.panelset.sideways[

#### This is a repeat of Cavanaugh et al. (2000) to see whether these "types" of stress fall into two (2) factors, namely Challenge and Hindrance Factors. Below are those questions questions and were given the following prompt: **Please indicate the level of stress you experience in the workplace for each scenario given below.**

1. The amount of projects and or assignments I have
2. The degree to which politics rather than performance affects organizational decisions
3. The amount of time I spend at work
4. The inability to clearly understand what is expected of me on the job
5. The volume of work that must be accomplished in the allotted time
6. The amount of red tape I need to go through to get my job done
7. Time pressures I experience
8. The lack of job security I have
9. The amount of responsibility I have
10. The degree to which my career seems "stalled"
11. The scope of responsibility my position entails

These were rated on a likert-type scale: *Produces no stress* - *Produces little stress* - *Produces some stress* - *Produces a lot of stress* - *Produces a great deal of stress*

]

#### .orange[Aggregate Stress Variables]

```r
s_items <- c("S1", "S2", "S3", "S4", "S5", 
 "S6", "S7", "S8", "S9", "S10", "S11")
df_stress <- df_ch[, s_items]
```

#### .orange[Disregard 'Na' Values]

```r
df_stress.nona <- na.omit(df_stress)
```

#### .orange[Correlate Stress Variables]

```r
stress_cor <- round(cor(df_stress.nona[s_items]), 2)
```

]

#### Bartlett's Test of Sphiricity

```r
cortest.bartlett(stress_cor,
                 n = nrow(df_stress.nona))
```

```
$chisq
[1] 2040.457

$p.value
[1] 0

$df
[1] 55
```

]

#### Kaizer-Meyer-Olkin Test

```r
KMO(stress_cor)
```

```
Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = stress_cor)
Overall MSA =  0.85
MSA for each item = 
  S1   S2   S3   S4   S5   S6   S7   S8   S9  S10  S11 
0.89 0.84 0.93 0.89 0.83 0.88 0.85 0.81 0.82 0.84 0.83 
```

]

#### Determinant

```r
det(stress_cor)
```

```
[1] 0.02573218
```

]

####

```r
stress_efa <- principal(df_stress.nona,
 nfactors = ncol(df_stress.nona))
stress_efa$values
```

```
 [1] 4.3876882 1.2739303 0.9476863 0.8029990 0.7766607 0.6455479 0.6136892
 [8] 0.5078418 0.4694313 0.3172493 0.2572760
```

]

]

```
Principal Components Analysis
Call: principal(r = df_stress.nona, nfactors = 2, rotate = "oblimin")
Standardized loadings (pattern matrix) based upon correlation matrix
    item   TC1   TC2   h2   u2 com
S9     9  0.85       0.67 0.33 1.0
S1     1  0.80       0.61 0.39 1.0
S11   11  0.79       0.59 0.41 1.0
S5     5  0.76       0.63 0.37 1.0
S7     7  0.66       0.57 0.43 1.1
S3     3  0.55       0.42 0.58 1.2
S8     8        0.78 0.56 0.44 1.0
S10   10        0.67 0.48 0.52 1.0
S4     4        0.61 0.37 0.63 1.0
S2     2        0.57 0.42 0.58 1.1
S6     6        0.43 0.33 0.67 1.6

TC1  TC2
SS loadings           3.52 2.14
Proportion Var        0.32 0.19
Cumulative Var        0.32 0.51
Proportion Explained  0.62 0.38
Cumulative Proportion 0.62 1.00

With component correlations of 
     TC1  TC2
TC1 1.00 0.42
TC2 0.42 1.00

Mean item complexity =  1.1
Test of the hypothesis that 2 components are sufficient.

The root mean square of the residuals (RMSR) is 0.09 
 with the empirical chi square 484.62 with prob < 4.2e-81

Fit based upon off diagonal values = 0.94
```
]

]

### The items Cavanaugh et al. (2000) found to be Challenge and Hindrance were further supported to have covariance together in a two (2) factor structure.

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#regression4)
| [**Next Page >>**](#irt1)
|
]

---
name: irt1

### Item Response Theory | *Extraversion Items*

.panelset.sideways[

#### (Donnellan et al., 2006) I am the life of the party

]

#### (Donnellan et al., 2006) I don't talk a lot

]

#### (Donnellan et al., 2006) I talk to a lot of different people

]

#### (Donnellan et al., 2006) I keep in the background

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#efa)
| [**Next Page >>**](#irt2)
|
]

---
name: irt2

### Item Response Theory | *Agreeableness Items*

.panelset.sideways[

#### (Donnellan et al., 2006) I am not really interested in others

]

#### (Donnellan et al., 2006) I sympathize with others' feelings

]

#### (Donnellan et al., 2006) I am not interested in other people's problems

]

#### (Donnellan et al., 2006) I feel others’ emotions

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt1)
| [**Next Page >>**](#irt3)
|
]

---
name: irt3

### Item Response Theory | *Conscientiousness Items*

.panelset.sideways[

#### (Donnellan et al., 2006) I get chores done right away

]

#### (Donnellan et al., 2006) I often forget to put things back in their proper place

]

#### (Donnellan et al., 2006) I like order

]

#### (Donnellan et al., 2006) I make a mess of things

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt2)
| [**Next Page >>**](#irt4)
|
]

---
name: irt4

### Item Response Theory | *Neuroticism Items*

.panelset.sideways[

#### (Donnellan et al., 2006) I am relaxed most of the time

]

#### (Donnellan et al., 2006) I have frequent mood swings

]

#### (Donnellan et al., 2006) I seldom feel blue

]

#### (Donnellan et al., 2006) I get upset easily

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt3)
| [**Next Page >>**](#irt5)
|
]

---
name: irt5

### Item Response Theory | *Intellect / Imagination Items*

.panelset.sideways[

#### (Donnellan et al., 2006) I have a vivid imagination

]

#### (Donnellan et al., 2006) I have difficulty understanding abstract ideas

]

#### (Donnellan et al., 2006) I am not interested in abstract ideas

]

#### (Donnellan et al., 2006) I do not have a good imagination

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt4)
| [**Next Page >>**](#irt6)
|
]

---
name: irt6

### Item Response Theory | *Percieved Organizational Support Items*

.panelset.sideways[

#### (Rhoades, 2001) My org really cares about my well-being

]

#### (Rhoades, 2001) My organization strongly considers my goals and values

]

#### (Rhoades, 2001) My organization shows little concern for me

]

#### (Rhoades, 2001) My organization cares about my opinions

]

#### (Rhoades, 2001) My organization is willing to help me if I need a special favor

]

#### (Rhoades, 2001) Help is available from my organization when I have a problem

]

#### (Rhoades, 2001) My organization would forgive an honest mistake on my part

]

#### (Rhoades, 2001) If given the opportunity, my organization would take advantage of me

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt5)
| [**Next Page >>**](#irt7)
|
]

---
name: irt7

### Item Response Theory | *Perceived Supervisor Support Items*

.panelset.sideways[

#### (Rhoades, 2001) My supervisor cares about my opinions

]

#### (Rhoades, 2001) My work supervisor really cares about my well-being

]

#### (Rhoades, 2001) My supervisor strongly considers my goals and values

]

#### (Rhoades, 2001) My supervisor shows very little concern for me

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt6)
| [**Next Page >>**](#irt8)
|
]

---
name: irt8

### Item Response Theory | *Perceived Coworker Support Items*

.panelset.sideways[

#### (Karasek, 1998) My coworkers are competent

]

#### (Karasek, 1998) My coworkers are interested in me

]

#### (Karasek, 1998) I have hostile coworkers

]

#### (Karasek, 1998) I have friendly coworkers

]

#### (Karasek, 1998) My coworkers work together

]

#### (Karasek, 1998) My coworkers are helpful

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt7)
| [**Next Page >>**](#irt9)
|
]

---
name: irt9

### Item Response Theory | *Challenge Appraisal Items*

.panelset.sideways[

#### (LePine et al., 2016) Working to fulfill the demands of my job helps to improve my personal growth and well-being

]

#### (LePine et al., 2016) I feel the demands of my job challenge me to achieve personal goals and accomplishment

]

#### (LePine et al., 2016) In general, I feel that my job promotes my personal accomplishment

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt8)
| [**Next Page >>**](#irt10)
|
]

---
name: irt10

### Item Response Theory | *Hindrance Appraisal Items*

.panelset.sideways[

#### (LePine et al., 2016) Working to fulfill the demands of my job thwarts my personal growth and well-being

]

#### (LePine et al., 2016) I feel the demands of my job constrain my achievement personal goals and development

]

#### (LePine et al., 2016) In general, I feel that my job hinders my personal accomplishment

]

]

]

]

.footnote[
| [**Go to Navigator**](#navigator1)
| [**<< Previous Page**](#irt9)
| [**Next Page >>**](#conclusion)
|
]

---
name: conclusion
class: inverse, middle, center

# Thank you for checking out our research!

## | [**<< Previous Page**](#irt10) | [**Go to Navigator**](#navigator1) | [**References**](#references)

---
name: references

### References

Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among U.S. managers. *Journal of Applied Psychology*, *85*, 65–74. https://doi.org/10.1037/0021-9010.85.1.65

Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. *Psychological Assessment*, *18*(2), 192–203. https://doi.org/10.1037/1040-3590.18.2.192

Karasek, R., Brisson, C., Kawakami, N., Houtman, I., Bongers, P., & Amick, B. (1998). The Job Content Questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteristics. *Journal of Occupational Health Psychology*, *3*, 322–355. https://doi.org/10.1037//1076-8998.3.4.322

LePine, M. A., Yiwen Zhange, Crawford, E. R., & Rich, B. L. (2016). Turning their pain to gain: Charismatic leader influence on follower stress appraisal and job performance. *Academy of Management Journal*, *59*, 1036–1059. https://doi.org/10.5465/amj.2013.0778

Rhoades, L., Eisenberger, R., & Armeli, S. (2001). Affective commitment to the organization: The contribution of perceived organizational support. *Journal of Applied Psychology*, *86*, 825–836. https://doi.org/10.1037/0021-9010.86.5.825