HW assignment NO2
Mait Metelitsa
29 March 2019
- Overview of the dataset used
- Goals and hypotheses for data analysis
- Finding the main categories to describe exceptional or paranormal experiences
- Associations between factors of extraordinary experiences and the state of mental wellbeing and demographic variables
- Summary of the results
- Appendix No1 – labels and codes for questionnaire items
Overview of the dataset used
The dataset used has been sourced from organisation called Association of Religious Data Archives (ARDA) that collects and makes publicly available data collected in academic religion-related studies. Current dataset is from survey that assessed the level of encountering exceptional, or paranormal, experiences in one’s daily life and the subjective value placed on those experience. The items that describe paranormal experiences have been derived by pooling questions from different previously published relevant questionnaires. The data collection instrument also includes feedback for basic demographic variables such as gender, age group, religious confession and information about respondent´ s general mental wellbeing (e.g. level of depression, anxiety, aggressiveness etc.). The dataset consists of 711 individual observations, collected mainly from Germany. The descriptive measures and codebook for dataset is presented in the webpage of ARDA.
Goals and hypotheses for data analysis
The first goal of the analysis is to derivate and describe the main factors (or latent variables) of exceptional or paranormal experiences based on the pool of questions used. Idiomatically, factor or latent variable can be seen as a subset of questions that describes the same general topic, therefore leading to situation by which items from the same factor have strong covariance with each other. Each emergent factor is descriptively labelled based on the content of questionnaire items that are most strongly associated with the given latent variable.
The second main goal for analysis deals with the question of how the mental wellbeing indicators are related to level of experiencing exceptional or paranormal experiences and is the pattern of associations same across the religious confessions. In detail, the two main hypotheses being here:
Mental wellbeing related variables have stronger association with encountering exceptional or paranormal experiences in the group of respondents that have declared being religious compared to atheists (H1). By this hypothesis, it is expected that being religious creates an enabling context in one’s thinking and worldview by which inner emotional states are more readily explained and externalized through exceptional or paranormal experiences;
Secondly, I expect to see the same religion dependent contextual effect on experiencing exceptional mental states if Protestant and Catholic respondents are compared. To be more precise, it is expected that mental wellbing is more strongly related to the experiencing of exceptional mental states in group of Catholics compared to Protestant respondents (H2), as Catholicism emphasizes the personal level miracles and exceptional states more strongly than rather scripture focused Protestants.
Further, to control the possible confounding effect of age and gender imbalances between religious groups, measures of age groups and gender have been added to regression-based analyses.
Of note, as the current analysis report is presented in a reproducible format (i.e. also including the R! script used to compute the results), the exact analytical steps and parameters used will not be outlined in-detail in textual part of the report as the relevant information can be obtained by the interested reader from presented code chucks. I hope that it will add to the overall brevity of the report.
Press the arrow on the left to see full list of R! packages loaded
## Setup steps for data analysis
Sys.setenv(LANG = "en")
## Loading and installing packages
packages_to_load <- c("mice","qgraph","ggcorrplot","ggthemes","jtools","huxtable","ggstance","languageR","tidyverse","knitr","kableExtra")
for (package in packages_to_load) {
if (!require(package,character.only = TRUE, quietly = TRUE)) install.packages('package')
library(package,character.only= TRUE)
}##
## Attaching package: 'mice'## The following objects are masked from 'package:base':
##
## cbind, rbind##
## Attaching package: 'huxtable'## The following object is masked from 'package:ggplot2':
##
## theme_grey##
## Attaching package: 'ggstance'## The following objects are masked from 'package:ggplot2':
##
## geom_errorbarh, GeomErrorbarh## -- Attaching packages ------------------------------------------------------------------------------------------------------------------------------------ tidyverse 1.2.1 --## v tibble 2.1.1 v purrr 0.3.2
## v tidyr 0.8.3 v dplyr 0.8.0.1
## v readr 1.3.1 v stringr 1.2.0
## v tibble 2.1.1 v forcats 0.4.0## -- Conflicts --------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::add_rownames() masks huxtable::add_rownames()
## x tidyr::complete() masks mice::complete()
## x purrr::every() masks huxtable::every()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x huxtable::theme_grey() masks ggplot2::theme_grey()##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rows## The following object is masked from 'package:huxtable':
##
## add_footnotepackage <- NULLPress the arrow on the left to see the steps of inital data wrangling
## Loadning data
setwd("C:/Users/Mait Metelitsa/Desktop/HW_2")
data <- read.csv(file = "Exceptional Experience Questionnaire.csv",
header = TRUE,
stringsAsFactors = FALSE,
sep = ";",
dec = ",")
## Selecting questionnaire data
data_qs <- data[,c("P1DREAMS","P2WLDABS","P3OBLIV","P4POWERS","P5ANTFUT","P6PROTEC","P7THTSCH","P8NOSENS","P9HRVOIC","P10POWRS","P11LIGHT",
"P12SEPAR","P13SECRT","P14HEAT","P15FATE","P16ENRGY","P17THGTS","P18HAPNS","P19DYING","P20THTPR","P21EXPDG","P22MNDRS",
"P23RDMND","P25DRVIV","P26HELP","P27FEEL","P28RECON","P29VISNS","P30CHGED","P31TOUCH","P34CNCTS",
"P35CALNG","P36INNER","P37PLSAT","P38CRSED","P40CNTCT","P41SEEPT","P42DISCT","P43FLKNG","P44TRVIL","P45PRSCE","P46CONSU","P47ADVIC",
"P48INSPR","P49RADTN","P50SRNDS","P51EGO","P52FORGN","P53NOFLW","P54EXPWR","P55GODLY","P56KDHTD","P57KNWLG")]
## Filling-in missing values (imputing missing values using Full Information Maximum Likelihood based estimations)
data_qs <- mice(data_qs,m = 5,printFlag = FALSE)$data
## Correlation matrix
corMat <- cor(data_qs, use = "pairwise.complete.obs",method = "pearson")Finding the main categories to describe exceptional or paranormal experiences
Appendix No1 of the report presents the codes and long labels for items of the questionnaire that describe going through exceptional or paranormal experiences. In order to select groups of items that have stronger covariances between them, correlation matrix (Plot NO1) and partial correlation network (Plot NO2) based visual exploratory analyses were performed. Initial hypothesis for factor structure was stringently (in a statistical sense) tested by using structural equation modelling (SEM) based approach.
## Correlation plot
ggcorrplot(corMat, hc.order = TRUE,
type = "lower",
lab = TRUE,
lab_size = 3,
method="circle",
digits = 2,
colors = c("#B3000C", "white", "#76BA1B"),
title="Correlogram of raw questionnaire data",
ggtheme=theme_tufte(),
tl.cex = 11)
Plot NO1: Correlogram of questionnaire items
## Partial correlation network
Graph_lasso <- qgraph(corMat, graph = "glasso", layout = "spring", tuning = 0.5,
sampleSize = nrow(data_qs))
Plot NO2: Partial correlation network (Note: Only the three first letters of questionnaire item code are presented on the graph; stronger and greener lines between nodes of the graph depict stronger partial correlations between the variables)
Press the arrow on the left to see full output of confirmatory factor analysis
## Confirmatory factor analysis
## Specifying the model
FA.model <- ' ## Latent variables
Higher_energies =~ P4POWERS + P6PROTEC + P7THTSCH + P16ENRGY + P45PRSCE + P48INSPR + P49RADTN + P55GODLY + P56KDHTD + P57KNWLG
Disassociation =~ P2WLDABS + P3OBLIV + P19DYING + P20THTPR + P42DISCT + P43FLKNG + P50SRNDS
Thinking_patterns =~ P9HRVOIC + P10POWRS + P15FATE + P17THGTS + P23RDMND + P47ADVIC + P52FORGN
Dreams =~ P1DREAMS + P5ANTFUT + P22MNDRS + P25DRVIV
## QUestionnaire item level co-variance
P56KDHTD ~~ P57KNWLG
P55GODLY ~~ P56KDHTD
P42DISCT ~~ P50SRNDS
'
## Fitting the model to the data
fit <- lavaan::sem(FA.model, data=data_qs,missing = "FIML", estimator = "MLR")
lavaan::summary(fit, fit.measures=TRUE,standardized = TRUE)## lavaan 0.6-3 ended normally after 87 iterations
##
## Optimization method NLMINB
## Number of free parameters 93
##
## Number of observations 711
## Number of missing patterns 46
##
## Estimator ML Robust
## Model Fit Test Statistic 1026.512 866.191
## Degrees of freedom 341 341
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.185
## for the Yuan-Bentler correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 7721.345 6388.034
## Degrees of freedom 378 378
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.907 0.913
## Tucker-Lewis Index (TLI) 0.897 0.903
##
## Robust Comparative Fit Index (CFI) 0.914
## Robust Tucker-Lewis Index (TLI) 0.905
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -23471.016 -23471.016
## Scaling correction factor 1.475
## for the MLR correction
## Loglikelihood unrestricted model (H1) -22957.760 -22957.760
## Scaling correction factor 1.247
## for the MLR correction
##
## Number of free parameters 93 93
## Akaike (AIC) 47128.032 47128.032
## Bayesian (BIC) 47552.733 47552.733
## Sample-size adjusted Bayesian (BIC) 47257.435 47257.435
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.053 0.047
## 90 Percent Confidence Interval 0.049 0.057 0.043 0.050
## P-value RMSEA <= 0.05 0.080 0.946
##
## Robust RMSEA 0.051
## 90 Percent Confidence Interval 0.046 0.055
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049 0.049
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv
## Higher_energies =~
## P4POWERS 1.000 0.938
## P6PROTEC 0.956 0.049 19.528 0.000 0.897
## P7THTSCH 0.946 0.046 20.553 0.000 0.887
## P16ENRGY 0.699 0.049 14.258 0.000 0.655
## P45PRSCE 0.895 0.049 18.089 0.000 0.839
## P48INSPR 0.829 0.043 19.200 0.000 0.778
## P49RADTN 0.831 0.042 19.985 0.000 0.779
## P55GODLY 0.881 0.043 20.619 0.000 0.826
## P56KDHTD 0.696 0.044 15.696 0.000 0.653
## P57KNWLG 0.708 0.044 16.089 0.000 0.664
## Disassociation =~
## P2WLDABS 1.000 0.565
## P3OBLIV 0.926 0.083 11.177 0.000 0.523
## P19DYING 1.195 0.098 12.177 0.000 0.675
## P20THTPR 1.147 0.094 12.229 0.000 0.648
## P42DISCT 1.023 0.077 13.337 0.000 0.578
## P43FLKNG 1.317 0.095 13.794 0.000 0.744
## P50SRNDS 0.955 0.074 12.952 0.000 0.539
## Thinking_patterns =~
## P9HRVOIC 1.000 0.131
## P10POWRS 3.029 0.594 5.101 0.000 0.397
## P15FATE 3.229 0.657 4.913 0.000 0.423
## P17THGTS 3.913 0.782 5.006 0.000 0.513
## P23RDMND 3.532 0.667 5.292 0.000 0.463
## P47ADVIC 3.905 0.793 4.926 0.000 0.512
## P52FORGN 3.119 0.671 4.647 0.000 0.409
## Dreams =~
## P1DREAMS 1.000 0.590
## P5ANTFUT 0.745 0.071 10.559 0.000 0.440
## P22MNDRS 1.308 0.092 14.277 0.000 0.772
## P25DRVIV 1.193 0.081 14.773 0.000 0.703
## Std.all
##
## 0.735
## 0.629
## 0.701
## 0.600
## 0.723
## 0.707
## 0.694
## 0.747
## 0.673
## 0.660
##
## 0.572
## 0.519
## 0.673
## 0.622
## 0.627
## 0.695
## 0.628
##
## 0.380
## 0.542
## 0.547
## 0.616
## 0.577
## 0.645
## 0.542
##
## 0.652
## 0.476
## 0.792
## 0.690
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .P56KDHTD ~~
## .P57KNWLG 0.045 0.024 1.849 0.064 0.045
## .P55GODLY ~~
## .P56KDHTD 0.217 0.030 7.362 0.000 0.217
## .P42DISCT ~~
## .P50SRNDS 0.139 0.027 5.116 0.000 0.139
## Higher_energies ~~
## Disassociation 0.335 0.031 10.666 0.000 0.632
## Thinkng_pttrns 0.094 0.019 5.066 0.000 0.764
## Dreams 0.278 0.031 8.999 0.000 0.503
## Disassociation ~~
## Thinkng_pttrns 0.051 0.011 4.653 0.000 0.690
## Dreams 0.161 0.022 7.193 0.000 0.483
## Thinking_patterns ~~
## Dreams 0.038 0.009 4.126 0.000 0.494
## Std.all
##
## 0.083
##
## 0.411
##
## 0.290
##
## 0.632
## 0.764
## 0.503
##
## 0.690
## 0.483
##
## 0.494
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .P4POWERS 1.750 0.048 36.444 0.000 1.750 1.372
## .P6PROTEC 2.121 0.054 39.324 0.000 2.121 1.488
## .P7THTSCH 1.270 0.048 26.638 0.000 1.270 1.004
## .P16ENRGY 0.830 0.041 20.091 0.000 0.830 0.760
## .P45PRSCE 0.969 0.044 22.072 0.000 0.969 0.835
## .P48INSPR 1.314 0.042 31.560 0.000 1.314 1.195
## .P49RADTN 2.213 0.042 52.494 0.000 2.213 1.972
## .P55GODLY 1.067 0.042 25.621 0.000 1.067 0.965
## .P56KDHTD 0.735 0.037 19.979 0.000 0.735 0.758
## .P57KNWLG 0.951 0.038 25.067 0.000 0.951 0.945
## .P2WLDABS 1.035 0.037 27.866 0.000 1.035 1.048
## .P3OBLIV 1.660 0.038 43.658 0.000 1.660 1.647
## .P19DYING 0.763 0.038 20.109 0.000 0.763 0.761
## .P20THTPR 1.306 0.039 33.294 0.000 1.306 1.253
## .P42DISCT 0.861 0.035 24.773 0.000 0.861 0.935
## .P43FLKNG 0.998 0.041 24.609 0.000 0.998 0.933
## .P50SRNDS 0.726 0.033 22.312 0.000 0.726 0.845
## .P9HRVOIC 0.075 0.013 5.673 0.000 0.075 0.216
## .P10POWRS 0.343 0.028 12.321 0.000 0.343 0.469
## .P15FATE 0.454 0.029 15.541 0.000 0.454 0.587
## .P17THGTS 0.479 0.032 15.156 0.000 0.479 0.575
## .P23RDMND 0.538 0.030 17.817 0.000 0.538 0.671
## .P47ADVIC 0.365 0.030 12.142 0.000 0.365 0.460
## .P52FORGN 0.509 0.028 17.882 0.000 0.509 0.675
## .P1DREAMS 1.426 0.034 41.984 0.000 1.426 1.576
## .P5ANTFUT 0.945 0.035 27.133 0.000 0.945 1.022
## .P22MNDRS 1.506 0.037 41.174 0.000 1.506 1.546
## .P25DRVIV 1.654 0.038 43.153 0.000 1.654 1.621
## Higher_energis 0.000 0.000 0.000
## Disassociation 0.000 0.000 0.000
## Thinkng_pttrns 0.000 0.000 0.000
## Dreams 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .P4POWERS 0.747 0.046 16.265 0.000 0.747 0.459
## .P6PROTEC 1.229 0.069 17.761 0.000 1.229 0.605
## .P7THTSCH 0.813 0.055 14.675 0.000 0.813 0.508
## .P16ENRGY 0.765 0.050 15.189 0.000 0.765 0.640
## .P45PRSCE 0.643 0.047 13.651 0.000 0.643 0.477
## .P48INSPR 0.605 0.042 14.434 0.000 0.605 0.500
## .P49RADTN 0.652 0.040 16.158 0.000 0.652 0.518
## .P55GODLY 0.542 0.040 13.653 0.000 0.542 0.443
## .P56KDHTD 0.516 0.034 15.273 0.000 0.516 0.548
## .P57KNWLG 0.572 0.036 15.740 0.000 0.572 0.565
## .P2WLDABS 0.655 0.044 14.921 0.000 0.655 0.672
## .P3OBLIV 0.742 0.044 17.049 0.000 0.742 0.731
## .P19DYING 0.550 0.044 12.447 0.000 0.550 0.547
## .P20THTPR 0.666 0.038 17.418 0.000 0.666 0.614
## .P42DISCT 0.514 0.034 14.963 0.000 0.514 0.606
## .P43FLKNG 0.591 0.042 14.096 0.000 0.591 0.517
## .P50SRNDS 0.448 0.034 13.016 0.000 0.448 0.606
## .P9HRVOIC 0.102 0.025 4.105 0.000 0.102 0.856
## .P10POWRS 0.378 0.044 8.689 0.000 0.378 0.706
## .P15FATE 0.419 0.037 11.337 0.000 0.419 0.700
## .P17THGTS 0.431 0.044 9.792 0.000 0.431 0.621
## .P23RDMND 0.428 0.032 13.410 0.000 0.428 0.667
## .P47ADVIC 0.367 0.043 8.536 0.000 0.367 0.584
## .P52FORGN 0.401 0.030 13.438 0.000 0.401 0.706
## .P1DREAMS 0.471 0.034 14.042 0.000 0.471 0.575
## .P5ANTFUT 0.661 0.038 17.231 0.000 0.661 0.774
## .P22MNDRS 0.353 0.036 9.701 0.000 0.353 0.372
## .P25DRVIV 0.546 0.043 12.547 0.000 0.546 0.524
## Higher_energis 0.879 0.065 13.591 0.000 1.000 1.000
## Disassociation 0.319 0.039 8.187 0.000 1.000 1.000
## Thinkng_pttrns 0.017 0.007 2.508 0.012 1.000 1.000
## Dreams 0.348 0.044 7.968 0.000 1.000 1.000## Predicting values for latent variables from the model estimated previously
data_for_regressions <- lavaan::lavPredict(fit,method = "regression")
data_for_regressions <- cbind(data_for_regressions,data[,c("TRANSPER","SOCSUPP","BSIZWA","BSIUIS","BSIDEP","BSIANG","BSIAGG","BSIPARD","I.AGE","I.GENDER","I.RELIGION")])
colnames(data_for_regressions) <- c("Higher_energies","Disassociation","Thinking_patterns","Dreams",
"Transpersonal.trust","Social.support","Compulsiveness",
"Social.insecurity","Depression","Anxiety","Aggression","Paranoid.thinking","Age","Gender","Religion")
## Subsetting variable about religious views
data_for_regressions[data_for_regressions[,"Religion"] %in% c(1,"1"),"Religion"] <- "Catholic"
data_for_regressions[data_for_regressions[,"Religion"] %in% c(2,"2"),"Religion"] <- "Protestant"
data_for_regressions[data_for_regressions[,"Religion"] %in% c(3,"3"),"Religion"] <- "Atheist"
data_for_regressions[data_for_regressions[,"Religion"] %in% c(4,"4"),"Religion"] <- "Other"Based on visual exploratory analysis and confirmatory factor analysis four major thematic groups of questions emerged:
Firstly, feeling of presence of higher energies (labelled: Higher_energies) that is associated with aspects describing being inspired by spiritual powers at work, being helped by higher powers, having thoughts that can change outer physical environment, feeling of energy moving through one’s spine, having episodes of divine inspirations, feeling the special energies of others, sense of being filled with light and power, sense of being filled with kind-hearted light and sense of obtaining knowledge in sudden bursts.
Secondly, feeling of being disassociated from the outer world (labelled: Disassociation). This factor is related to questionnaire items describing seeing the world as absurd, feeling oblivious to the world around, having the sense of going through the process of dying, slowing down of one’s thought process, perception of being disconnected form environment, having flaky view of the world around and being completely disconnected from surrounding environment.
Thirdly, having unorthodox thinking patterns (labelled: Thinking_patterns). This factor is related to questionnaire items describing hearing external voices in one’s head that are insulting, perception of being controlled by external powers, seeing one’s fate, having one’s thoughts changed by external forces, sensing that others can read one’s thoughts, hearing external voices in one’s head that give helpful advice and seeing some of one’s thoughts as being foreign.
Fourthly, having pronounced or extra vivid dreams (labelled: Dreams). This factor is related to questionnaire items describing having strange dreams, seeing future events in one’s dreams, having dreams that are especially meaningful and being influenced for a long time by the vividness of one’s dreams.
Associations between factors of extraordinary experiences and the state of mental wellbeing and demographic variables
Associations between factors of extraordinary experiences and the state of mental wellbeing have been assessed through four different regression models, results of which have been presented graphically on following plots (Plots NO3 -NO6). Each plot presents standardized regression coefficients between dependent variable describing category of extraordinary experience and facets of mental well-being and demographic variables (age group and gender). Of note, regression models have been fitted separately by three religious’ groups (Catholics, Protestants and Atheists).
Press the arrow on the left to see analytical steps performed to assess the influence of mental wellbeing to the feeling of presence of higher energies
Higher_energies_regression_Catholic <- lm(Higher_energies ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Catholic",])
Higher_energies_regression_Protestant <- lm(Higher_energies ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Protestant",])
Higher_energies_regression_Atheist <- lm(Higher_energies ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Atheist",])
Higher_energies_regression_plot <-
jtools::plot_summs(Higher_energies_regression_Catholic, Higher_energies_regression_Protestant, Higher_energies_regression_Atheist, scale = TRUE,
model.names = c("Catholic", "Protestant", "Atheist"), legend.title = "Religious group") Higher_energies_regression_plot
Plot NO3: Influence of mental wellbeing on experiencing the presence of higher energies (standardized regression coefficients; gender coefficient in comparison to base group of males).
Press the arrow on the left to see analytical steps performed to assess the influence of mental wellbeing to the unusual thinking patterns
Thinking_patterns_regression_Catholic <- lm(Thinking_patterns ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Catholic",])
Thinking_patterns_regression_Protestant <- lm(Thinking_patterns ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Protestant",])
Thinking_patterns_regression_Atheist <- lm(Thinking_patterns ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Atheist",])
Thinking_patterns_regression_plot <-
jtools::plot_summs(Thinking_patterns_regression_Catholic, Thinking_patterns_regression_Protestant, Thinking_patterns_regression_Atheist, scale = TRUE,
model.names = c("Catholic", "Protestant", "Atheist"), legend.title = "Religious group") Thinking_patterns_regression_plot
Plot NO4: Influence of mental wellbeing on experiencing unusual thinking patterns (standardized regression coefficients; gender coefficient in comparison to base group of males).
Press the arrow on the left to see analytical steps performed to assess the influence of mental wellbeing to the experience of having extra vivid dream states
Dreams_regression_Catholic <- lm(Dreams ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Catholic",])
Dreams_regression_Protestant <- lm(Dreams ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Protestant",])
Dreams_regression_Atheist <- lm(Dreams ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Atheist",])
Dreams_regression_plot <-
jtools::plot_summs(Dreams_regression_Catholic, Dreams_regression_Protestant, Dreams_regression_Atheist, scale = TRUE,
model.names = c("Catholic", "Protestant", "Atheist"), legend.title = "Religious group") Dreams_regression_plot
Plot NO5: Influence of mental wellbeing on experiencing unusally vivid dreams (standardized regression coefficients; gender coefficient in comparison to base group of males).
Press the arrow on the left to see analytical steps performed to assess the influence of mental wellbeing to the dissociative states of mind
Disassociation_regression_Catholic <- lm(Disassociation ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Catholic",])
Disassociation_regression_Protestant <- lm(Disassociation ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Protestant",])
Disassociation_regression_Atheist <- lm(Disassociation ~ Gender+ Social.insecurity + Depression + Anxiety + Aggression +
Paranoid.thinking + Age,
data = data_for_regressions[data_for_regressions[,"Religion"] == "Atheist",])
Disassociation_regression_plot <-
jtools::plot_summs(Disassociation_regression_Catholic, Disassociation_regression_Protestant, Disassociation_regression_Atheist, scale = TRUE,
model.names = c("Catholic", "Protestant", "Atheist"), legend.title = "Religious group")Disassociation_regression_plot
Plot NO6: Influence of mental wellbeing on experiencing disassociative states (standardized regression coefficients; gender coefficient in comparison to base group of males).
Summary of the results
The main take-home-message of the analysis is the fact that extraordinary or paranormal experiences can be categorized into four major groups: feeling of presence of higher energies, feeling of being disassociated from the outer world, having unorthodox thinking patterns and having pronounced or extra vivid dreams.
The second most notable and generalizable finding from regression analysis is the influence of age to the propensity to experience extraordinary or paranormal mental states. The effect of age is especially clearly seen in respect to experiencing higher energies across all three religious’ groups, including atheists. Similarly, unusual thinking patterns and mental disassociation are more often experienced in older age groups that are either Catholic or Protestant, but not in the group of atheists. Further studies need to address whether the observed effects are related to actual age of the person or rather constitute the cohort effect, meaning that older people come from cultural background of their youth during which the belief in supernatural forces was generally more accepted.
Further notable and somewhat interesting finding is the fact that females compared in comparison to males seem to be more inclined to experience extraordinary or paranormal mental states. Firstly, Catholic and Protestant females have higher frequency of experiencing the presence of higher energies compared to respective groups of Catholic and Protestant males. Secondly, females, irrespective to their religious views, seem to experience more often vivid or pronounced dream states. Jokingly, the wisdom seen in folk tales, by which brooms are used as a mean of transportation mainly by females seems to have at least some data-based grounding.
As for the hypotheses presented in the beginning of the analysis about the associations between state of mental wellbeing and experience of extraordinary mental states (H1) dependent on religious leanings (H2), no or only weak corroboration can be found from regression analysis. Only the state of anxiety seems to be more broadly associated with different facets of extraordinary or paranormal experiences. In detail, higher anxiety level is associated with more frequent experiencing of higher energies in both religious groups (Protestants and Catholics) but not in the group of atheists, therefore supporting the H1 presented in the beginning of the analysis. Further, unusual thinking patterns and extra vivid dreams are dependent on anxiety level only in the group of Catholic respondents which gives partial support for hypotheses H1 and H2.
Appendix No1 – labels and codes for questionnaire items
codes_labels <- read.csv("Questionnaire_codes_labels.csv", stringsAsFactors = FALSE, sep = ";")
knitr::kable(codes_labels,
caption = "Table1: Labels and codes for questionnaire items") %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = FALSE,
position = "c")| Code | Label |
|---|---|
| P1DREAMS | I have strange and unordinary dreams. |
| P2WLDABS | The world around me seems absurd or exaggerated. |
| P3OBLIV | I feel oblivious to the world around me. |
| P4POWERS | Spiritual powers inspire me at work. |
| P5ANTFUT | I anticipate future events in my dreams. |
| P6PROTEC | A higher being protects and/or helps me. |
| P7THTSCH | Through my thoughts the world around me is changed without me doing any physical action. |
| P8NOSENS | I find myself in a state with no clear sense of time. |
| P9HRVOIC | I clearly hear, without noticeable external influence, voices that insult or make fun of me. |
| P10POWRS | External powers control me. |
| P11LIGHT | Everything is dipped into a special kind of light. |
| P12SEPAR | My mind is separated from my body. |
| P13SECRT | Everything has a deep and secret meaning, even street signs and license plates. |
| P14HEAT | I experience conditions of extreme heat in parts of my body or in my entire body. |
| P15FATE | I can see my fate. |
| P16ENRGY | Something that feels like energy flows upward through my inner spine. For example the flow of fluid or Light. |
| P17THGTS | My thoughts are changed by an external power. |
| P18HAPNS | I think of something and in that moment it happens. |
| P19DYING | A part of me is dying. |
| P20THTPR | My thought process is slowing down. |
| P21EXPDG | My entire body, or just a part of it, is expanding. |
| P22MNDRS | I have meaningful dreams. |
| P23RDMND | Others read my mind or hear my thoughts. |
| P25DRVIV | I dream vividly so that my dreams have long lasting effect on me. |
| P26HELP | I stand by people, who are in need or emergency, in spirit and am thereby able to help them. |
| P27FEEL | I feel vibrations, pins and needles, and pricks on my insides or under my skin. |
| P28RECON | I am reconciled with all. |
| P29VISNS | I have visions of beings, places, or events in my mind’s eye. |
| P30CHGED | I am being changed/transformed. |
| P31TOUCH | I am in touch with everything. |
| P34CNCTS | All of a sudden, I recognize connections in the universe. |
| P35CALNG | I know my calling. |
| P36INNER | I hold conversations with my inner voice. |
| P37PLSAT | Energies pulsate through my body. |
| P38CRSED | I am cursed. |
| P40CNTCT | I have contact with higher beings. |
| P41SEEPT | I see my past, or parts of it, play before my mind as if it were a movie. |
| P42DISCT | I perceive myself disconnected from my environment. |
| P43FLKNG | My view of the world is flaking away. |
| P44TRVIL | Nothing seems trivial to me. |
| P45PRSCE | I feel the presence of higher or non-earthly beings. |
| P46CONSU | I am consumed by a thought and cannot stop thinking about it. |
| P47ADVIC | I clearly hear, without noticeable external influence, voices that give me helpful advice. |
| P48INSPR | I have inspirations. |
| P49RADTN | Some people give off special radiation that I can feel. |
| P50SRNDS | I feel completely disconnected from my surroundings. |
| P51EGO | My ego inner self) becomes one with other living beings or the world. |
| P52FORGN | Some of my thoughts seem foreign, as if they were not my own. |
| P53NOFLW | I can no longer follow my thoughts. |
| P54EXPWR | A strong, external power is taking over my body. |
| P55GODLY | I am completely filled with Godly light and power. |
| P56KDHTD | I am completely filled with kindhearted light. |