psychonetrics (version 0.15)

NA2020: Network Analysis 2020 Self-Report Data

Description

A subset of self-report data collected in 2020 by Adela-Maria Isvoranu as part of a graduate-level course on network analysis at the University of Amsterdam. Students in the course collected the data from a convenience sample. The full dataset contains responses to 87 items covering topics such as sleep, well-being, and social functioning. This subset contains 8 items used in Chapter 6 of the textbook Network Psychometrics with R (Epskamp, Isvoranu, & Haslbeck, 2022) to demonstrate Gaussian Graphical Model estimation with psychonetrics. All items are rated on a 1--7 Likert scale. The dataset contains some missing values, making it suitable for demonstrating FIML estimation.

Usage

data("NA2020")

Format

A data frame with 501 observations on 8 variables.

regular_sleep

I try to keep a regular sleep pattern (Q10)

worried_sleep

I am worried about my current sleeping behavior (Q13)

sleep_interfere

My sleep interferes with my daily functioning (Q14)

happy_health

I am happy with my physical health (Q68)

optimistic_future

I feel optimistic about the future (Q70)

very_happy

I am very happy (Q75)

feel_alone

I often feel alone (Q77)

happy_love_life

I am happy with my love life (Q80)

References

Epskamp, S., Haslbeck, J. M. B., Isvoranu, A. M., & Van Borkulo, C. D. (2022). Pairwise Markov random fields. In A. M. Isvoranu, S. Epskamp, L. J. Waldorp, & D. Borsboom (Eds.), Network psychometrics with R: A guide for behavioral and social scientists (pp. 95--118). Routledge.

Examples

data(NA2020)

# \donttest{
# Estimate a GGM using FIML (as in Chapter 6):
library(dplyr)
mod <- ggm(NA2020, estimator = "FIML") %>% runmodel

# Prune non-significant edges:
mod_pruned <- mod %>% prune(alpha = 0.05)

# Inspect parameters:
mod_pruned %>% parameters
# }