06:00
Longitudinal modeling in practice
Prof. Maria Tackett
Mar 20, 2024
Announcements
HW 04 due TODAY at 11:59pm
Project 02
- Draft report due Friday at noon
Introduce final project
Topics
Read Kudrnáčová and Kudrnáč (2023) as example of longitudinal modeling in practice
- Background and motivation
- Data collection and structure
- Exploratory data analysis
- Modeling
- Results in context and limitations
(Mostly) reproduce models from the paper
Note
Click here to access the article on Canvas.
Background and motivation
Read the abstract and answer the following questions:
- What is the primary motivation for the study? What gap in the existing research do the researchers hope to address?
- What outcomes are the researchers primarily interested in?
- What effects are the researchers primarily interested in?
- What is a key conclusion from their study ?
Data collection
- Data are from the Czech Household Panel Survey (CHPS)
- Data collected annually from 2015 - 2020 between June - October
- Two-stage stratified sampling used
- All members of a household were invited to participate
- Approached the same households for each wave (year)
- Respondents filled out a paper questionnaire
- Large reduction in response between 2018 and 2019 due to new requirements
Data structure
Multilevel structure with 3 levels
Level Three: Household
Level Two: Individual
- Level One: Wave
Response variables
Response variables are based on the “subjective component of integrative quality of life theory.”
Figure 1 in Kudrnáčová and Kudrnáč (2023)
Authors state that these factors are important for describing overall quality of life.
Outcomes of interest
We will focus on three of the outcomes:
Life satisfaction (
lfstat_OT): “represents a stable assessment of general feelings about life and indicates a long-term attitude”- Measured as response, ranging from 0 (extremely dissatisfied) to 10 (extremely satisfied), to the question “All things considered, how satisfied are you with your life as a whole?”
Wellbeing (
wellbe_OT): “captures a person’s emotional state and touches on their mental state”- Measured as average response, ranging from “at no time” to “all of the time”, to three items about how often in the last two weeks respondents “have been cheerful and in good spirits”, “have felt calm and relaxed”, and “have been active and vigorous”
Outcomes of interest
Happiness (
happy_OT): a person’s current positive emotional condition- Measured as response, ranging from 0 (extremely unhappy) to 10 (extremely happy), to the question “Taking all things together, how happy would you say you are?”
Sleep variables
Describe how each sleep variable is measured (page 6):
sleep duration (
SDweek_OT)quality of sleep (
slequal_OT)social jetlag (
jetlag_OT)
04:00
Other covariates
- sex
- highest level of education attained (basic and secondary vocational, secondary with maturita, tertiary education)
- household income (divided into 6 categories)
- age
- employment status (employed, self-employed, unemployed, student, retired, on maternity leave)
- number of children below age 5 in the household
- wave (2018, 2019, 2020)
Application exercise
Complete the Exploratory Data Analysis section.
08:00
Modeling
Analysis strategy
Read the Statistical analysis strategy on page 7.
- What type of random effects did the researchers include in their models?
- Describe the data wrangling and preparation.
- Describe the modeling building process.
05:00
Application exercise
Complete the Modeling section.
05:00
Results
See the list of hypotheses the researchers want to test on page 4 -5 .
What term(s) in the model can we use to test each hypothesis?
What are your conclusions about the effects of sleep on quality of life indicators?
04:00
Limitations
What are some potential limitations of this study?
References
Kudrnáčová, Michaela, and Aleš Kudrnáč. 2023. “Better Sleep, Better Life? Testing the Role of Sleep on Quality of Life.” Plos One 18 (3): e0282085.