Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States methods
Show snapshot details
On this page
This experiment, in seven questions
Jump straight to the part of the recipe you need. Data and provenance labels stay close to the action they support.
Shopping and prep list
What do I need before I start?
Biological model pending
Subject model for the experiment.
- Use
- confirm full cohort details in the source paper
Before you run
What should be confirmed before execution?
First confirmation
Species or subject information is missing.
Confirm before execution
Protocol does not list structured equipment.
Confirm before execution
This page shows passages quoted from the paper. Equipment, materials, times, and temperatures are hidden until the page is rebuilt from the full paper and checked by a reviewer. Check the paper before using this method.
Confirm before execution
Open the source paper before finalizing run-specific details.
Procurement checkpoint
Use source-stated vendors where present. Treat mapped products as sourcing options unless the page marks an exact source match.
Open quote workflowStep-by-step procedure
What do I do, in order?
2.2 Measures
2. 2. 1. 2 Quantitative assessment. Regarding our selection of quantitative impacts to measure, we chose nine survey items based on information gathered from a review of previous research and new interview data. These nine survey items measured the following concepts: negative emotion states, preoccupation with COVID-19, feeling stressed, worry, and time demands.
2.2 Measures
Regarding the review of previous research, we examined studies of other large-scale disasters (i.e., the World Trade Center terrorist attacks on September 11, 2001; previous epidemics requiring quarantine), which are almost always associated with psychological impacts on the general population [ ]. These studies provided some guidance on what impacts to measure for the impacts of COVID-19 on college students.
2.2.2 Risk factors
Sociodemographic factors were self-reported and allowed identification of potential differences in impact levels by gender, age, race/ethnicity, socioeconomic status (SES), and academic status (undergraduate vs. graduate-seeking). SES was measured with perceived social class, which has been shown to accurately represent SES in student populations, using a battery of seven questions on class, parental education, and relative family income [, ]. To measure academic status, we asked respondents whether they were in pursuit of an undergraduate or graduate degree.
2.2.2 Risk factors
Another set of plausible lifestyle-related risk factors was time use. We utilized a recent recall question structure from the American Time Use Survey that strongly predicts objective time use and activity measures [ ]. Three items were used to ask respondents to indicate how many hours they spent outdoors (at a park, on a greenway/trail, in a neighborhood/yard, etc.), in front of a screen (on a smartphone/computer, watching television, online gaming, etc.), and engaged in moderate or vigorous physical activity that caused an increase in breathing or heart rate (fast walking, running, etc.) in the past 24 hours [, ].
2.3 Analyses
Last, using the composite scores from the EFA, we used the identified latent constructs from the psychological impact survey items as input variables in a latent profile analysis (LPA) [ ]. Criteria for determining the number of profiles in the LPA included statistical adequacy of the solution and interpretability of each profile [ ]. Indices used to determine statistical adequacy included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-size adjusted Bayesian Information Criterion [ ]. For each of these indices, lower values represented better model fit. Also, the entropy criterion was calculated as a measure of classification precision [ ]. We favored a parsimonious solution with fewer profiles over a more complex solution if this improved the interpretability of the LPA [ ]. Z-scores of the input variables were used to interpret the profiles....
2.3 Analyses
For the unadjusted results, risk factors were evaluated with chi-squared contingency tables. Residuals from observed versus expected count comparisons determined the direction of the effect of the risk factors (i.e., more or less likely that a group was classified to a higher impact profile than another profile). Statistical significance of risk factors was calculated with Bonferroni adjustments to reduce Type I Error [ ]. Continuous measures were reduced to dichotomous or categorical factors based on clinically meaningful levels, past research, and data distributions. BMI was classified into four categories (less than 18 = underweight; 18 to 24.99 = normal; 25 to 29.99 = overweight; 30.0 and over = obese) [ ]. General health was separated into two groups: poor/fair health and good/very good/excellent health [ ]. Screen time was separated into less than eight hours on a device and eig...
3.2 Psychological impact profiles
Mean values of the psychological impact survey items are shown in. Eight of these were included in the EFA. (Feeling guilty demonstrated low communality ( h 2 =.21) and was removed from further analyses.) All eight items displayed relatively normal distributions ( ). Criteria of the resulting model were acceptable: Tucker Lewis Index = 0.95; Kaiser-Meyer-Olkin (KMO) factor adequacy measure of sampling adequacy (MSA) =.89 [ ]; significant Bartlett's test of sphericity, χ 2 (28) = 10503, p <.001. The VSS Criterion [ ] achieved a maximum of.93 with a two-factor solution, compared to.89 for a one-factor solution or.94 for a three-factor solution (, and Figs). We labelled the first factor as "Emotional Distress" since it was composed largely of negative affect items (afraid, irritable, sad, preoccupied and stressed). The second factor was composed of three items dealing with h...
3.3 Risk factors
Sociodemographic (a), lifestyle (b), and COVID-19 victim awareness (c) risk factors associated with high, moderate, and low psychological impact profiles for students across the United States. Residuals from Pearson's chi-squared tests depict likelihood of profile membership based on risk factor. Only significant factors ( p <.05) are reported. Reference groups include men; over 32 age; other race/ethnicity; average/above average SES (social class and relative family income); good/very good/excellent general health; less than 2 hours of time outdoors; less than 8 hours of screen time; and not knowing someone infected (COVID-19).
Measurement outputs
What raw and processed outputs should exist?
Analysis plan
How should the outputs become interpretable results?
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
inferred from protocolPreprocessing / cleaning
Review raw outputs for quality, remove unusable captures, and organize the data into a comparison-ready table or image set.
inferred from protocolScoring or quantification
Quantify the primary endpoint described in the methods before moving into any statistical comparison.
inferred from protocolStatistical comparison
Statistical method not yet structured for this page.
inferred from protocolReporting output
Report representative outputs alongside the summary comparison used to support the conclusion.
inferred from protocolSource and audit
What supports the facts on this page?
Evidence quotes (8)
2. 2. 1. 2 Quantitative assessment. Regarding our selection of quantitative impacts to measure, we chose nine survey items based on information gathered from a review of previous research and new interview data. These nine survey items measured the following concepts: negative emotion states, preoccupation with COVID-19, feeling stressed, worry, and time demands.
Regarding the review of previous research, we examined studies of other large-scale disasters (i.e., the World Trade Center terrorist attacks on September 11, 2001; previous epidemics requiring quarantine), which are almost always associated with psychological impacts on the general population [ ]. These studies provided some guidance on what impacts to measure for the impacts of COVID-19 on college students.
Sociodemographic factors were self-reported and allowed identification of potential differences in impact levels by gender, age, race/ethnicity, socioeconomic status (SES), and academic status (undergraduate vs. graduate-seeking). SES was measured with perceived social class, which has been shown to accurately represent SES in student populations, using a battery of seven questions on class, parental education, and relative family income [, ]. To measure academic status, we asked respondents whether they were in pursuit of an undergraduate or graduate degree.
Another set of plausible lifestyle-related risk factors was time use. We utilized a recent recall question structure from the American Time Use Survey that strongly predicts objective time use and activity measures [ ]. Three items were used to ask respondents to indicate how many hours they spent outdoors (at a park, on a greenway/trail, in a neighborhood/yard, etc.), in front of a screen (on a smartphone/computer, watching television, online gaming, etc.), and engaged in moderate or vigorous physical activity that caused an increase in breathing or heart rate (fast walking, running, etc.) in the past 24 hours [, ].
Last, using the composite scores from the EFA, we used the identified latent constructs from the psychological impact survey items as input variables in a latent profile analysis (LPA) [ ]. Criteria for determining the number of profiles in the LPA included statistical adequacy of the solution and interpretability of each profile [ ]. Indices used to determine statistical adequacy included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-size adjusted Bayesian Information Criterion [ ]. For each of these indices, lower values represented better model fit. Also, the entropy criterion was calculated as a measure of classification precision [ ]. We favored a parsimonious solution with fewer profiles over a more complex solution if this improved the interpretability of the LPA [ ]. Z-scores of the input variables were used to interpret the profiles. The criteria to assign low and high values is not established and so we adopted previous studies' thresholds [ ]. These included standardized scores between +0.5 and -0.5 being labelled as moderate, scores above 0.5 being labelled as high, and scores below -0.5 being labelled as low levels of psycho...
For the unadjusted results, risk factors were evaluated with chi-squared contingency tables. Residuals from observed versus expected count comparisons determined the direction of the effect of the risk factors (i.e., more or less likely that a group was classified to a higher impact profile than another profile). Statistical significance of risk factors was calculated with Bonferroni adjustments to reduce Type I Error [ ]. Continuous measures were reduced to dichotomous or categorical factors based on clinically meaningful levels, past research, and data distributions. BMI was classified into four categories (less than 18 = underweight; 18 to 24.99 = normal; 25 to 29.99 = overweight; 30.0 and over = obese) [ ]. General health was separated into two groups: poor/fair health and good/very good/excellent health [ ]. Screen time was separated into less than eight hours on a device and eight or more hours on a device [ ]. Time outdoors was split into three groups: Less than 1.00 hour, 1.00 to 1.99 hours, and 2.00 hours or more [, ]. Time spent exercising was also split into three groups: 30.00 minutes or less, 30.01 to 59.99 minutes, and 1.00 hour or more [ ]. In addition, social cl...
Mean values of the psychological impact survey items are shown in. Eight of these were included in the EFA. (Feeling guilty demonstrated low communality ( h 2 =.21) and was removed from further analyses.) All eight items displayed relatively normal distributions ( ). Criteria of the resulting model were acceptable: Tucker Lewis Index = 0.95; Kaiser-Meyer-Olkin (KMO) factor adequacy measure of sampling adequacy (MSA) =.89 [ ]; significant Bartlett's test of sphericity, χ 2 (28) = 10503, p <.001. The VSS Criterion [ ] achieved a maximum of.93 with a two-factor solution, compared to.89 for a one-factor solution or.94 for a three-factor solution (, and Figs). We labelled the first factor as "Emotional Distress" since it was composed largely of negative affect items (afraid, irritable, sad, preoccupied and stressed). The second factor was composed of three items dealing with how time was spent presumably in worry during the pandemic (worry, too much time and a lot of time), and so we labelled it "Worry Time." This is a term from clinical psychology that describes time spent reflecting on all the possible impacts of a health concern, including those worries that an indivi...
Sociodemographic (a), lifestyle (b), and COVID-19 victim awareness (c) risk factors associated with high, moderate, and low psychological impact profiles for students across the United States. Residuals from Pearson's chi-squared tests depict likelihood of profile membership based on risk factor. Only significant factors ( p <.05) are reported. Reference groups include men; over 32 age; other race/ethnicity; average/above average SES (social class and relative family income); good/very good/excellent general health; less than 2 hours of time outdoors; less than 8 hours of screen time; and not knowing someone infected (COVID-19).
Machine-readable layer
[
{
"@context": "https://schema.org",
"@type": "HowTo",
"name": "Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States methods",
"description": "Protocol for Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States methods",
"step": [
{
"@type": "HowToStep",
"position": 1,
"name": "2.2 Measures",
"text": "2. 2. 1. 2 Quantitative assessment. Regarding our selection of quantitative impacts to measure, we chose nine survey items based on information gathered from a review of previous research and new interview data. These nine survey items measured the following concepts: negative emotion states, preoccupation with COVID-19, feeling stressed, worry, and time demands."
},
{
"@type": "HowToStep",
"position": 2,
"name": "2.2 Measures",
"text": "Regarding the review of previous research, we examined studies of other large-scale disasters (i.e., the World Trade Center terrorist attacks on September 11, 2001; previous epidemics requiring quarantine), which are almost always associated with psychological impacts on the general population [ ]. These studies provided some guidance on what impacts to measure for the impacts of COVID-19 on college students."
},
{
"@type": "HowToStep",
"position": 3,
"name": "2.2.2 Risk factors",
"text": "Sociodemographic factors were self-reported and allowed identification of potential differences in impact levels by gender, age, race/ethnicity, socioeconomic status (SES), and academic status (undergraduate vs. graduate-seeking). SES was measured with perceived social class, which has been shown to accurately represent SES in student populations, using a battery of seven questions on class, parental education, and relative family income [, ]. To measure academic status, we asked respondents whether they were in pursuit of an undergraduate or graduate degree."
},
{
"@type": "HowToStep",
"position": 4,
"name": "2.2.2 Risk factors",
"text": "Another set of plausible lifestyle-related risk factors was time use. We utilized a recent recall question structure from the American Time Use Survey that strongly predicts objective time use and activity measures [ ]. Three items were used to ask respondents to indicate how many hours they spent outdoors (at a park, on a greenway/trail, in a neighborhood/yard, etc.), in front of a screen (on a smartphone/computer, watching television, online gaming, etc.), and engaged in moderate or vigorous physical activity that caused an increase in breathing or heart rate (fast walking, running, etc.) in the past 24 hours [, ]."
},
{
"@type": "HowToStep",
"position": 5,
"name": "2.3 Analyses",
"text": "Last, using the composite scores from the EFA, we used the identified latent constructs from the psychological impact survey items as input variables in a latent profile analysis (LPA) [ ]. Criteria for determining the number of profiles in the LPA included statistical adequacy of the solution and interpretability of each profile [ ]. Indices used to determine statistical adequacy included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-size adjusted Bayesian Information Criterion [ ]. For each of these indices, lower values represented better model fit. Also, the entropy criterion was calculated as a measure of classification precision [ ]. We favored a parsimonious solution with fewer profiles over a more complex solution if this improved the interpretability of the LPA [ ]. Z-scores of the input variables were used to interpret the profiles...."
},
{
"@type": "HowToStep",
"position": 6,
"name": "2.3 Analyses",
"text": "For the unadjusted results, risk factors were evaluated with chi-squared contingency tables. Residuals from observed versus expected count comparisons determined the direction of the effect of the risk factors (i.e., more or less likely that a group was classified to a higher impact profile than another profile). Statistical significance of risk factors was calculated with Bonferroni adjustments to reduce Type I Error [ ]. Continuous measures were reduced to dichotomous or categorical factors based on clinically meaningful levels, past research, and data distributions. BMI was classified into four categories (less than 18 = underweight; 18 to 24.99 = normal; 25 to 29.99 = overweight; 30.0 and over = obese) [ ]. General health was separated into two groups: poor/fair health and good/very good/excellent health [ ]. Screen time was separated into less than eight hours on a device and eig..."
},
{
"@type": "HowToStep",
"position": 7,
"name": "3.2 Psychological impact profiles",
"text": "Mean values of the psychological impact survey items are shown in. Eight of these were included in the EFA. (Feeling guilty demonstrated low communality ( h 2 =.21) and was removed from further analyses.) All eight items displayed relatively normal distributions ( ). Criteria of the resulting model were acceptable: Tucker Lewis Index = 0.95; Kaiser-Meyer-Olkin (KMO) factor adequacy measure of sampling adequacy (MSA) =.89 [ ]; significant Bartlett's test of sphericity, χ 2 (28) = 10503, p <.001. The VSS Criterion [ ] achieved a maximum of.93 with a two-factor solution, compared to.89 for a one-factor solution or.94 for a three-factor solution (, and Figs). We labelled the first factor as \"Emotional Distress\" since it was composed largely of negative affect items (afraid, irritable, sad, preoccupied and stressed). The second factor was composed of three items dealing with h..."
},
{
"@type": "HowToStep",
"position": 8,
"name": "3.3 Risk factors",
"text": "Sociodemographic (a), lifestyle (b), and COVID-19 victim awareness (c) risk factors associated with high, moderate, and low psychological impact profiles for students across the United States. Residuals from Pearson's chi-squared tests depict likelihood of profile membership based on risk factor. Only significant factors ( p <.05) are reported. Reference groups include men; over 32 age; other race/ethnicity; average/above average SES (social class and relative family income); good/very good/excellent general health; less than 2 hours of time outdoors; less than 8 hours of screen time; and not knowing someone infected (COVID-19)."
}
],
"tool": [],
"supply": [],
"isBasedOn": {
"@type": "ScholarlyArticle",
"headline": "Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States",
"datePublished": "2021",
"author": [
{
"@type": "Person",
"name": "Matthew H. E. M. Browning"
},
{
"@type": "Person",
"name": "Lincoln R. Larson"
},
{
"@type": "Person",
"name": "Iryna Sharaievska"
},
{
"@type": "Person",
"name": "Alessandro Rigolon"
},
{
"@type": "Person",
"name": "Olivia McAnirlin"
},
{
"@type": "Person",
"name": "Lauren Mullenbach"
},
{
"@type": "Person",
"name": "Scott Cloutier"
},
{
"@type": "Person",
"name": "Tue M. Vu"
},
{
"@type": "Person",
"name": "Jennifer Thomsen"
},
{
"@type": "Person",
"name": "Nathan Reigner"
},
{
"@type": "Person",
"name": "Elizabeth Covelli Metcalf"
},
{
"@type": "Person",
"name": "Ashley D'Antonio"
},
{
"@type": "Person",
"name": "Marco Helbich"
},
{
"@type": "Person",
"name": "Gregory N. Bratman"
},
{
"@type": "Person",
"name": "Hector Olvera Alvarez"
},
{
"@type": "Person",
"name": "Christine M Wickens"
},
{
"@type": "Person",
"name": "Chung-Ying Lin"
},
{
"@type": "Person",
"name": "Chung-Ying Lin"
}
],
"identifier": "10.1371/journal.pone.0245327"
}
},
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Experiments",
"item": "https://replicatescience.com/experiments"
},
{
"@type": "ListItem",
"position": 2,
"name": "Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States methods",
"item": "https://replicatescience.com/experiments/psychological-impacts-from-covid-19-among-university-students-risk-factors-across-seven-states-in-the-united-states-methods-matthew-h-e-m-browning-pmc7790395/psychological-impacts-from-covid-19-among-university-students-risk-factors-across-seven-states-in-th-mlph6kj7"
}
]
}
]