Lauren A Trichtinger
Biographic Data
| ID | 99427 |
|---|---|
| NAME | Lauren A Trichtinger |
| GIVEN NAMES | Lauren A |
| FAMILY NAME | Trichtinger |
| SIGNATURE | TRICHTINGER L A |
| AFFILIATIONS | University of Notre Dame |
| ORCID | 0000-0002-4228-2793 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Identifying intersectional prospective predictors of suicidal thoughts and behaviors among sexual minority adults
In general, discrimination and distress predict suicidality in sexual minorities • Co-occurring psychosocial factors increase risk for suicidal thoughts and behaviors • Unique interactions of factors highlight the need for tailored interventions
Disentangling the Heterogeneity in Minority Stress
PolychoricRM
Many applications of structural equation modeling involve ordinal (e.g., Likert) variables. A popular way of dealing with ordinal variables is to estimate the model with polychoric correlations rather than Pearson correlations. Such an estimation also requires the asymptotic covariance matrix of polychoric correlations. It is computationally intensive to estimate polychoric correlations and their asymptotic covariance matrices. We describe a comp…
Testing P-Technique Factor Analysis With Non-Normal Time Series
There is an increasing need to analyze multivariate time series data due to the rapid development of data collection tools such as smartphone APPs, wearable sensors, and brain imaging techniques. P-technique factor analysis allows researchers to establish a measurement model for these time series. Analyzing such data is challenging because they are often non-normal (e.g., steps, heart rate, sleep, mood, and brain signals) and correlated at nearby…
Quantifying Model Error in P-technique Factor Analysis
P-technique factor analysis is an exploratory factor model for multivariate time series data. Assessing model fit of P-technique factor models is non-trivial because time series data are correlated at nearby time points. We present a test statistic that is appropriate for P-technique factor analysis. In addition, the test statistic allows researchers to quantify the amount of model error. We explore the statistical properties of the test statisti…
A Bootstrap Procedure for Testing P-Technique Factor Analysis
"A Bootstrap Procedure for Testing P-Technique Factor Analysis." Multivariate Behavioral Research, 56(1), p. 152
Overconfidence at the Keyboard
Gesture, voice, expression, and context add richness to communications that increase chances of accurate interpretation. E-mails lack much of this richness, leading readers to impose their own richness. Three experiments tested the effect of communicator relationship and availability of context on e-mail writer and reader confidence levels and accuracy. Effects of nonverbal and verbal behavior were also investigated. Results showed that confidenc…
Overconfidence at the Keyboard
Gesture, voice, expression, and context add richness to communications that increase chances of accurate interpretation. E-mails lack much of this richness, leading readers to impose their own richness. Three experiments tested the effect of communicator relationship and availability of context on e-mail writer and reader confidence levels and accuracy. Effects of nonverbal and verbal behavior were also investigated. Results showed that confidenc…
Overconfidence at the Keyboard
Gesture, voice, expression, and context add richness to communications that increase chances of accurate interpretation. E-mails lack much of this richness, leading readers to impose their own richness. Three experiments tested the effect of communicator relationship and availability of context on e-mail writer and reader confidence levels and accuracy. Effects of nonverbal and verbal behavior were also investigated. Results showed that confidenc…
Quantifying Model Error in P-technique Factor Analysis
P-technique factor analysis is an exploratory factor model for multivariate time series data. Assessing model fit of P-technique factor models is non-trivial because time series data are correlated at nearby time points. We present a test statistic that is appropriate for P-technique factor analysis. In addition, the test statistic allows researchers to quantify the amount of model error. We explore the statistical properties of the test statisti…
A Bootstrap Procedure for Testing P-Technique Factor Analysis
"A Bootstrap Procedure for Testing P-Technique Factor Analysis." Multivariate Behavioral Research, 56(1), p. 152
PolychoricRM
Many applications of structural equation modeling involve ordinal (e.g., Likert) variables. A popular way of dealing with ordinal variables is to estimate the model with polychoric correlations rather than Pearson correlations. Such an estimation also requires the asymptotic covariance matrix of polychoric correlations. It is computationally intensive to estimate polychoric correlations and their asymptotic covariance matrices. We describe a comp…
Testing P-Technique Factor Analysis With Non-Normal Time Series
There is an increasing need to analyze multivariate time series data due to the rapid development of data collection tools such as smartphone APPs, wearable sensors, and brain imaging techniques. P-technique factor analysis allows researchers to establish a measurement model for these time series. Analyzing such data is challenging because they are often non-normal (e.g., steps, heart rate, sleep, mood, and brain signals) and correlated at nearby…
Identifying intersectional prospective predictors of suicidal thoughts and behaviors among sexual minority adults
In general, discrimination and distress predict suicidality in sexual minorities • Co-occurring psychosocial factors increase risk for suicidal thoughts and behaviors • Unique interactions of factors highlight the need for tailored interventions
Disentangling the Heterogeneity in Minority Stress
Mathematics (4 works) · Statistics (4 works) · Statistical hypothesis testing (3 works) · Computer Science (2 works) · Distress (2 works) · Econometrics (2 works) · Inference (2 works) · LGBTQ Health, Identity, and Policy (2 works) · Multivariate analysis (2 works) · Psychometric Methodologies and Testing (2 works)