Stephanie T Lane
Biographic Data
| ID | 8867653 |
|---|---|
| NAME | Stephanie T Lane |
| GIVEN NAMES | Stephanie T |
| FAMILY NAME | Lane |
| SIGNATURE | LANE S T |
| AFFILIATIONS | University of North Carolina at Chapel Hill |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Initial validation of the trust of automated systems test (Toast)
Trust is a key determinant of whether people rely on automated systems in the military and the public. However, there is currently no standard for measuring trust in automated systems. In the present studies, we propose a scale to measure trust in automated systems that is grounded in current research and theory on trust formation, which we refer to as the Trust in Automated Systems Test (TOAST). We evaluated both the reliability of the scale str…
Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data
"Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data." Multivariate Behavioral Research, 52(1), pp. 126–127
Unsupervised Classification During Time-Series Model Building
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in tha…
The separation of between-person and within-person components of individual change over time
OBJECTIVE: Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful t…
No prominent works on this page.
The separation of between-person and within-person components of individual change over time
OBJECTIVE: Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful t…
Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data
"Evaluating the Use of the Automated Unified Structural Equation Model for Daily Diary Data." Multivariate Behavioral Research, 52(1), pp. 126–127
Unsupervised Classification During Time-Series Model Building
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in tha…
Initial validation of the trust of automated systems test (Toast)
Trust is a key determinant of whether people rely on automated systems in the military and the public. However, there is currently no standard for measuring trust in automated systems. In the present studies, we propose a scale to measure trust in automated systems that is grounded in current research and theory on trust formation, which we refer to as the Trust in Automated Systems Test (TOAST). We evaluated both the reliability of the scale str…
Computer Science (4 works) · Data mining (3 works) · Machine learning (3 works) · Mental Health Research Topics (3 works) · Artificial Intelligence (2 works) · Econometrics (2 works) · Mathematics (2 works) · Multivariate statistics (2 works) · Psychology (2 works) · Statistics (2 works)