Assessing Differences between Nested and Cross-Classified Hierarchical Models
Bibliographic Data
| ID | 6169527 |
|---|---|
| Authors | D Melamed (0000-0002-8821-7698, The Ohio State University, Columbus, OH, USA), M Vuolo (0000-0002-2916-0046, The Ohio State University, Columbus, OH, USA) |
| Year | 2019 |
| Volume | 49 |
| Issue | 1 |
| Pages | 220-257 |
| Publication date | 2019-07-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methodology (JOURNAL) |
| Journal identifiers | ISSN: 0081-1750 • E-ISSN: 1467-9531 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/0081175019862839 |
| OpenAlex | W2963945371 |
| Language | EN |
| Citations received | 3 |
| References cited | 21 |
In multilevel data, cross-classified data structures are common. For example, this occurs when individuals move to different regions in longitudinal data or students go to different secondary schools than their primary school peers. In both cases, the data structure is no longer fully nested. Estimating cross-classified multilevel models is computationally intensive, so researchers have used several shortcuts to decrease run time. We consider how these shortcuts affect parameter estimates. In particular, we compare parameter estimates from fully nested and cross-classified models using a series of Monte Carlo simulations. When the outcome is continuous, we identify systematic differences in estimated standard errors and some differences in the estimated variance components. When the outcome is binary, we also find differences in the estimated coefficients. Accordingly, we caution researchers to avoid fully nested model specifications when cross-classification exists but suggest some limited conditions under which parameter estimates are unlikely to be different
Data mining · Econometrics · Hierarchical database model · Monte Carlo method · Multilevel model · Nested set model · Outcome (game theory · Statistics · Variance (accounting · Variance components · Advanced Causal Inference Techniques · Computer Science · Mathematics · School Choice and Performance · Urban, Neighborhood, and Segregation Studies
Statistical Power Analysis for the Behavioral Sciences
Multilevel Analysis
Improved Approximations for Multilevel Models with Binary Responses
Fitting Linear Mixed-Effects Models Using lme4
Multilevel Statistical Models
The Impacts of Ignoring a Crossed Factor in Analyzing Cross-Classified Data
The Impact of Inappropriate Modeling of Cross-Classified Data Structures
Native Out-Migration and Neighborhood Immigration in New Destinations
Multiple contexts of exposure
Neighborhood context and racial/ethnic differences in young children's obesity
The role of local food availability in explaining obesity risk among young school-aged children
Neighborhood Immigration and Native Out-Migration
Neighborhoods and Schools as Competing and Reinforcing Contexts for Educational Attainment
Everybody's doin' it (right)
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,6 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |