ordinal logistic regression

Knowledge graph centered on ordinal logistic regression with 11 nodes and 19 connections. Top connected: multiple hierarchy stratification theory, socioeconomic status, outdoor recreation constraints, ordinal logistic regression, Mesa County.

Method synopsis

Uses ordinal logistic regression to model the probability of experiencing different levels of outdoor recreation constraints based on sociodemographic characteristics. Allows for multiple response categories rather than binary outcomes.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Do socio-cultural traits and other demographics affect outdoor recreation constraints? The case for Mesa County, Colorado (2018), Journal of Kinesiology & Wellness. Implementations in other papers (listed below) may differ.

1. Ordinal logistic regression analysis
   Each constraint served as a dependent variable regressed using ordinal logit regression to determine effects of demographic and personal characteristics. Two separate models were tested due to different phases of data collection - Model 1 without income but with Spanish-speaking variable, Model 2 with income but without Spanish-speaking variable
   Quantities: Model 1: N=580 respondents, 60 total coefficients tested. Model 2: N=91 respondents due to smaller sample with income dataDuration: Post-data collection analysis phaseConditions: Complete data required for regression analysis - no missing data points allowed
   Equipment: statistical software for ordinal logistic regression

Typical Equipment

• statistical software for ordinal logistic regression

Output Measurements

• constraint probability estimates
• odds ratios
• statistical significance levels

Papers Using This Protocol (1)

Places Where Used