Using Spatial Autocorrelation Analysis to Guide Mixed Methods Survey Sample Design Decisions
Bibliographic Data
| ID | 12811925 |
|---|---|
| Authors | Timothy T Brown (0000-0002-5334-0768, The University of Texas at Dallas), Jennifer Wood (0000-0002-0794-4288, Temple University, corresponding author), Daniel A Griffith (0000-0001-5125-6450, The University of Texas at Dallas) |
| Year | 2015 |
| Volume | 11 |
| Issue | 3 |
| Pages | 394-414 |
| Publication date | 2015-12-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Mixed Methods Research (JOURNAL) |
| Journal identifiers | ISSN: 1558-6898 • E-ISSN: 1558-6901 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1558689815621438 |
| OpenAlex | W2396994780 |
| Language | EN |
| Citations received | 5 |
| References cited | 40 |
Mixed methods researchers share a commitment to knowing their sampling frames and minimizing discovery failure, especially when using surveys. Notwithstanding advances in sampling strategies, the geographic clustering of perceptions has not been fully considered for its relevance to sampling. This article examines the value of spatial autocorrelation analysis to guide sampling decisions. Spatial autocorrelation refers to the clustering of (dis)similar phenomena and signals the likely existence of perception subgroups. Through a spatial autocorrelation analysis of Dallas, Texas, the authors identify sampling frames for collecting data about perceptions of West Nile Virus eradication measures. They furnish some empirical confirmation of the geographic clustering of perceptions and argue for designs that identify perception clustering, which can affect qualitative sampling as well as advance the integration of quantitative and qualitative research
Autocorrelation · Cluster analysis · Data mining · Data science · Econometrics · Geography · Perception · Relevance (law · Sample (material · Sampling (signal processing · Sampling design · Sampling frame · Sociology · Spatial analysis · Statistics · Computer Science · Health Policy Implementation Science · HIV, Drug Use, Sexual Risk · Mathematics · Psychology · Survey Methodology and Nonresponse · Artificial Intelligence
Social Area Analysis
Sage Handbook of Mixed Methods in Social & Behavioral Research
Analyzing Complex Survey Data
Best Practices for Mixed Methods Research in Health Sciences
Mixed Method Designs in Implementation Research
Qualitative research sample design and sample size
The Hotspot Matrix
Syringe access, syringe sharing, and police encounters among people who inject drugs in New York City
Mixed Methods Sampling
Caqdas-GIS Convergence
The 1 + 1 = 3 Integration Challenge
The Merits of Mixing Methods in Evaluation
Assessing the Geographic Coverage and Spatial Clustering of Illicit Drug Users Recruited through Respondent-Driven Sampling in New York City
Cities as Systems Within Systems of Cities
Mixed-Methods Research Methodologies
Dynamic models of segregation
The Nature of Cities
How ideological migration geographically segregates groups
Establishing Qualitative Geographic Sample Size in the Presence of Spatial Autocorrelation
Local Indicators of Spatial Association—Lisa
Designing and Conducting Mixed Methods Research
Mixed Methods Research
Hot Spots of Predatory Crime
WITHDRAWN - Mixed Methods Sampling
Approaches to sampling and case selection in qualitative research
A Computer Movie Simulating Urban Growth in the Detroit Region
Factorial Ecology
Introduction
Respondent-Driven Sampling
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2016 - 2025 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |