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Cited by 1,270

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Multilevel Statistical Models

Goulias, Konstadinos·2002·New Directions in Civil Engineering
DOI 10.1201/9781420042283.ch9
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Abstract

Recent travel demand forecasting systems and related data analyses target individual and household variations in behavior not only as functions of individual and household characteristics, but also as functions of other variables to capture the effect of social and geographical context on individual and household behavior. As discussed in previous chapters new conceptual and theoretical ideas in travel behavior are increasingly offered, providing analytical frameworks for human behavior in geographic space, social space, and time. To test hypotheses within these frameworks and to capture the relationships within and across different dimensions (levels), suitable data analytic techniques are needed. Assuming we are able to identify and clearly define levels of social groupings, such as the family, the neighborhood, or the professional group, our interest centers on explaining individual behavior not only as a function of personal motivational factors, but also as a function of group influence, such as task allocation(s) and role assignments within a group (e.g., the household). At a more macro (aggregate) level we are also interested in the role personal factors play in shaping group behavior(s). Techniques to accomplish this must support behavioral theories that aim to explain behavior using factors that influence behavior at the same level of the behavioral unit of analysis (named micro-to-micro relationships), at one level higher (more aggregated) from processes taking place (named macro-to-micro effects), and at one level lower from processes taking place (named micro-to-macro effects). Data analyses that include variables from different levels (e.g., a person, household, neighborhood, city, state) are inherently operating at multiple levels. These are called multilevel analyses because they examine the relationships among variables that are defined at different and multiple levels. Figure 9.1 provides an example of a hierarchy of this type. Each observation is a time point at which a person’s behavior has been recorded or reported (within the time dimension we can have another hierarchy of Konstadinos G. Goulias Pennsylvania State University

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Metadata

Authors
Goulias, K.
Venue
New Directions in Civil Engineering
Publisher
CRC Press
Published
2002-12-26
ISSN
2154-7319 (ISSN-L)
DOI
10.1201/9781420042283.ch9
OpenAlex
W2480193640
Cited by
1,270
References
2
FWCI
125.75
Type
book chapter
Language
en
Open access
closed

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