Talk:Multilevel model

Proposed text

Klein et al. set the stage by citing pioneers in multilevel research, and essentially walking the reader through benefits, issues, and examples of current work in multilevel research. The authors stop short of identifying best practices; given the relative newness of multilevel research, perhaps there is insufficient depth of content to start presenting norms.

The guidelines for the development of multilevel theory by House et al, and Klein et al. cited in Klein (1999) calls for specification, explication, and flexibility of levels; it seems, however, that too much specificity may come at the cost of generalizability. The 1999 paper goes on to direct attention to other papers in the special issue which counter this impression, and bring flexibility to the field. Similarly, care is taken to point out the extremes of excessive organizational anthropomorphism, or excessive individual reductionism. These cautions are echoed in Klein & Kozlowski (2000), under the heading of ecological fallacy (generalizing findings from aggregated data back to the lower level at which it was collected) or atomist fallacy – extrapolating from a lower level of analysis to higher levels. In fact, Ostruff (1993) has shown that in spanning levels, results may be stronger or weaker, or even change direction.

Klein(2000) uses the effective tool of narrating a fictional multilevel study to walk the reader through the steps of choosing models, choosing sample methods (based on choice of model), and choosing analytical procedures (consistent with model and sampling choices). It is noted that data collection is a huge challenge, with at least 30 points of reference being preferred. Various models (single-level, cross-level, and homologous multilevel) are detailed, and validation of aggregation techniques: rwg index and Eta-squared to look at variance within groups, Within-and-Between Analysis (WABA) and two Intraclass correlations ICC(1) and ICC(2). Once data aggregation is essentially justified, the next challenge is analysis. Analytic tools, including WABA, Cross-level operator analysis (CLOP) and Hierarchical Linear Modeling (HLM) are presented, but covered superficially.

Singer(1998) takes us through a step-by-step use of SAS PROC MIXED function to illustrate regressing multilevel models. The paper is basically a tutorial in which school-effect (hierarchical) models and individual growth models, which are commonly used in multilevel research, are used as examples.

Finally, Liao and Chuang (2004) present a multilevel analysis of customer service, spanning employees, managers, and customers from 25 restaurants – a truly phenomenal data collection effort! This paper goes beyond explaining rationale or method, and provides an actual example of the use of multilevel research. The presentation is strong, including a parsimonious structural model, and a sequential presentation of results. Seeing the individual-level antecedent is interesting, then adding the context of store level contributors for service performance fulfills a goal of multilevel research: it provides depth and context, which overall allows more insight into the process.

Multilevel Research

Klein et al. set the stage by citing pioneers in multilevel research, and essentially walking the reader through benefits, issues, and examples of current work in multilevel research. The authors stop short of identifying best practices; given the relative newness of multilevel research, perhaps there is insufficient depth of content to start presenting norms.

The guidelines for the development of multilevel theory by House et al, and Klein et al. cited in Klein (1999) calls for specification, explication, and flexibility of levels; it seems, however, that too much specificity may come at the cost of generalizability. The 1999 paper goes on to direct attention to other papers in the special issue which counter this impression, and bring flexibility to the field. Similarly, care is taken to point out the extremes of excessive organizational anthropomorphism, or excessive individual reductionism. These cautions are echoed in Klein & Kozlowski (2000), under the heading of ecological fallacy (generalizing findings from aggregated data back to the lower level at which it was collected) or atomist fallacy – extrapolating from a lower level of analysis to higher levels. In fact, Ostruff (1993) has shown that in spanning levels, results may be stronger or weaker, or even change direction.

Klein(2000) uses the effective tool of narrating a fictional multilevel study to walk the reader through the steps of choosing models, choosing sample methods (based on choice of model), and choosing analytical procedures (consistent with model and sampling choices). It is noted that data collection is a huge challenge, with at least 30 points of reference being preferred. Various models (single-level, cross-level, and homologous multilevel) are detailed, and validation of aggregation techniques: rwg index and Eta-squared to look at variance within groups, Within-and-Between Analysis (WABA) and two Intraclass correlations ICC(1) and ICC(2). Once data aggregation is essentially justified, the next challenge is analysis. Analytic tools, including WABA, Cross-level operator analysis (CLOP) and Hierarchical Linear Modeling (HLM) are presented, but covered superficially.

Singer(1998) takes us through a step-by-step use of SAS PROC MIXED function to illustrate regressing multilevel models. The paper is basically a tutorial in which school-effect (hierarchical) models and individual growth models, which are commonly used in multilevel research, are used as examples.

Finally, Liao and Chuang (2004) present a multilevel analysis of customer service, spanning employees, managers, and customers from 25 restaurants – a truly phenomenal data collection effort! This paper goes beyond explaining rationale or method, and provides an actual example of the use of multilevel research. The presentation is strong, including a parsimonious structural model, and a sequential presentation of results. Seeing the individual-level antecedent is interesting, then adding the context of store level contributors for service performance fulfills a goal of multilevel research: it provides depth and context, which overall allows more insight into the process. —Preceding unsigned comment added by 72.231.170.111 (talk) 18:54, 25 April 2008 (UTC)

The above might be a useful addition if there were any details for the supposed references. Anyone any ideas? Melcombe (talk) 17:24, 19 February 2012 (UTC)

Mixed model

Mixed model redirects here, but this is incorrect. A multilevel model may be mixed, but not necessarily so. A mixed model is one in which some of the effects are considered to be fixed and others are considered to be random. Someone with a bit more statistical knowledge than I have could perhaps re-write the mixed-model article. --Crusio (talk) 09:56, 2 September 2008 (UTC)

This no longer points here, so problem solved Melcombe (talk) 17:19, 19 February 2012 (UTC)

I think "mixed model" is used inconsistently in different academic fields. This may be the source of the confusion. — Preceding unsigned comment added by 74.131.140.231 (talk) 18:41, 2 November 2012 (UTC)

History

This pages requires a bit more history - certainly the 1972 paper by Dennis Lindley and Adrian Smith "Bayes Estimates for the Linear Model" discusses hierarchy and hyperpriors and the paper by Stephen Raudenbush and Anthony S. Bryk, "A Hierarchical Model for Studying School Effects", are two older references, but it needs to be more thorough. Seanpor (talk) 08:44, 5 November 2014 (UTC)

 @article{raudenbush1986hierarchical,
   author = {Stephen Raudenbush and Anthony S. Bryk},
   title = {A Hierarchical Model for Studying School Effects},
   journal = {Sociology of education},
   volume = {59},
   number = {1},
   year = {1986},
   month = {January},
   pages = {1--17},
   publisher = {Americal Sociological Association}
 }
 @article{lindley1972bayes,
   author = {Denis V. Lindley and Adrian F. M. Smith},
   title = {Bayes Estimates for the Linear Model},
   journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
   volume = {34},
   number = {1},
   year = {1972},
   pages = {1--41}
 }

Linear vs. nonlinear MLM

This page is exclusively about linear MLM; either the title should be changed or it should be substantially expanded, or else a new page should be added. I'm new to editing Wikipedia so I am not sure which is best or what to do. PeterLFlomPhD (talk) 14:14, 20 July 2015 (UTC)

Problem with the example Suggestion

The example is, first, not simple. Second, it will involve interactions across levels, which are barely mentioned elsewhere. Should a different example be used? Or, perhaps, should more be added about cross-level interactions and then this complex example expanded? -- PeterLFlomPhD (talk) 21:11, 1 August 2015 (UTC)