Introduction to Multilevel Modeling Techniques Amira. El -Deosokey

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Introduction to Multilevel Modeling

Techniques

Amira. El-Deosokey

Assistant Professor, Higher Future Institute for specialized technological

Studies, Egypt.

Abstract

In this paper, I outline several conceptual and

methodological issues related to modeling individual

and group processes embedded in clustered/hierarchical

data structures. We position multilevel modeling

techniques within a broader set of univariate and

multivariate methods commonly used to study different

types of data structures. We then illustrate how the

choice of analysis method affects how best to examine

the data. This overview gives us an idea of our further

development of these themes and models in this study.

Introduction

Over the past several decades, concerns in various fields

with conceptual and methodological issues in

conducting research with hierarchical (or nested) data

have led to the development of multilevel modeling

techniques. Research on organizations such as

universities or product and service firms presents

opportunities to study phenomena in hierarchical

settings. Individuals (Level 1) may work within specific

formally defined departments (Level 2), which may be

found within larger organizations (Level 3), which, in

turn, may be located within specific states, regions, or

nations. These individuals interact with their social

contexts in a variety of ways. Individuals bring certain

skills and attitudes to the workplace; they are clustered

in departments or work units having certain

characteristics, and they are also clustered within

organizations having particular characteristics. Because

of the presence of these successive groupings in

hierarchical data, individuals within particular

organizations may share certain properties including

socialization patterns, traditions, attitudes, and work

goals. Similarly, properties of groups (e.g., leadership

patterns, improvement in productivity) may also be

influenced by the people in them. Hierarchical data also

result from the specific research design and the nature of

the data collected. In survey research, for example,

individuals are often selected to participate in a study

from some type of stratified random sampling design

(e.g., individuals may be chosen from certain

neighborhoods in particular cities and geographical

areas). Longitudinal data collection also presents

another research situation where a series of

measurements is nested within the individuals who

participate in the study.

In contrast, disaggregation refers to moving a variable

conceptualized at a higher level to a lower level. For

example, in a different analysis we may have

productivity measured at the organizational level but also

have items that express individual employee attitudes

and motivation. In this case, we intend to analyze the data

at the individual level to see whether employee attitudes

influence productivity. If we assign to all individuals the

same value on the organizational productivity variable

(and possibly other organizational variables such as

size), we attribute properties of the organization to

individuals. This can also confound the analysis.

MACRO

LEVEL

Context Composition

Structure

Resources

What contextual, structural, compositional, and resource variables affect organizational

productivity?

Organizational

Productivity

---------------------------------------------------------------------------------------------------------------------

How do structural characteristics, compositional variables, and teamwork

affect departmental productivity?

-----------------------------------------------------------------------------------------------------------------------

MACRO LEVEL

Departmental

Productivity

Individual

Productivity

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IntroductiontoMultilevelModelingTechniquesAmira.El-DeosokeyAssistantProfessor,HigherFutureInstituteforspecializedtechnologicalStudies,Egypt.AbstractInthispaper,Ioutlineseveralconceptualandmethodologicalissuesrelatedtomodelingindividualandgroupprocessesembeddedinclustered/hierarchicaldatastructures.W...

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Introduction to Multilevel Modeling Techniques Amira. El -Deosokey

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