Modern Methods for Business Research

Regular price €179.80
advanced data modeling for business research
analysis
Block Toeplitz Matrix
Category=KJQ
Category=KJT
class
Composite Heuristic
correlation
DEA
DEA Formulation
DEA Model
decision science applications
DFA
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
equation
factor analysis methods
Graded Response Model
IRT Model
latent
Latent Class
Latent Class Analysis
Latent Class Models
Latent Class Probabilities
Latent Growth Models
Latent Trait
Latent Variable
latent variable modeling
matrix
Mixed Rasch Model
modeling
Multifacet Rasch Model
multilevel statistical analysis
Multilevel Structural Equation Modeling
Nominal Response Model
parameter
Partial Credit Model
PLS Approach
PLS Procedure
quantitative research techniques
Rasch measurement models
Raw Data
structural
Tabu Size
Time Series
variables

Product details

  • ISBN 9780805826777
  • Weight: 990g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Mar 1998
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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This volume introduces the latest popular methods for conducting business research. The goal of each chapter author--a leading authority in a particular subject area--is to provide an understanding of each method with a minimum of mathematical derivations. The chapters are organized within three general interrelated topics--Measurement, Decision Analysis, and Modeling.

The chapters on measurement discuss generalizability theory, latent trait and latent class models, and multi-faceted Rasch modeling. The chapters on decision analysis feature applied location theory models, data envelopment analysis, and heuristic search procedures. The chapters on modeling examine exploratory and confirmatory factor analysis, dynamic factor analysis, partial least squares and structural equation modeling, multilevel data analysis, modeling of longitudinal data by latent growth curve methods and structures, and configural models of longitudinal categorical data.