Accounting for sampling weights in PLS path modeling

Simulations and empirical examples

Jan Michael Becker, Ida Rosnita Ismail

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Applications of partial least squares (PLS) path modeling usually focus on survey responses in management, social science, and market research studies, with researchers using their collected samples to estimate population parameters. For this purpose, the sample must represent the population. However, population members are often not equally likely to be included in the sample, which indicates that sampling units have different probabilities of being selected. Hence, sampling (post-stratification) weights should be used to obtain consistent estimates when estimating population parameters. We discuss alterations to the basic PLS path modeling algorithm to consider sampling weights in order to achieve better average population estimates in situations where researchers have a set of appropriate weights. We illustrate the effectiveness and usefulness of the approach with simulations and an empirical example of a job attitude model, using data from Ireland.

Original languageEnglish
Pages (from-to)606-617
Number of pages12
JournalEuropean Management Journal
Volume34
Issue number6
DOIs
Publication statusPublished - 1 Dec 2016

Fingerprint

Partial least squares
Sampling
Modeling and simulation
Modeling
Usefulness
Market research
Ireland
Simulation
Job attitudes
Social sciences

Keywords

  • Job satisfaction
  • Organizational commitment
  • PLS path modeling
  • Post-stratification weights
  • Sampling weights
  • Simulation
  • Weighted PLS (WPLS)

ASJC Scopus subject areas

  • Strategy and Management

Cite this

Accounting for sampling weights in PLS path modeling : Simulations and empirical examples. / Becker, Jan Michael; Ismail, Ida Rosnita.

In: European Management Journal, Vol. 34, No. 6, 01.12.2016, p. 606-617.

Research output: Contribution to journalArticle

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