### 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 language | English |
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Pages (from-to) | 606-617 |

Number of pages | 12 |

Journal | European Management Journal |

Volume | 34 |

Issue number | 6 |

DOIs | |

Publication status | Published - 1 Dec 2016 |

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### 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.

Research output: Contribution to journal › Article

*European Management Journal*, vol. 34, no. 6, pp. 606-617. https://doi.org/10.1016/j.emj.2016.06.009

}

TY - JOUR

T1 - Accounting for sampling weights in PLS path modeling

T2 - Simulations and empirical examples

AU - Becker, Jan Michael

AU - Ismail, Ida Rosnita

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

KW - Job satisfaction

KW - Organizational commitment

KW - PLS path modeling

KW - Post-stratification weights

KW - Sampling weights

KW - Simulation

KW - Weighted PLS (WPLS)

UR - http://www.scopus.com/inward/record.url?scp=84994899464&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994899464&partnerID=8YFLogxK

U2 - 10.1016/j.emj.2016.06.009

DO - 10.1016/j.emj.2016.06.009

M3 - Article

VL - 34

SP - 606

EP - 617

JO - European Management Journal

JF - European Management Journal

SN - 0263-2373

IS - 6

ER -