Evidence map›Paper›PMID 38046122›Full record

ArticleFrontiers in psychology2023

Measuring educational leadership in Singapore: re-examining the psychometric properties of the Multifactor Leadership Questionnaire.

John Chee Keng Wang, Woon Chia Liu, Ying Hwa Kee, Betsy Ng, Lilian Chua, Liang Hu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

John Chee Keng WangNational Institute of Education, Nanyang Technological University, Singapore, Singapore.
Woon Chia LiuNational Institute of Education, Nanyang Technological University, Singapore, Singapore.
Ying Hwa KeeNational Institute of Education, Nanyang Technological University, Singapore, Singapore.
Betsy NgNational Institute of Education, Nanyang Technological University, Singapore, Singapore.
Lilian ChuaNational Institute of Education, Nanyang Technological University, Singapore, Singapore.
Liang HuZhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The Multifactor Leadership Questionnaire (MLQ-5X) has been used frequently to assess leadership in different settings. Despite its popularity, there are many critiques of the MLQ-5X such as its questionable multidimensional structure, lack of connection to the theory and the different factor structures of the measurement tool. The purpose of this study was to re-examine the psychometric properties of the MLQ-5X in the Singapore educational context using two datasets. Methods: A total of 872 teachers (40.1% male and 59.9% female) from 20 secondary schools in Singapore completed two sets of MLQ-5X, one set for their immediate reporting officer and one set for their school leaders. Results: Congeneric Confirmatory Factor Analysis, Rho's coefficients, and AVE were used to analyze MLQ-5X's convergent validity and internal consistency. After five items were deleted, the MLQ-5X showed acceptable internal consistency and convergent validity. Eight measurement models were tested with the original 36 items and the reduced items MLQ-5X. Latent factor correlation matrix with confidence intervals was used to assess the discriminant validity of the MLQ-5X. The results provided support for a nine first-order factors and three second-order factors model (transformation [IIA, IIB, IM, IS, IC, CR], transactional (MBEA), and non-leadership (MBEP and LF). Discussion: The discriminant validity of the hierarchical measurement model of MLQ-5X is supported using dataset 2.

Indexed as

congeneric CFAconvergent validitydiscriminant validityMLQ-5Xtransformational-transactional leadership

Identifiers

PMID38046122
PMCPMC10690820

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.