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Measuring leadership an assessment of the Multifactor Leadership Questionnaire.

ABSTRACT: Although the most used measure of transformational leadership, the Multifactor Leadership Questionnaire (MLQ), has been the subject of intense scrutiny among leadership scholars, little interest has been shown in analyzing the relationship between its underlying constructs and / or their measures. The present study identifies a formative factor structure for most MLQ first-order factors, replacing the usual reflective model. We demonstrate the value of this structure using data from two different samples. First, we applied the MLQ to a sample of 129 police officers from the Catalan Police workforce. Second, we ran an online survey with 300 US citizens. We argue that three second-order factors (transformational, transactional, and laissez faire) should be used as emergent aggregate multidimensional models to describe three different leadership styles, challenging the ubiquitous multidimensional latent models favored in the extant literature. We then propose that transformational/charismatic leadership should be treated as a multidimensional emergent profile model, replacing the leadership development order of precedence, which is dominant in modern leadership research.

SUBMITTER: Batista-Foguet JM

PROVIDER: S-EPMC8297756 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Measuring leadership an assessment of the Multifactor Leadership Questionnaire.

Batista-Foguet Joan Manuel JM Esteve Marc M van Witteloostuijn Arjen A

PloS one 20210722 7

Although the most used measure of transformational leadership, the Multifactor Leadership Questionnaire (MLQ), has been the subject of intense scrutiny among leadership scholars, little interest has been shown in analyzing the relationship between its underlying constructs and / or their measures. The present study identifies a formative factor structure for most MLQ first-order factors, replacing the usual reflective model. We demonstrate the value of this structure using data from two differen ...[more]

PMID: 34293007

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Project description:BackgroundOvarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.MethodsGene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.ResultsA total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.ConclusionThis study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

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Measuring leadership an assessment of the Multifactor Leadership Questionnaire.

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Measuring leadership an assessment of the Multifactor Leadership Questionnaire.

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