README for MADATA Dataset: Data, data-analysis scripts, and research materials from the article: Which Leadership Style Do More Narcissistic Subordinates Prefer in Supervisors?


GENERAL INFORMATION
Title: Data, data-analysis scripts, and research materials from the article: Which Leadership Style Do More Narcissistic Subordinates Prefer in Supervisors?

Date: 12 January 2024

Creators: Jennifer Eck

Division: School of Social Sciences > Kulturvergleichende Sozial- und Persönlichkeitspsychologie (MZES) (Gebauer 2017-)

Keywords: Narcissism; Narcissistic Admiration; Narcissistic Rivalry; Leadership Styles; Similarity-Attraction Principle

Abstract: Background and Objective: Subordinates in Western cultures generally prefer supervisors with a democratic rather than autocratic leadership style. It is unclear, however, whether more narcissistic subordinates share or challenge this prodemocratic default attitude. On the one hand, more narcissistic individuals strive for power and thus may favor a democratic supervisor, who grants them power through participation. On the other hand, similarity attracts and, thus, more narcissistic subordinates may favor an autocratic supervisor, who exhibits the same leadership style that they would adopt in a leadership position.
Method: Four studies (Ntotal = 1,284) tested these competing hypotheses with two narcissism dimensions: admiration and rivalry. Participants indicated the leadership style they generally prefer in a supervisor (Study 1), rated their own supervisor’s leadership style (Study 2a: individual ratings; Study 2b: team ratings), and evaluated profiles of democratic and autocratic supervisors (Study 3).
Results: We found a significantly weaker prodemocratic default attitude among more narcissistic subordinates: Subordinates’ narcissism was negatively related to endorsement of democratic supervisors and positively related to endorsement of autocratic supervisors. Those relations were mostly driven by narcissistic rivalry rather than narcissistic admiration.
Conclusion: The results help clarify the narcissistic personality and, in particular, how more narcissistic subordinates prefer to be led.

DOI dataset on MADATA: https://doi.org/10.7801/427
License of all files on MADATA: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.en)

DOI journal article:  https://doi.org/10.1111/jopy.12950


FILES AND STRUCTURE ON MADATA

#Materials
NarcSub_Material-File.pdf: Contains the research materials used in the studies.

#Study 1
NarcSub_Study1_Data.csv: Data collected in Study 1.
NarcSub_Study1_Codebook.csv: Codebook for Study 1 data.
NarcSub_Study1_R-Script.R: R-script for data analysis in Study 1.

#Study 2a
NarcSub_Study2a_Data.csv: Data collected in Study 2a.
NarcSub_Study2a_Codebook.csv: Codebook for Study 2a data.
NarcSub_Studies2a-2b_R-Script.R: R-script for data analysis in Studies 2a and 2b.

#Study 2b
NarcSub_Study2b_Data.csv: Data collected in Study 2b.
NarcSub_Study2b_Codebook.csv: Codebook for Study 2b data.

#Study 3
NarcSub_Study3_Data.csv: Data collected in Study 3.
NarcSub_Study3_Codebook.csv: Codebook for Study 3 data.
NarcSub_Study3_R-Script.R: R-script for data analysis in Study 3.

#Online Supplement
NarcSub_OnlineSupplement_R-Script.R: R-script for the online supplement.
NarcSub_References_R-Packages.pdf: References for R-packages used in the analysis.

#Mplus Scripts and Outputs
NarcSub_Mplus-Scripts.zip: Archive containing Mplus-scripts used in the analysis.
NarcSub_Mplus-Outputs.zip: Archive containing Mplus-outputs from the analysis.

#Usage Notes
The data files are in CSV format and can be opened with any text editor or spreadsheet software (e.g., Microsoft Excel, Google Sheets) or data analysis tools (e.g., R/RStudio, STATA).
R-scripts can be executed in R or RStudio (https://rstudio-education.github.io/hopr/starting.html).
Mplus-scripts require Mplus software (https://www.statmodel.com/index.shtml) for execution.
Ensure that you have the necessary R-packages installed to run the R-scripts. Refer to NarcSub_References_R-Packages.pdf for the list of required packages.

#Methodology
Data Collection
Data were collected online. In Studies 1 and 3, we used Amazon Mechanical Turk (MTurk, https://www.mturk.com). In Studies 2a-b, we used a snowballing procedure (a student assistant distributed the study invitation via organizational contacts and social networks, such as Facebook and Xing, and we asked participants to invite other members of their organizational team who had the same supervisor to take part in the study).
The participation requirements and exclusion criteria for each study can be found below:
Study 1 + Study 3, Participation Requirements: US resident; approval rating for past MTurk work >95%. Exclusion Criteria: Participants reporting that they did not take their participation seriously.
Study 2a, Participation Requirements: Participant has been working with their supervisor for at least 1 year
Study 2b, Participation Requirements: Participant has been working with a supervisor, but to maximise sample size we did not specify the minimum duration; has at least two colleagues who had been working with the same supervisor and were willing to take part in the study. 

Data Analysis
Analysis was performed using R/RStudio and Mplus. The provided scripts include all the necessary steps to replicate the analyses reported in the article.


CONTACT INFORMATION
For any questions or issues regarding the dataset, please contact jennifer.eck@uni-mannheim.de.