Financial Choices of Millennial Working Professionals: A Dataset on Psychological Biases, Cultural Influences, and Financial Literacy

Description

This dataset contains survey data collected from 400 millennial working professionals in Bengaluru, Karnataka, India, to examine the behavioural, cultural, and financial-literacy factors associated with their financial choices. The study focuses on millennials aged 30–45 years and employs a quantitative, cross-sectional research design. Respondents were selected using purposive sampling, as participation was restricted to working individuals belonging to the millennial generation. The dataset comprises 21 variables, including a serial number, four demographic characteristics, and 16 measurement items representing four latent constructs: Psychological Biases (PB), Cultural Influences (CI), Financial Literacy (FL), and Financial Choices (FC). Each construct is measured using four items. Psychological Biases capture behavioural tendencies related to overconfidence, anchoring, loss aversion, and herding. Cultural Influences reflect family financial values, community norms, social expectations, and culturally embedded financial practices. Financial Literacy measures respondents' understanding and application of fundamental financial concepts, while Financial Choices encompass decisions related to saving and spending, investment, borrowing, and long-term financial planning. All construct items were measured using a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The demographic variables include gender, age group, job type, and investment experience. Age was classified into four categories: 30–33 years, 34–37 years, 38–41 years, and 42–45 years. Investment experience was categorised as up to 2 years, 3–5 years, 6–10 years, and above 10 years. The dataset was developed to investigate the direct effects of psychological biases, cultural influences, and financial literacy on financial choices, as well as the effects of psychological biases and cultural influences on financial literacy. It also enables the examination of the mediating role of financial literacy in the relationships between psychological biases and financial choices and between cultural influences and financial choices. The data are suitable for partial least squares structural equation modelling (PLS-SEM) and can also support reliability analysis, validity assessment, mediation analysis, descriptive statistics, and other multivariate statistical techniques. The accompanying code sheet provides the coding scheme for demographic variables and the complete measurement statements corresponding to the 16 construct items. This dataset may be useful to researchers studying behavioural finance, financial literacy, psychological biases, cultural influences, personal financial decision-making, and the financial behaviour of millennial working professionals.

Steps to reproduce

Steps to Reproduce the Analysis: This is open to anyone to use, but citing this source is mandatory The results of this study can be reproduced using the dataset and code sheet provided in this repository. The following steps should be followed: Download the Data.xlsx and Code Sheet.xlsx files and retain them in the same working folder. The dataset contains responses from 400 millennial working professionals in Bengaluru, Karnataka, India, while the code sheet provides variable definitions, demographic coding, construct codes, and measurement items. Review the code sheet to understand the coding structure of the demographic variables and the 16 measurement items representing four constructs: Cultural Influences (CI), Financial Choices (FC), Financial Literacy (FL), and Psychological Biases (PB). Import Data.xlsx into SmartPLS and specify the four latent constructs. Assign the corresponding indicators to each construct as follows: CI_1 to CI_4 for Cultural Influences, FC_1 to FC_4 for Financial Choices, FL_1 to FL_4 for Financial Literacy, and PB_1 to PB_4 for Psychological Biases. Construct the structural model by specifying the five direct relationships: PB → FC, CI → FC, FL → FC, PB → FL, and CI → FL. The two indirect relationships to be assessed are PB → FL → FC and CI → FL → FC, with Financial Literacy serving as the mediating construct. Run the PLS algorithm to assess the measurement and structural models. Evaluate indicator reliability using outer loadings; internal consistency using Cronbach's alpha, rho_A, and composite reliability; convergent validity using average variance extracted (AVE); and discriminant validity using the Fornell–Larcker criterion and heterotrait–monotrait ratio (HTMT). Perform bootstrapping to assess the statistical significance of the direct and indirect relationships. Report the path coefficients (β), standard errors, t-values, p-values, variance inflation factors (VIF), effect sizes (f²), and mediation results. Assess the model's explanatory power using R² and adjusted R² for the endogenous constructs, Financial Choices and Financial Literacy. Model-fit measures, including SRMR, d_ULS, d_G, chi-square, and NFI, may also be examined where applicable. Compare the reproduced results with those reported in the associated research paper. Minor numerical variations may occur depending on the SmartPLS version, bootstrapping settings, number of bootstrap subsamples, random seed, and software configuration used for the analysis.

Institutions

• Alliance University

  Karnataka, Bengaluru

Categories

Licence