A Methodological Review on Time Series Panel Data

Authors

  • Mahima Oli

DOI:

https://doi.org/10.3126/rnjds.v6i1.58919

Keywords:

Cronbach's alpha, Hypothetico-deductive model, Lean six sigma method, Multiple regression analysis

Abstract

It is a general methodological review on Time Series Panel Data. The paper argues that timeseries panel data can be analyzed from more general to the more specific, which is known as a hypothetico-deductive model that includes seven main stages: problem, hypothesis, research design, measurement, data collection data analysis, and generalization. For the interpretation of data, the lean six sigma method can be used to integrate the six sigma concept (focusing on reducing defect products) and the lean concept (focusing on eliminating waste) as the methodology of cycle (DMAIC: define, measure, analyse, improve and control). Multiple regression analysis may suffer from multicollinearity or heteroscedastic, or autocorrelation problem. The variance inflation factors (VIF) is used to check multicollinearity that enabled us to eliminate the problem and select the “best” predictor variable to enter when other independent variables are present. Cronbach's alpha as an index's reliability is a metric used to assess the interval consistency of a set of items from zero (no internal consistency) to unity (perfect internal consistency). The review is applicable for the researchers who want to use time series panel data for the quantitative analysis. It is a general methodological review based on secondary information. To generalize the findings of Time Series Panel Data in the process of generalization, Cronbach's alpha, Hypothetico-deductive model, Lean six sigma method, and Multiple regression analysis are the basic significant statistical attributes.

Downloads

Download data is not yet available.
Abstract
69
PDF
56

Downloads

Published

2023-10-02

How to Cite

Oli, M. (2023). A Methodological Review on Time Series Panel Data. Research Nepal Journal of Development Studies, 6(1), 38–50. https://doi.org/10.3126/rnjds.v6i1.58919

Issue

Section

Articles