مطالب

Second language (L2) researchers often rely on quantitative methods and measurement instruments like questionnaires and scales to explore latent constructs. They usually borrow sophisticated statistical techniques from disciplines like psychology and education to conduct quantitative analyses in L2 research. However, there are growing concerns about the inappropriate use and reporting practices of such statistical procedures like exploratory factor analysis (EFA). This study aimed to describe and evaluate the methodological issues in EFA research practices, examine the relationship between study features and outcomes, and improve future L2 research practices. This study presented the results of a methodological synthesis study on exploratory factor analysis use published in five universally reputable psychology-related journals since 2000. Specifically, the researchers identified 93 EFA studies and developed a coding scheme of key EFA considerations to analyze how EFA was used and reported in these articles. The article discussed the several results and provides essential recommendations for L2 researchers intending to employ EFA in their works.

Background: Rainfall variation is a clear indicator of climate change. IPCC in its multiple assessment reports has raised concerns over the changing climate and rising global average temperatures which could lead to widespread and catastrophic impacts on natural and human systems. The study of long-term patterns is crucial in establishing evidence of shifting climate and informing policy decisions.Objectives:This study examines the rainfall trend and variability of the semi-arid Agra district and assesses rainfall in the region. Annual rainfall for 101 years from 18 grid data points was statistically analysed for the same.Methodology: For the projection and analysis of data points, a Thiessen network polygon was drawn using QGIS 3.28.4. Each grid data point was assigned a Thiessen polygon. According to the area each Thiessen polygon covers, it was assigned weights. Then according to the weight of each polygon, annual rain in that area was calculated. Next, the data were tested for homogeneity and breakpoints using the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test. After this the trend of the data was identified using Mann-Kendall and the magnitude was calculated using Theil-Sen’s estimator. R-studio was used for all the statistical analysis and graph plotting.Results: Upon conducting the Standard Normal Homogeneity test, Buishand’s Range test, Buishand’s U test and Pettitt’s test it was found that the data was non-homogeneous with the breakpoint in the year 1967. The Mann-Kendall test revealed a declining trend in the annual rainfall and the Theil-Sen estimator calculated the magnitude of this declining trend to be -1.63 for the last century.Conclusion: The findings suggest that climate change is having a significant impact on rainfall in the semi-arid Agra district. The declining trend in rainfall could have several negative consequences for the region, including water scarcity, crop failure, and increased risk of droughts.