Climate change-Hung Gia Hoang

Published: 14 March 2019| Version 1 | DOI: 10.17632/4hzyn86n8f.1
Contributor:
Hung Hoang

Description

This is the data-set for my paper entitled: "Climate Change as Perceived by Central Vietnamese Smallholders: Causes, Indicators and Determinants" In this study, I used a cross-sectional survey research design. A stratified random sampling strategy was used to select 96 smallholders for interviews. A two-section structured questionnaire was developed to collect data. The first section consisted of statements on: (1) asking whether or not climate change has occurred in the district; (2) causes of climate change; and (3) indicators of climate change. In these statements, two answer choices including “yes” and “no” were given to measure the perceptions of respondents. The second section gathered demographic and socio-economic information.Data were analysed by using SPSS version 20. Descriptive statistics such as frequency percentages and means were used, and inferential statistics such as Chi squares test were applied. A binary logistic regression analysis was used to estimate the effect of the independent variables on the dependent variable. The dependent variable is a dummy variable which takes a value of 1 for climate change perceiver and 0 otherwise. The value of the model showed that the prediction using this logistic regression model is reasonable for decision making. The model chi-square (80.702, p <0.000) and level of model correct prediction (90.6%) were high, indicating that there is a statistically significant relationship between the sets of independent variables and smallholders’ perceptions of climate change. A strong association between smallholders’ perceptions of climate change and the explanatory variables is also described by Model Nagelkerke R2 equal to 0.785. Eight of the 13 independent variables of logistic coefficients were shown as statistically significant. These included: credit program participation, agricultural practice, ICT owned, household type, gender, age, farm size and training participation.

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