advertisement

quantitative data analysis and interpretation Assoc. Prof. Dr. Abul Bashar Bhuiyan Data Analysis Methods Data analysis Research Methods ❑ Quantitative data analysis ❑ Qualitative data analysis Quantitative data analysis STEPS OF DATA ANALYSIS Conclusions and Recommendations Reliability and Validity Test Testing of HypothesisAccept/Reject Descriptive Analysis Econometric Analysis Compared findings with most available literature Summarized the findings Data Entry and Check QUANTITATIVE DATA ANALYSIS There are two main statistical methods are used in data analysis: DESCRIPTIVES ANALYSIS ECONOMETRIC ANALYSIS DESCRIPTIVES ANALYSIS Econometric Techniques: Econometric Techniques: Econometric is the use of statistical methods to develop theories or test existing hypotheses in economics or finance. Econometrics relies on techniques such as regression models and null hypothesis testing. Econometrics can also be used to try to forecast future economic or financial trends Most Popular Econometric Techniques Regression Analysis: Regression Analysis: Regression Analysis Measurement of Multiple Regression Analysis R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. R-squared measures the strength of the relationship between your model and the dependent variable on a convenient 0 – 100% scale. After fitting a linear regression model, you need to determine how well the model fits the data. Does it do a good job of explaining changes in the dependent variable? There are several key goodness-of-fit statistics for regression analysis. In this post, we’ll examine R-squared (R2 ), highlight some of its limitations, and discover some surprises. For instance, small R-squared values are not always a problem, and high R-squared values are not necessarily good! Adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases when the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected. Measurement of Multiple Regression Analysis ANOVA: ANOVA(Analysis of Variance) is a framework that forms the basis for tests of significance & provides knowledge about the levels of variability within a regression model. ... Whereas, ANOVA is used to predict a continuous outcome on the basis of one or more categorical predictor variables. Beta Coefficient : The beta coefficient is the degree of change in the outcome variable for every 1-unit of change in the predictor variable. ... If the beta coefficient is positive, the interpretation is that for every 1-unit increase in the predictor variable, the outcome variable will increase by the beta coefficient value. T Statistics: The t statistic is the coefficient divided by its standard error. P-Values: P-values and coefficients in regression analysis work together to tell you which relationships in your model are statistically significant and the nature of those relationships. The coefficients describe the mathematical relationship between each independent variable and the dependent variable. The p-values for the coefficients indicate whether these relationships are statistically significant. Tolerances and VIF: The variance inflation factor (VIF) and tolerance are two closely related statistics for diagnosing collinearity in multiple regression. They are based on the R-squared value obtained by regressing a predictor on all of the other predictors in the analysis. Tolerance is the reciprocal of VIF. (VIF accept less than 10 and tolerances accept less than 2) Data Entry and editing Data Editing: Editing – Review of the questionnaires with the objective of increasing accuracy and precision. It consists of screening questionnaires to identify; ➢ Illegible ➢ Incomplete ➢ Inconsistent Or Ambiguous Responses. Data Entry and editing 2 2 2 2 3 3 3 3 3 3 3 Practical Data Analysis Reliability Test Practical Data Analysis Reliability Test Practical Data Analysis Reliability Measurement Practical Data Analysis Reliability Result and Tabulation in Ms word Table 1.1: Reliability Result Statistics Variables DV IV1 IV2 IV3 IV4 Over All Crohn's back alpha 0.639 0.864 0.863 0.903 0.923 0.935 Practical Data Analysis SPSS Output SPSS Output Interpretation of data and report writing Sample of Demographic Results Table 4.1 Age Range of Respondents Item Age Range of Respondents Frequency Percent (%) 20–30 95 19.0 31–40 158 31.6 41–50 136 27.2 51–64 92 18.4 65–70 12 2.4 >71 7 1.4 Total 500 100.0 The data 4.1 show age of respondent, there are highest age group is about 31.6 % of age range 31-40 years old. The second highest about 27.2% of age group is 41-50 years old, age group 20-30 years contain 19%, next range of age group is 18.4% within 51-64 years old group. Finally, lowest age group respondent are 65 and 71 above where 3.8% respectively. Table 4.1 Gender of Respondents Gender Male Female Total Frequency Percent 34 44 78 43.6 56.4 100 Interpretation of data and report writing Table4.2 Sample of Descriptive Statistical Results DV Issues Observation Scale Proportio n of High Proportion of Low Average Value of Scale SD 4 and 5 (%) DVQ1 1 14.1 2 10.3 3 16.7 4 5* 28.2 1 and 2 (%) 30.8 3.51 1.393 59.0 24.4 DVQ2 10.5 7.7 14.1 33.3 32.1 3.71 1.294 65.4 18.2 DVQ3 9.0 14.1 16.7 33.3 26.9 3.55 1.276 60.3 23.1 DVQ4 6.4 15.4 42.3 35.9 4.01 1.051 50.9 21.8 DVQ5 15.4 7.7 11.5 39.7 15 24.4 3.51 1.363 64.1 23.1 DVQ6 12.8 6.4 24.4 33.3 21.8 3.45 1.273 55.1 19.2 Table 4.2 shows the status of (Put your DVQ1). The survey data were categorized based on the Observation Scale (Strongly disagree=1, Disagree=2, No change =3, Agree=4, and Strongly agree=5). In terms of (put your DVQ-1), 59% of the respondents said that they agreed with the question, whereas, 24.4 % said it strongly disagreed. Moreover, the average comment of respondent status recorded from the survey data was 3.51 and Standard Deviation score 1.393 respectively. Then Continue of writing DVQ-2-6 and complete your writing of descriptive statistics for table of DV Interpretation of data and report writing Sample of Descriptive Statistical Results Issues Observation Scale 1 2 3 Average Value of Scale SD 4 5* Proportio n of High Proportion of Low 4 and 5 (%) 1 and 2 (%) IV1Q1 IV1Q2 IV1Q3 IV1Q4 IV1Q5 Table 00 shows the status of (Put your IVQ1). The survey data were categorized based on the Observation Scale (Strongly disagree=1, Disagree=2, No change =3, Agree=4, and Strongly agree=5). In terms of (put your IVQ-1), 59% of the respondents said that they agreed with the question, whereas, 24.4 % said it strongly disagreed. Moreover, the average comment of respondent status recorded from the survey data was 3.51 and Standard Deviation score 1.393 respectively. Then Continue of writing IV1Q-2-5 and complete your writing of descriptive statistics for table of IV1 Practical Data Analysis Practical Data Analysis Practical Data Analysis Practical Data Analysis Interpretation of data and report writing Sample of Multiple Regression Result Table Model Summary Model 1 Std. Error Adjusted of the R Square R R Square R Square Estimate Change F Change a .900 .810 .800 .37445 .810 77.974 Change Statistics df1 df2 4 Sig. F Change 73 .000 Interpretation of data and report writing Table Coefficients Statistics Model 1 (Constant) IV1 IV2 IV3 IV4 Coefficientsa Standardiz Unstandardized ed Coefficients Coefficients B Std. Error Beta .762 .172 .345 .303 .071 .113 .059 .095 .073 .094 .439 .340 .084 .135 t Collinearity Statistics Tolerance VIF Sig. 4.439 .000 5.843 3.180 .968 1.205 .000 .002 .336 .232 .461 .227 .343 .208 Hypothesis Accept and Reject 2.170 Accepted 4.410 Accepted 2.911 Accepted 4.815 Accepted Sample Table for regression writing Model 1 R Square Adjusted R Square Sig. F Change (Constant) IV1 IV2 IV3 IV4 .810 Unstandardized Coefficients B Std. Error .762 .172 .345 .059 .303 .095 .071 .073 .113 .094 Coefficientsa Standardized Coefficients Beta .439 .340 .084 .135 t Collinearity Statistics Tolerance VIF Sig. 4.439 5.843 3.180 .968 1.205 .000 .000 .002 .336 .232 .461 .227 .343 .208 Hypothesis Accept and Reject 2.170 4.410 2.911 4.815 Accepted Accepted Accepted Accepted .800 .000 The summarized results in Table the overall estimated result of multiple regression analysis is satisfactory. This result is based on the cross-section primary data where the adjusted R² is .810 and the observed R² is.800 The adjusted R² revealed that the dependent and independent variables have a good relationship and all independent variables can explain about 80% of the present total monthly household income. The ANOVA table also reflected the goodness of model, and the F-test estimated that the regression is quite meaningful because the dependent variable is related to each specific explanatory variable. Interpretation of data and report writing No H1 H2 Statement of Hypothesis Based on Accept/Reject Power Prestige has significant positive effect on compulsive Beta> Sig>Tolerance and VIF Support buying. Beta> Sig>Tolerance and VIF Distrust has significant positive effect on compulsive buying. Support Accepted Rejected H3 Anxiety has significant positive effect on compulsive buying. Beta> Sig>Tolerance and VIF Support Accepted H4 Credit Card Usage moderates the effect of Power Prestige on Beta> Sig>Tolerance and VIF Support Compulsive Buying Credit Card Usage moderates the effect of distrust on Beta> Sig>Tolerance and VIF Support Compulsive Buying Credit Card Usage moderates the effect of Anxiety on Beta> Sig>Tolerance and VIF Support Compulsive Buying Accepted H5 H6 Rejected Accepted STEPS OF DATA ANALYSIS Conclusion and Recommendation Reliability and Validity Test Testing of HypothesisAccept/Reject Descriptive Analysis Econometric Analysis Compared with most available literature Summarized the findings Data Entry and Check