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
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3
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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