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| RESEARCH ANALYTICS: A PRACTICAL APPROACH TO DATA ANALYSIS |
This book is specially designed to explain the basic fundamentals of analytics required for research projects. the book is extremely helpful for students who want to make their career in the area of analytics and data science. it not only covers the basic concepts of statistics required for data analytics but also explains advance topics of analytics such as structural equation modeling and panel data analytics in detail. the book also guides research students who need to conduct data analysis in their research projects.
contents : -
chapter 1: concept of research and data types in research analytics
1.1 research process
1.2 data and related concepts
1.3 types of data
1.4 data dictionary
1.5 statistical software for data analysis
1.6 summary
1.7 exercises
chapter 2: reliability and validity analysis
2.1 meaning of reliability and validity
2.2 reliability analysis
2.3 validity analysis
2.4 testing of construct validity with cfa using amos software
2.5 summary
2.6 exercises
chapter 3: data cleaning, imputation, and outlier testing
3.1 concept of data cleaning
3.2 data imputation (for replacing missing data)
3.3 outlier testing
3.4 summary
3.5 exercises
chapter 4: spss commands
4.1 data view and variable view
4.2 compute (using mathematical computations to create new scale variables)
4.3 select cases – filtering data
4.4 split file
4.5 sorting data
4.6 visual binning
4.7 recode variables
4.8 merge files
4.9 commands for import and export of data in spss
4.10 summary
4.11 exercises
chapter 5: descriptive statistics
5.1 frequency distributions
5.2 measures of central tendency
5.3 measures of dispersion
5.4 distribution analysis
5.5 summary
5.6 exercises
chapter 6: hypothesis testing: an introduction
6.1 hypothesis and its types
6.2 concept of hypothesis testing
6.3 decision rules for hypothesis testing
6.4 summary
6.5 exercises
chapter 7: test of difference
7.1 one-sample t-test
7.2 independent-samples t-test
7.3 paired-samples t-test
7.4 summary
7.5 exercises
chapter 8: analysis of variance – one-way anova
8.1 concept of anova
8.2 using anova over t-test
8.3 hypothesis testing in one-way anova
8.4 summary
8.5 exercises
chapter 9: non-parametric tests
9.1 chi-square test: the test of association
9.2 comparing two independent conditions: wilcoxon rank-sum test and mann-whitney test
9.3 kruskal-wallis test
9.4 friedman anova
9.5 summary
9.6 exercises
chapter 10: correlation analysis
10.1 concept of correlation analysis
10.2 covariance
10.3 non-parametric correlation
10.4 partial correlation
10.5 summary
10.6 exercises
chapter 11: regression analysis
11.1 concept of regression analysis
11.2 illustration of regression analysis
11.3 regression diagnostics
11.4 summary
11.5 exercises
chapter 12: web analytics: an introduction
12.1 concept of web analytics
12.2 summary
12.3 exercises
chapter 13: dummy regression models
13.1 regression with dummy (categorical) variables
13.2 dummy regression models as a substitute for one way anova
13.3 precautions while using dummy variables
13.4 summary
13.5 exercises
chapter 14: logistic regression model
14.1 linear probability models
14.2 logit or logistic regression model
14.3 summary
14.4 exercises
chapter 15: panel data analytics
15.1 an introduction to panel data
15.2 types of panel data regression models
15.3 random effect models
15.4 summary
15.5 exercises
chapter 16: structural equation modeling (sem)
16.1 constructs and items
16.2 confirmatory factor analysis (cfa)
16.3 structural equation modelling (sem)
16.4 introduction to the amos interface
16.5 summary
16.6 exercises
chapter 17: time series analytics
17.1 time series data
17.2 forecasting with arima modeling
17.3 example: forecasting of wpi series using the arima method in r
17.4 summary
17.5 exercises
chapter 18: exploratory factor analysis (efa)
18.1 concept and applications of efa
18.2 applying efa ? a case study
18.3 summary
18.4 exercises
Author : Ajay kumar chauhan
Publication : Dreamtech press
Isbn : 9789351198086
Store book number : 107
NRS 785.00
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