Top 5 Data Analysis Tools for a Perfect Thesis


These are the top 5 data analysis tools being used in paper helper's leading thesis writing services in Mumbai, Delhi, Bangalore and all over India.
Top 5 Data Analysis Tools for a Perfect Thesis

Studying data is an everyday chore of the researchers. Researchers depend heavily on data for solving their research problem at hand. Data analysis holds great importance in research as it makes the study of data a lot simpler and definitely more accurate. It helps the researchers to straightforwardly interpret the data so that nothing is left out in their research that can help them derive insights from it.


Data analysis in very simple words refers to the process of analyzing huge amount of data into different formats. Data these days are abundant and are available in different forms and can be extracted from different sources. It is with the help of data analysis that the researchers are able to clean, sort and transform the data into a consistent form so as to be effectively studied.


This blog attempts at explaining the top 5 data analysis tools generally used by the researchers to analyze data in their research.


1. ANOVA

ANOVA or Analysis of variance is an analytical tool used in statistics that divides an observed aggregate variability found in a data set into two parts, namely: systematic factors and random factors. While, the systematic factors have a statistical influence on the given data set; the random factors do not.


ANOVA test is used by the analysts/researchers to determine the influence of the independent variables on the dependent variables in a regression study. It allows the analysts/researchers to compare more than two groups simultaneously in order to determine if any relationship exists between them. The result of the ANOVA test, called the F statistic or F-ratio, allows analysis of multiple groups of data to determine the variability between and within samples.


Also Read: 3 Tips How to Write an Effective Title for Research Paper


2. Correlation

A correlational study aims at investigating relationships between variables without any control or manipulation of these variables on the part of the analysts/researchers. A correlation reflects the strength and/or direction of the relationship between two (or more variables). The direction of a correlation can be either positive or negative.


‘Positive correlation’ is when both the variables change in the same direction. As for example: when increase in height, leads to increase in weight. On the other hand, ‘negative correlation’ is when the variables change in the opposite directions. As for example: increase in coffee consumption, reduces tiredness. Apart from positive and negative correlation, a situation where there is no relationship between the variables is called ‘zero correlation’. For example, there is no relation between coffee consumption and increase in height.


Analysts/researchers generally use correlation in the following situations:

  • For investigating non-causal relationships

  • For exploring casual relationships between variables

  • For testing new measurement tools


3. Regression

Regression analysis is a group of statistical methods that are used for estimating the relationships that exists between a dependent variable and one or more independent variables. It is generally used to access the strength of relationship between the variables as well as for modelling the future relationship between them.


Regression analysis can be of multiple types like: linear regression, multiple regression and non-linear analysis. The linear regression and multiple regression are however, the most common types of regression used by analysts/researchers. The non-linear regression is mostly used for data sets of more complicated nature, wherein the dependent and independent variables reflect a non-linear relationship.

4. Factor Analysis (CFA & EFA)

A powerful technique of data reduction is the factor analysis. It enables the analysts/researchers to investigate the concepts that cannot be easily and directly measured. Reducing huge amount of variables into a few comprehensible underlying factors, factor analysis produces easily understandable actionable data. Application of this technique enables analysts/researchers to spot trends faster and visualize themes throughout their datasets, in turn enabling them to learn points in common in the datasets. Factor Analysis is thus, mostly used for identifying the relationship that exist between all the variables included in the dataset.


There are generally three types of factor analysis, namely the Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Construct Validity. However, amongst the three, the CFA & EFA are the most commonly used forms of factor analysis. Exploratory Factor Analysis or EFA is generally used when the analysts/researchers need to develop a hypothesis about the relationship between the variables. The Confirmatory Factor Analysis or CFA is used for testing the hypothesis about the relationship between the variables.

Thus, in a nutshell, factor analysis is a statistical technique that is used to reduce a huge number of variables into a fewer number of factors.

5. SEM

The Structural Equation Modeling or SEM is a multivariate technique of statistical analysis that is used by the analysts/researchers to analyze the structural relationships. SEM is a combination of the factor analysis and the multiple regression analysis that is used for analyzing the structural relationship between measured and latent variables.

This statistical analysis method is generally preferred by the researchers as it estimates the multiple and interrelated dependence in a single analysis. In this technique, two types of variables are used, namely: endogenous and exogenous variables. The endogenous variables are equivalent to dependent variables and equal to the independent variable.


Thus, this method though similar, but is more powerful than regression analysis, as it examines linear casual relationships among variables, while at the same time, accounting for measurement error.


 

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