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.
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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