Objective

After the research question is formed, and after results are
collected; the next step would be to extract meaning out of them. This is done
through result analysis, where the research team examines the results from
different perspectives and in different ways, in order to confirm or deny any
underlining assumptions relating to the research topic. In other words, data
analysis is used to interrupt the data obtained throughout the research, and
use it to gain insight about the research topic.

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The Process

Data analysis is to done to prove through mathematical and
empirical means; the existence of a relationship between the dependent variable
and independent variable(s), and if such relation can’t be supported after the
statistical models are applied; then this affects the conclusion of the
research and it ultimately means that the research question and any assumptions
previously made, must be reexamined. This analysis would utilize several
methods and models of empirical and statistical nature; this is discussed in more
detail below.

Relationships

The research topic is concerned with the attitude of the workforce
towards automation, and whether they fear losing their jobs to it or not. Thus
the analysis would be done to establish a relation between the work force (as
function of the independent variables) and the fear of losing job to automation
(the dependent variable).  If the
underlining assumptions are true; then one would expect to see correlation
between (for example) skill level and fear of job loss to automation.

 

Methods of Analysis

The research team means to employ several statistical methods to
inspect the existence of a relation between the variables. These methods
include the following:

Descriptive statistics

Descriptive statistics are basic statistical tools and concept that
give a general and over the top view of the main characteristics of the sample
and what are the most distinct traits and possible causes of error. These tools
include but not limited to:

·        
Frequency
Distribution & Visual Representation (ie: Histograms)

Frequency
distribution is simply a way to arrange the data in and orderly fashion and
classify it according to the number of observations. Frequency distribution is
obtained through grouping data in an classes of adequate size and listing the
frequency of observation included in each class, and this method can summarize
data in an effective manner and allow for visual graphs to be used such as
Histogram. Lastly, it is worth noting that frequency distributions can be
applied to qualitative data sets and quantitative data as well

 Mean

The
expected value of a population is known as the Mean, it also refers to the
central tendency of a given data set. This value is obtained by dividing the
value sum over number the of observations, and it can be sometimes used as an
expression of the average of a data set. The mean is useful when dealing with
data set; for it can reveal what is the overall tendency in one value (relative
to the set), which can lead to finding any skewness present in the data.

·        
Standard
Deviation

The
term Standard Deviation is an expression used to indicate (in quantifiable
amounts) the amount of dispersion present in any given data set. This value
gives a measure of how close the points in the data set are to the expected
value or the mean.

·        
Contingency
Table

Contingency table
is sets of values presented in a tabular matrix that show the frequency
distribution for multiple variables. They are one common way that is used for
survey analysis. They give a big picture of all of the variables which allows
for comparison between the variables and reveals initial relation between them

 

Factor Analysis

In the field of statistics, one method which can be used to examine
the interdependence of variables is Factor Analysis. This method is used to
express the changeability of observed variables in terms of possibly fewer
hidden or latent variables. An example of that may be lowering the variables
from eight to four due to the fact the only four are essential and the rest
change as these essential variables change. This method is useful to examine
the degree of dependency of each variable on one another; which in turn allows
to researchers to focus on the most relevant variables to the topic.

Correlation Coefficient

Correlation Coefficient is a value used in statistics that indicate
how strong is the relation between two variables. This method can be a very
useful way to find out if there is any relation between the two variables and
if so how significant it is. This means if a change in variable happens; the
other variable would exhibits some sort of change as well tough that doesn’t
necessarily means that one causes the other. If the change is proportional;
than this means that it is positive correlation, and if it is disproportional;
then it is a negative correlation.

Bivariate analysis (Trend Analysis) & Multiple
Regressions

Trend Analysis or Bivariate analysis is a way that is used in
research, engineering and since; to analyze the relation between two variables
(one of which is dependent on the other). it is a useful way to examine how
strong of a relation the dependent variable has to the independent one. This is
expressed by the degree of accuracy of a prediction of the dependent variable
based of the independent variable within a specific range of the data set. The
relation is usually expressed in terms of a linear function Y=mX+b.

Bivariate analysis applies only for two variables. For more than
two variables; multiple regressions is used. Multiple regression is similar to
linear or Bivariate regression in the sense that it is used to examine the
relation between variables, but it can be used for multiple variables. Same
concept applies in term of how good is the relation, obtained from multiple regression,
in anticipating the value of dependent variable. It also can be used to see
which one of the independent variables can best predict the value of independent
variables, and this may lead to finding out which variable is the most
dominant. 

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