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## Challenges Faced by PhD Students While Analyzing .pdf

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Challenges Faced by PhD Students While Analyzing Their
Quantitative Data
Statistical Data analysis in quantitative research generally involves varied statistical
techniques such as regression analysis, multivariate analysis, significance testing, and so on.
These can be efficiently performed by expert analysts having quantitative skills and extensive
knowledge in statistics. Data can statistically be inferred only after performing the
Quantitative Data Analysis.
You have to turn raw numbers into meaningful data in Quantitative Data Analysis by
applying critical and rational thinking. As same figure within a dataset may be interpreted in
different ways, it becomes vital to apply careful and fair judgment. Data analysis in
quantitative research must be performed by professionals having relevant experience and
skill.
&quot;Statistics is the grammar of science.&quot; Karl Pearson
Quantitative Research Methods for PhD Students - the Challenges
It is usual for dissertation committees to attack vigorously the way in which the results of a
study are analyzed. Not to mention that Statistical Data analysis in quantitative research
itself is intimidating and extremely difficult for PhD students.
There are four major challenges faced by PhD students/researchers while analyzing
Quantitative Data and they are discussed below:
1: Hypothesis development
o A hypothesis is where proposing an answer to a research question takes place.
There are two types of hypotheses, namely, a null hypothesis (this indicates no
effect or change) and an alternative hypothesis (this is usually an experimental
hypothesis). Hypothesis can never be proved or disproved; we can only get
evidence that either supports or contradicts it. Hypothesis consists of concepts
that have to be measured. Concepts need to be translated into measurable
factors and they need to be treated as variables.
2: Casualty: Cause and Impact
o This involves showing how things have come to be the way they presently are.
Variables must be identified for this as under:

Dependent variable: variable that is measured for finding the impact of
independent variable

Independent Variable: a variable deliberately manipulated by
researcher

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Control Variable: potential independent variable that is held constant
throughout the analysis

3: Generalizability (External Validity)
o This involves the extent to which a study’s results may be generalized beyond
the sample - the degree to which results may be extrapolated
4: Reliability (Internal Validity)
o This is concerned with repetition of the research for establishing its findings.
A reliable test must produce same results during successive trials.
Statistical coaches and consultants (or statisticians) help PhD students with Statistical Data
analysis in quantitative research in the following manner:

Careful review of data

Providing instructions on statistics

Developing an analysis strategy

Identifying the software and method for study

Implementing Quantitative Data Analysis
&quot;We must be careful not to confuse data with the abstractions we use to analyze
them.&quot; William James

Conclusion
Availing support for Statistical Data analysis in quantitative research helps PhD
students to overcome the challenges in analyzing their quantitative data effectively.
Moreover, these services adhere to ethical guidelines.