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.

“Statistics is the grammar of science.” 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:

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1: Hypothesis development

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

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

3: Generalizability (External Validity)

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)

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

“We must be careful not to confuse data with the abstractions we use to analyze them.” 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.