Free Essay Sample on Data Analysis Concepts

Published: 2022-10-21
Free Essay Sample on Data Analysis Concepts
Type of paper:  Research paper
Categories:  Data analysis
Pages: 2
Wordcount: 479 words
4 min read

Concept One - Research Question

A research question is a vital element in the anchorage of the research process. A good research question is relevant, is of interest, crosses not ethical boundaries, and is testable. The research questions are three types namely descriptive, relational, and causal. I have learned that descriptive question involves a description of existing characteristics, conditions, and happenings. Its analysis is based on the researcher's observational skills in the hope of finding something never described before. An example is public opinion polls (Kumar, 2011).

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Relational question involves examining if there is a relationship between two or more existing variables under given circumstances. The variables in question are currently in the population. An example of a relational question is if two goats are given the same amount of feed and live under similar conditions, will they gain the same weight amount? Seemingly, causal research question involves determining cause and effect if there are changes in one variable in a known habitat and its effect on the other variables in the same known habitat. An example of a causal question is, does a change in seasons affect the migration of animals in the United States? (Polgar & Thomas, 2011).

Concept Two - Data Measurement

Data is recorded material which is acceptable and stored to validate findings in a research. Types of data or measurement scales are used in categorizing different variables. Types of data include nominal, ordinal, interval and ratio. Nominal scale involves data with no quantitative value with no order but is given labels or names in each category. The variables in this data are mutually exclusive but have no numerical significance. An example is a dichotomous scale involving two variables such as male and female gender. Seemingly, ordinal data has significant order in the variables, but their differences in measurements are meaningless. I have observed that ordinal scale measure non-numeric concepts such as discomfort, satisfaction, and happiness (Agresti, 2018).

Interval scale involves data with numeric in which order and meaningful measurement intervals are known. With interval data, more statistics such as central tendency by mean, median and mode can be calculated. An example of an interval scale is Celsius temperature. However, the interval scale has no true starting point, that is, no true zero. Seemingly, ratio data type elaborates on the order, exact measurement intervals with an absolute starting point of zero. I have learned that ratio data is useful in descriptive and inferential statistics while providing reliable information on statistical analysis. Examples of ratio data are weight and height (Beatty, 2018).


Agresti, A. (2018). An introduction to categorical data analysis. Wiley.

Beatty, W. (2018). Four Levels (Scales) of Data Measurement. In Decision Support Using Nonparametric Statistics (pp. 17-22). Springer, Cham.

Kumar, R. (2011). Research Methodology (ed.).

Polgar, S., & Thomas, S. A. (2011). Introduction to Research in the Health Sciences E-Book. Elsevier Health Sciences.

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