Variable Questions and Data Privacy

Variables must vary

Variables are essential part of quantitative research, thus understanding fundamental articulation on variables such as different types of variables, operationalization and measurement and, scales of variables underpin in an effective platform for variables comprehension. In particularly, “variables must vary” posit a significant implications in variables understanding , accommodating fundamental variability features in a quantitative research and comprehension of variables as essential part in a quantitative research. This statement (“variables must vary.”) implies that different units, or participants in a must indicate differences in the variable. For example in one study, gender may be variable demonstrating difference unit or participant. Significantly, one study, focusing on married women will great gender as a non-variable in a study, while married women form a significant variable for the study. Therefore, the statements (“variables must vary.”) are fundamental, demonstrating that different units and participants must indicate different variable in a research study.

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Levels of variables measurement

Significantly, understanding the operationalization and measurement of variables are essential strategies for effective data quantification and decision-making in a research study. Scales of variables measurement is fundamental because it provide effective articulation on the relationship between variables and conclusion in a research study for practical implications and conclusion. In a comprehensive understanding of the variables, there are four different level of variables measurement that requires effective articulation and description to effectively operationalize and measure variables in a consistent and accurate manner in a research study. Notably, variable measurement levels including nominal measurement, ordinal level, interval and ratio variables. Nominal level of variable measurement describes one fundamental basic measure that contains two or more mutually inclusive and exclusive categories that cannot be ordered. For instance, a list of states in India Himachal Pradesh, Uttaranchal, Maharashtra are nominal variables that require specific rile for effective ordering and ranking. Accommodating rules such as alphabetical order would provide an effective platform for data ordering and ranking States in India in alphabetical order. Similarly, gender, accommodating male, female and third gender is vital examples of nominal variables measurement.

Consequently, ordinal variable describes level of variable measurements in which two or more categories like in nominal measurement of variables. However, ordinal variable can be ranked, accommodating the ranking capabilities in differentiating categories in such variables. Noteworthy, ranking in ordinal variables measurements level do not contain numerical value; hence can only be measure in terms of greater or lesser than. For example asking a student who often they read in day would provide significant example of ordinal variables measurement levels. For instance, frequently, sometimes, yes or no are vital examples of ordinal measurement of variables. Interval is vital level of data measurements that accommodates significant articulation on numerical value and continuum measurement that requires effective articulation. Specifically, Interval variables are variables that have a numerical value, and are measured on a continuum, accommodating equal interval between values and items. For instance a temperature measured in Celsius or Fahrenheit is a vital example of interval level of variable measurements. For instance, the difference between 20 degrees Celsius and 30 degrees Celsius is equal to the different between 30 degrees Celsius and 40 degrees Celsius. Similarly, the example, 1 dollar to 2 dollars is the same interval as 88 dollars to 89 dollars gives another significant example in interval level of variable measurements. Finally, the ratio variables measures numerical value and continuum, however the ratio values are in absolute zero. This zero on the measurements scale indicating no value of the variable or the unit or items measured is absent at level zero. Some significant examples of ratio variables level of measurement include height, weight, currency, mass, among others. Thus, scales of measurement of variables are nominal, ordinal, interval and ratio, accommodating categories, numerical value, and order among other issues in variables instances of variable measurement level.

Discreet and Continuous Variables

Understanding data accommodates quantitative and qualitative data in a study. In statistical analysis, accommodate quantitative data provide significant attention to discrete and continuous variables that posits fundamental characteristics. Discrete variables are variable assuming a finite number of isolated values. In contrast, Continuous variables define variables that assume infinite number of different values. According to the author, understanding discrete and continuous variables accommodates meaning, specific numbers range, classification, assumption and representation (Allen 2017). On one hand, discrete variables constitute complete range of numbers, while continuous variable define an incomplete rage of number. Similarly, discrete variables obtains value through counting while, continuous variable obtain values through measuring. Discrete variables assume distinct or separate values. In contrast continuous variables assume any value between the two values. Finally, in classification, discrete variables describe non-overlapping classification, while continuous variables identify overlapping classifications. For example, the discrete variable is describable with possible values such as 1, 2, 3… while continuous variable can define how far a ball thrown upwards will take to settle, accommodate measurement accuracy for actual value determination. Thus, understanding the comparison between discrete and continuous variables provide a vital platform for decision-making in data usage and operationalization.

In calculation, discrete variables are insignificant information that gives less accurate outcomes for decision-making and process improvement. Accommodating accuracy in using discrete variables requires repeated measurements (Berkman, & Reise, 2012) Therefore, dependability on probability of the actual magnitude depends on the accuracy of the values.

Level of measurement for:

Career field (e.g., accountant, production manager,). This is Nominal level of variable measurement because it describes one fundamental basic measure that contains two or more mutually inclusive and exclusive categories that cannot be ordered.

Temperature in Fahrenheit. This is Interval variables measurement of variables because there are variables that have a numerical value, and are measured on a continuum, accommodating equal interval between values and items.

A job satisfaction survey measured as “disagree, neutral, agree” is an example of ordinal variable describes level of variable measurements because there are two or more categories like in nominal measurement of variables.

Finally, an example, Total sales for a firm is an example of, the ratio variables because it measures numerical value and continuum; however the ratio values are in absolute zero.

Types of a variable

The number of workers in each department of a large organization describes discrete variable because these are variables are variable assuming a finite number of isolated values.

The dollars of revenue earned during a fiscal year is discrete variable accommodating variables have variable assuming a finite number of isolated values.

The number of software licenses available to employees in a firm is an example of discrete variables because, variables have variable assuming a finite number of isolated values

Finally, the average annual salary of middle managers of an organization demonstrates continuous variable because the variables that assume infinite number of different values

Data Privacy

Data privacy is a fundamental concern for business organization and researchers that require pragmatic articulation to ensure availability, integrity and confidentiality of data. Specifically, creating a significant platform for effective data collection, processing, analysis and transmission, security privacy is a vital consideration in data management. In a research scenario, accommodating techniques such as information security practices and data privacy and confidential through a robust data protection strategies such as access control , authentication and data transmission and sharing polices provide an enhanced platform for data privacy. Notably, organizations dealing with customers’ data for decision-making require a robust platform for a secure data protection platform. Specifically, embracing data privacy principles including fair, lawful and transparency, accuracy, storage limitation, integrity and confidentiality and accountability are vital for effective data privacy concepts (Medine, and Murthy, 2020). Therefore, Data privacy is a vital concern that requires real-time solution and technology-based strategies for a secure and safety data management systems.

 

 

 

 

 

References

Allen, M. (2017). Variables, Continuous. In: The SAGE Encyclopedia of Communication and Research methods. SAGE Publications

Berkman, E.T., & Reise, S.P. (2012). A Conceptual Guide to Statistics Using SPSS. Thousand Oaks, California, United States: SAGE Publications, Inc.

Medine, D., and Murthy, D. (2020). New Approaches to Data Protection and Privacy. https://www.cgap.org/sites/default/files/publications/2020_01_Focus_Note_Making_Data_Work_for_Poor

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