Consider The Five Survey Questions On Job Satisfaction

Consider The Five Survey Questions Below From A Job Satisfaction Surve

Consider the five survey questions below from a job satisfaction survey, and indicate the levels of measurement used for each question (nominal, ordinal, interval, or ratio). Briefly explain your rationale for each decision. Double-check the work of at least one peer, and discuss any differences.

Question A: I feel I am being paid a fair amount for the work I do (Fields, 2002). Disagree very much Disagree moderately Disagree slightly Agree slightly Agree moderately Agree very much

Question B: My primary role in the company is: administrative, maintenance, laborer, manager, driver.

Question C: A reasonable amount I should be expected to contribute annually to the company's health plan is: 0 to $2,000, $2,001 to $4,000, $4,001 to $6,000, $6,001 to $8,000, $8,001 or greater.

Question D: Indicate the highest amount you were able to contribute to your 401k in 2017. $1,000, $2,000, ..., up to $24,000.

Paper For Above instruction

Understanding the levels of measurement in survey questions is fundamental to proper data analysis in organizational research. Each question's type determines what statistical techniques are appropriate and guides meaningful interpretation. This paper analyzes the provided survey questions from a job satisfaction survey to categorize their levels of measurement (nominal, ordinal, interval, ratio) and justifies each categorization with clear reasoning.

Question A: Perceived Pay Fairness

The first question assesses employees’ perceptions of whether they are paid fairly for their work. It employs a Likert scale with options ranging from "Disagree very much" to "Agree very much." The responses exhibit an ordered relationship indicating increasing agreement, yet the differences between options are not necessarily equal in magnitude. Consequently, this measure is best classified as ordinal.

Ordinal measurement captures data that can be ranked or ordered, but the intervals between ranks do not need to be equal. In this question, the options reflect a continuum of agreement, but it is not certain that the difference in sentiment between "Disagree very much" and "Disagree moderately" is quantitatively equivalent to that between "Agree moderately" and "Agree very much." Therefore, the data are ordinal, emphasizing rank order without assuming equal intervals.

Question B: Primary Role in the Company

This question asks respondents to specify their primary role, choosing among categories such as administrative, maintenance, laborer, manager, or driver. These categories represent distinct job functions without intrinsic order or numerical significance; they are labels or names of categories.

This type of data is classified as nominal, because it involves categorical variables that are mutually exclusive and do not have a logical order or ranking. The categories serve to identify or classify respondents’ roles without implying any hierarchy or magnitude.

Question C: Expected Annual Contribution to Health Plan

Question C presents ranges of monetary contributions, such as "0 to $2,000" and "$2,001 to $4,000," which respondents select based on their expectations. The options denote categories with a meaningful order, where higher ranges suggest greater contributions.

The data are ordered and have a clear rank, but the intervals between categories are inconsistent (e.g., $1,999 is the width of the first interval, while subsequent intervals vary slightly). Since these categories are ordered and the distances between them are not necessarily uniform, this question's data are best classified as ordinal.

Question D: Highest 401k Contribution

This question asks respondents to specify the maximum amount they contributed to their 401k, with options ranging from $1,000 to $24,000 in $1,000 increments. The responses are numerical and represent exact dollar amounts.

Because the dollar amounts are measured on a scale with equal intervals and a true zero point (no contribution), this data can be treated as ratio measurement. Ratio data allow for meaningful calculations of ratios (e.g., "$4,000 is twice $2,000") and are suitable for statistical operations that require interval data with a natural zero point.

Discussion of Measurement Levels and Rationales

In summary, Question A’s ordinal classification stems from the Likert scale used to gauge agreement levels. Question B's nominal classification arises from categorical job roles without intrinsic order. Question C’s ordered ranges of contribution fall under ordinal data because of their natural order, despite unequal intervals. Question D’s precise monetary inputs are ratio data, given the numeric scale's meaningful zero and equal intervals.

It is important to correctly identify these levels because it influences the choice of statistical analyses. For example, mean calculations are appropriate for ratio data but not for ordinal data like Likert scales, which are better summarized by medians or modes. Ethical and valid interpretation of survey results depends on such distinctions.

Peer Review and Differences

When reviewing a peer’s work, one might find disagreements regarding the classification of the Likert scale as ordinal rather than interval. Some researchers treat Likert scales as interval data assuming equal spacing, allowing for mean and standard deviation calculations. However, most methodological literature advises treating such scales as ordinal because the intervals are not guaranteed to be equal, especially with fewer response options. A proper understanding of these distinctions ensures more accurate data analysis and interpretation.

Conclusion

This analysis underscores the importance of understanding various measurement levels in survey research. Correct classification facilitates appropriate statistical techniques and ensures valid conclusions. For the provided questions, the classifications are as follows: Question A – ordinal, Question B – nominal, Question C – ordinal, Question D – ratio. Recognizing these allows researchers and practitioners to analyze and interpret data accurately, advancing organizational understanding and decision-making.

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