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PLEASE RESPOND TO POST DISCUSSION 1
Vincent Faulks
4 hours ago, at 3:31 AM
The variables from the survey in ACSPUMB1Y2019 included:
The time for departure for work: hour and minute (JWDP) (Independent variable) (Interval-Ratio level of measurement).
The family type and employment status (FES) (Independent variable) (Nominal level of measurement).
The ancestry re-code (ANC) (Dependent variable) (Ordinal level of measurement).
Each level of measurement has different properties and dynamics for statistical processes in quantitative research. Measurements are defined as designated values for a sole attribute (Keller, 2006). The types of measurements needed are based upon the objective, rationale, and purpose of conducted research studies. (Keller, 2006). Data collection essential for the research will determine the methodological measurements necessary for the study (Keller, 2006). Measurements can vary from simple categorization using names, numbers, or codes or organizations (Keller, 2006). Others may require much more systematic approaches utilizing mathematical implications for data representations and analysis (Keller, 2006). In statistics, the four major levels of measurement are nominal, ordinal, interval, and ratio (Keller, 2006). Nominal measurements use characters for organization and description of data with no rankings, ordinal measurements use numbers for rankings systems without incremental requirements, and interval-ratio measurements use numbers and ranking systems with incremental requirements between data sets (Keller, 2006). It is essential to understand the nature of each level of measurement primarily due to its requirements and added significance to the research study (Keller, 2006). With the difference in dynamics, each level of measurement adds various flows to quantitative research. Not having a complete understanding of the levels of measurement can cause misrepresentation of data, results, and analysis. In the development of quantitative research studies, its imperative to strategically use the appropriate levels of measurement relevant to the study.
References:
Keller, D. K. (2006). Data measurement. In The Tao of statistics: A path to understanding (with no math) (pp. 10-11). SAGE Publications, Inc., https://dx.doi.org/10.4135/9781452226637.n4
Keller, D. K. (2006). Data structure levels of measurement. In The Tao of statistics: A path to understanding (with no math) (pp. 12-21). SAGE Publications, Inc., https://dx.doi.org/10.4135/9781452226637.n5
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