The control group score of 47.26 on the pretest put it at the 26th percentile does this percentile score represent nominal oridinL or interval scale data
Interval, it does more than rank.
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To determine whether the percentile score represents nominal, ordinal, or interval scale data, we need to understand the characteristics of each scale.
Nominal data is categorical data that can be placed into distinct categories without any inherent order or numerical meaning. For example, categories like colors (red, blue, green) or yes/no responses fall under nominal scale.
Ordinal data, on the other hand, has categories that can be ordered or ranked, but the difference between the values is not meaningful or consistent. For example, a survey question asking respondents to rate their satisfaction level on a scale of 1-5 (1 being very dissatisfied and 5 being very satisfied) is an ordinal scale.
Interval scale data has the characteristics of both nominal and ordinal scales, but it also has consistent and meaningful intervals between the values. Additionally, interval scale data can include a zero value, but the zero point is arbitrary and doesn't represent the absence of the measured trait. Examples of interval scale data include temperature in degrees Celsius or Fahrenheit.
In the given scenario, the percentile score represents the relative standing or rank of the control group's pretest score. Percentiles are calculated by comparing a score to the distribution of scores from a specific population. In this case, the control group's score of 47.26 places it at the 26th percentile.
Since percentile scores are based on ranking and do not have equal intervals between values, it suggests that the data is ordinal in nature. Therefore, the percentile score represents ordinal scale data.