does a raw score less than the mean correspond to a positive or negative score? what about a raw score greater than the mean.
Z scores below the mean are negative and above the mean are positive.
To understand whether a raw score less than or greater than the mean corresponds to a positive or negative score, we need to consider what the mean represents in the context of the data distribution.
The mean, also known as the average, is a measure of central tendency that represents the average value of a set of data. It is calculated by adding up all the scores in a dataset and then dividing by the total number of scores.
When a raw score is less than the mean, it means that the particular score is lower than the average value of the dataset. The designation of positive or negative does not directly apply to raw scores. However, you can infer that a score lower than the mean might be considered below average or below the norm when looking at the distribution as a whole.
Conversely, when a raw score is greater than the mean, it means that the score is higher than the average value of the dataset. Again, neither a positive nor a negative designation directly applies to raw scores. But it can be inferred that a score higher than the mean might be considered above average or above the norm in the context of the distribution.
To calculate the mean and determine whether a raw score is less than or greater than the mean, you need the following steps:
1. Add up all the scores in the dataset.
2. Divide the sum by the total number of scores to find the mean.
3. Compare the raw score of interest to the mean.
- If the raw score is less than the mean, it is below the average value.
- If the raw score is greater than the mean, it is above the average value.
Understanding the relationship between a raw score and mean allows you to assess where a particular score lies in comparison to the overall dataset.