Discussion Board Reply Week 2

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Quantitative Research Methods Discussion Board #3 Reply Kaleah L. Singleton April 1, 2023 Author Note Kaleah L. Singleton I have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Kaleah L. Singleton Email: klblden@liberty.edu

Jeffrey, thank you for your intuitive answers to our discussion questions this week: Variables, Z Scores, Population and Output - Interpretation Questions D2.3.1 If you have categorical, ordered data (such as low-income, middle-income, and high- income), what type of measurement would you have? Why? I agree that income levels are considered ordinal variables due to the order from high to low ranking involved. We will always standardize income levels and scales to verify what income bracket individuals fall into. The full definition of ordinal focuses on whether the difference between pairs of levels is equal. If these pairs are unequal, then it may be reasonable to treat the levels of data as interval-level data, as we have in this example (Morgan, Leech, Gloeckner, & Barrett, 2020) . D2.3.2a Compare and contrast nominal, dichotomous, ordinal, and standard variables. I agree that our text spells out all of the above with specifically defined measurements. Nominal variables as the most basic or lowest level of measurement in which the numerals assigned to each category stand for the name of the category, but they have no implied order or value (Morgan, Leech, Gloeckner, & Barrett, 2020) . You also stated that nominal variables have one category and dichotomous have two levels of categories, ordinal variables have mutually exclusive categories, and the standard variables have multiple levels/categories and are ordered from low to high (Morgan, Leech, Gloeckner, & Barrett, 2020) . D2.3.2b In social science research, why isn’t it important to distinguish between interval and ratio variables?

I agree that traditionally there are four levels or scales of measurement: nominal, ordinal, interval, and ratio, which all vary from unordered to the highest level of order (Morgan, Leech, Gloeckner, & Barrett, 2020) . If you look further on page 52 in our text under “ other terms for approximately normal variables ," the difference between interval and ratio scales comes from their ability to dip below zero. Interval scales hold no true zero and can represent values below zero. An interval scale allows you to measure all quantitative attributes. Any measurement of the interval scale can be ranked, counted, subtracted, or added, and equal intervals separate each number on the scale. However, these measurements need to provide a sense of ratio between one another (Morgan, Leech, Gloeckner, & Barrett, 2020). A ratio scale has the same properties as interval scales. You can use it to add, subtract, or count measurements. Ratio scales differ by having a character of origin, which is the starting or zero-point of the scale. Also, interval data is measured along a numerical scale with equal distances between adjacent values. On an interval scale, zero is an arbitrary point, not a complete absence of the variable (Bush, Demakis, & Rohling, 2017) . Common examples of interval scales include standardized tests, such as the SAT, and psychological inventories. D2.3.3 What percent of the area under the standard normal curve is within one standard deviation of (above or below) the mean? What does this tell you about scores that are more than one standard deviation away from the mean? Yes, approximately 34% of the area under the normal curve is between the mean and one standard deviation above or below the mean. If we include the are both to the right, and the left of the mean, 68% of the area under the normal curve is within one standard deviation from the mean" (Morgan, Leech, Gloeckner, & Barrett, 2020) . Two standard deviations to the right of the

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mean account for an area of approximately 95% of the curve. Values not falling within two standard deviations of the mean are seen as relatively rare events (Morgan, Leech, Gloeckner, & Barrett, 2020) . This answer can be straight from our text. Good job on your accuracy. D2.3.4 How do z scores relate to the normal curve? I agree that z = 1 means the number is one standard deviation away, z = 2 means two and so on (Morgan, Leech, Gloeckner, & Barrett, 2020) . How would you interpret a z score of – 3.0? I also agree that -3.0 is a point on the scale or data three standard deviations below the mean. What percentage of scores is between a z of -2 and a z of +2? Why is this important? Good catch that this was an earlier question worded differently. I agree with your answer that two standard deviations to the right of the mean account for an area of approximately 95% of the curve. Values not falling within two standard deviations of the mean are seen as relatively rare events (Morgan, Leech, Gloeckner, & Barrett, 2020) . D2.3.5 Why should you not use a frequency polygon if you have nominal data? What would be better to use to display nominal data? Our text states that a frequency polygon connects the points between the categories and is best used with approximately normal or ordinal data (Morgan, Leech, Gloeckner, & Barrett, 2020) . I agree that the use of polygons is significant in nominal data because there is no necessary ordering involved in the response categories (Morgan, Leech, Gloeckner, & Barrett, 2020) .

Overall, I can locate all answers per our text, and you have done a great job with the discussion questions this week. References Bush, S., Demakis, G., & Rohling, M. (2017). Publication Details for Handbook of foresnsic neuropsychology. Washington, DC: American Psychology Association. Morgan, G., Leech, N., Gloeckner, G., & Barrett, K. (2020). IBM SPSS for Introductory Statistics: Use and Interpretation, Sixth Edition (6th Ed.). Routledge.

Discussion Board Reply Week 2

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