Business Studies
There needs to be one source per question and in APA format with the DOI:
If you have categorical, ordered data (such as low income, middle income, high income) what type of measurement would you have? Why?
D2.3.2. (a) Compare and contrast nominal, dichotomous, ordinal, and normal variables. (b) In social science research, why isn’t it important to distinguish between interval and ratio variables?
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?
D2.3.4. (a) How do z scores relate to the normal curve? (b) How would you interpret a z score of –3.0? (c) What percentage of scores is between a z of –2 and a z of +2? Why is this important?
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?
ANSWER
Quantitative Research Methods
If you have categorical, ordered data (such as low income, middle income, high income) what type of measurement would you have? Why
Morgan et al. (2020) stated that an ordinal measurement would have been the appropriate form. The data’s classification is vital in this circumstance. The intervals between high-medium and low-income groups are certainly not similar, and the idea of roughly normal needs 5 or more organized categories, thus the data are neither intervals nor typical. Although categorical is closely aligned with nominal, if the divisions are arranged, the variable must be described as ordered.
D2.3.2. (a) Compare and contrast nominal, dichotomous, ordinal, and normal variables.
The most basic or simplest measurement scale is called a nominal variable, and every class is assigned a series of numbers that correspond to its description but have no intrinsic order or significance (Morgan et al., 2020). Just stages or groups exist for dichotomous values at any one moment. Although certain dichotomous variables could be inferred to provide order, some do not. In all dichotomous and nominal variables, it is beneficial to use the testing makes for various reasons. Ordinal variables are arranged from lowest to higher and mutually incompatible, similar to nominal scales, enabling assigning levels such as 1st, 2nd, and 3rd. If more results are found in the middle groups, ordinal examines if the recurrence tallies for each group or item are spread in a cone, normal distribution.
(b) In social science research, why isn’t it important to distinguish between interval and ratio variables?
The contrast between ratio and interval scales is their potential to deviate from “0”. Interval scales could display values lower than zero since they lack a real zero. An interval scale can be used to assess all quantitative characteristics. Any value on an interval scale could be classed, counted, subtracted from, or either inserted, and the scale’s values are spaced by equal intervals (Morgan et al., 2020).
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?
In either case, the proportion might be roughly 34percentage points high. The precise percentage is 34.13. Over one variance from the mean is present in about a 32percent of the overall scores. Around three of the data points will fall within each standard error of the total mean if a dataset is typically spread around its average (randomly, for our reasons).
D2.3.4. (a) How do z scores relate to the normal curve?
By setting the average to 0 and the variance to one, the normal curve may be changed into a normalized curve (Song et al., 2019). In just 1, 2, or 3 standard deviations, every normal curves hold the same principles. The transformation allows us to compare standard curves with various means and standard variances (Morgan et al., 2020). A standard normal distribution component, commonly referred to it as z-scores, is situated below the normal curve (Morgan et al., 2020).
(b) How would you interpret a z score of –3.0?
When the number is greater than the average, the z-score is affirmative; if it is smaller than the average value, it is negative. The z-score value shows how far you are all from the average by how much standard deviations (Song et al., 2019). These are 3 different deviations underneath the mean when the z value is -3.0.
- c) What percentage of scores is between a z of –2 and a z of +2? Why is this important?
To determine the chance that a score will be chosen randomly from the distributions or sample, researchers might utilize a primary normal distribution. A value within score-1 and score +1 standard variations from the average, for instance, has a 68 % possibility of being chosen randomly. 95 percent of the values in a normal curve fall between a value z of -2 and a value z of +2, in averages. It is crucial since the population’s statistical findings are reliant on these ratings.
Why should you not use a frequency polygon if you have nominal data? What would be better to use to display nominal data?
Frequency polygons are utilized with consistently spread or scale data since they present the data as rising steadily in value along with the graph. Better ways for displaying nominal data also comprise frequency count or the bar chart (Morgan et al., 2020).
References
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2020). IBM SPSS for
Introductory Statistics: Use and Interpretation, Sixth Edition (6th ed.). Routledge.
Song, C., Rohr, R. P., Saavedra, S., & Eklöf, A. (2019). Beware z scores.‐ The Journal of Animal
Ecology, 88(5), 808-809.
https://doi.org/10.1111/1365-2656.12964
