Is the level of measurement where data are classified into categories and the order of those categories is not important?
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Show Which graphical technique data by showing the relationship between two variables measured with interval or ratio scales? Scatter diagram Pie chart Bar chart. Histogram When discussing the normal distribution, the standard normal probability distribution I. has a mean equal to 1 and a standard deviation equal to 0. II. can be converted from any normal distribution using a formula. III. measures the distance from the mean in units of the standard deviation. I only II and III only I and II only I, II and III _______ is the lowest form of measurement. Ratio. Nominal. Interval. Ordinal. When defining the probabilities in a normal distribution, which is the best estimate of standard normal distribution? Use the Empirical Rule to determine your answer. 0.68 0.5 0.95 0.99 Systematic random sampling Simple random sampling Stratified random sampling Cluster sampling Mode Range Standard deviation Median ________ is a numeric value obtained from an experiment that may, by chance, result in different values. Predicted variable Estimated variable None of the listed choices. Random variable Which of the following variable is an example of quantitative data? The price of a house. The type of a house. The location of a house. The colour of a house. a To measure variability, which of the following statements are true? When conducting ______________, a population is first divided into strata (sub-groups). A sample is then chosenfromeachstratum. Question 5 Question 6 _________ is an appropriate measure of location to interpret the middle observation after all the observations havebeenarrangedfromlowtohigh? Levels of Measurement In 1946, Harvard University psychologist Stanley Smith Stevens developed the theory of the four levels of measurement when he published an article in Science entitled, "On the Theory of Scales of Measurement." In this famous article, Stevens argued that all measurement is conducted using four measurement levels. The four levels of measure, in order of complexity, are: Nominal Ordinal Interval Ratio Here is a simple trick for remembering the four levels of measurement: Think "NOIR." Noir is the French word for black. "N" is for nominal. "O" is for Ordinal. "I" is for Interval. And, "R" is for ratio. Categorical and Quantitative Measures: The nominal and ordinal levels are considered categorical measures while the interval and ratio levels are viewed as quantitative measures. Knowing the level of measurement of your data is critically important as the techniques used to display, summarize, and analyze the data depend on their level of measurement. Let us turn to each of the four levels of measurement. A. The Nominal Level The nominal level of measurement is the simplest level. "Nominal" means "existing in name only." With the nominal level of measurement all we can do is to name or label things. Even when we use numbers, these numbers are only names. We cannot perform any arithmetic with nominal level data. All we can do is count the frequencies with which the things occur. With nominal level of measurement, no meaningful order is implied. This means we can re-order our list of variables without affecting how we look at the relationship among these variables. Here are some examples of nominal level data:
With the nominal level of measurement, we are limited in the types of analyses we can perform. We can count the frequencies of items of interest, but we cannot sort the data in a way that changes the relationship among the variables under investigation. We can calculate the mode of the frequently occurring value or values. And, we can also perform a variety of non-parametric hypotheses tests. Non-parametric tests make no assumptions regarding the population from which the data are drawn. But, we cannot calculate common statistical measures like the mean, median, variance, or standard deviation. B. The Ordinal Level The ordinal level of measurement is a more sophisticated scale than the nominal level. This scale enables us to order the items of interest using ordinal numbers. Ordinal numbers denote an item's position or rank in a sequence: First, second, third, and so on. But, we lack a measurement of the distance, or intervals, between ranks. For example, let's say we observed a horse race. The order of finish is Rosebud #1, Sea Biscuit #2, and Kappa Gamma #3. We lack information about the difference in time or distance that separated the horses as they crossed the finish line. Here are some examples of ordinal level data:
With the ordinal level of measurement, we can count the frequencies of items of interest and sort them in a meaningful rank order. And, as we said, we cannot, however, measure the distance between ranks. In terms of statistical analyses, we can count the frequency of an occurrence of an event, calculate the median, percentile, decile, and quartiles. We can also perform a variety of non-parametric hypotheses tests. But, we cannot calculate common statistical measures like the mean, median, variance, or standard deviation. And, we cannot perform parametric hypothesis tests using z values, t values, and F values. C. The Interval Level With the interval level of measurement we have quantitative data. Like the ordinal level, the interval level has an inherent order. But, unlike the ordinal level, we do have the distance between intervals on the scale. The interval level, however, lacks a real, non-arbitrary zero. To repeat, here are three characteristics of the interval level:
The classic example of the interval scale is temperature measured on the Fahrenheit or Celsius scales. Let's suppose today's high temperature is 60º F and thirty days ago the high temperature was only 30º F. We can say that the difference between the high temperatures on these two days is 30 degrees. But, because our measurement scale lacks a real, non-arbitrary zero, we cannot say the temperature today is twice as warm as the temperature thirty days ago. In addition to temperature on the Fahrenheit or Celsius scales, examples of interval scale measures include:
With the interval level of measurement, we can perform most arithmetic operations. We can calculate common statistical measures like the mean, median, variance, or standard deviation. But, because we lack a non-arbitrary zero, we cannot calculate proportions, ratios, percentages, and fractions. We can also perform all manner of hypotheses tests as well as basic correlation and regression analyses. D. The Ratio Level The last and most sophisticated level of measurement is the ratio level. As with the ordinal and interval levels, the data have an inherent order. And, like the interval level, we can measure the intervals between the ranks with a measurable scale of values. But, unlike the interval level, we now have meaningful zero. The addition of a non-arbitrary zero allows use to calculate the numerical relationship between values using ratios: fractions, proportions, and percentages. An example of the ratio level of measurement is weight. A person who weights 150 pounds, weights twice as much as a person who weighs only 75 pounds and half as much as a person who weighs 300 pounds. We can calculate ratios like these because the scale for weight in pounds starts at zero pounds. n addition to weight, examples of ratio scale measures include:
With the ratio level of measurement, we can perform all arithmetic operations including proportions, ratios, percentages, and fractions. In terms of statistical analyses, we can calculate the mean, geometric mean, harmonic mean, median, mode, variance, and standard deviation. We can also perform all manner of hypotheses tests as well as correlation and regression analyses.
toc | return to top | previous page | next page What level of measurement that is used to classify or categorize data?The researcher should note that among these levels of measurement, the nominal level is simply used to classify data, whereas the levels of measurement described by the interval level and the ratio level are much more exact.
What levels of measurement are categorical?Categorical variables are those that have discrete categories or levels. Categorical variables can be further defined as nominal, dichotomous, or ordinal. Nominal variables describe categories that do not have a specific order to them. These include ethnicity or gender.
What is a level of measurement where the data have order and rank?The ordinal scale also categorizes variables into labeled groups, and these categories have an order or hierarchy to them. For example, you could measure the variable “income” on an ordinal scale as follows: low income, medium income, high income.
What is nominal level of measurement?Nominal level of measurement is the least precise and informative, because it only names the 'characteristic' or 'identity' we are interested. In other words, in nominal variables, the numerical values just "name" the attribute uniquely. In this case, numerical value is simply a label.
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