NURS 211 Prep Video 1 Notes

NURS 211 Prep Video Notes Video 1 Notes: Nurse Killam video in class Levels of Measurement: How to Remember Differences. [YouTube] -​ Quantitative researchers measure variables to answer their research question. -​ The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. -​ The higher the level of measurement, the more statistical tests that can be run with the data. -​ It is best to use the highest level of measurement possible when collecting information. Lowest Level of measurement ⟶ Highest Level of measurement Nominal ⟶ Ordinal ⟶ Interval ⟶ Ratio Remember the acronym “NOIR” ⬛ (“Black” in French) -​ The level of measurement of a variable depends on the nature of that variable as well as how the researcher collects the data. -​ For example: Some variables like gender can only be measured in a Nominal way. -​ Other variables like household income can be measured at multiple levels depending on how the question is asked. Nominal Data: -​ The LOWEST level of measurement. -​ Discrete catergories. Variables in a study are placed into mutually exclusive categories. -​ Each category has a criteria that a variable either has or does not have. -​ There is no natural order to these categories (order is arbitrary). -​ The categories may be assigned numbers but the numbers have no meaning because they are simply labels -​ For example: If we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair. -​ Nominal data may be considered dichotomous or categorical. -​ Dichotomous data falls into one of two categories like Male/female or yes/no. -​ Categorical data have more than two possible values such as marital status or group membership. -​ Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical. -​ Since nominal data is simply categorical it allows for the fewest statistical tests. It makes sense to report the number or percentage of people who are male or female in a particular group. -​ Nominal data is often presented in bar or pie charts. -​ The only measure of central tendency that makes sense with nominal data is the mode. -​ Possible measures of Nominal data: Mode, Modal percentage, Range, Frequency distribution. -​ Since there is no natural way to order nominal data you cannot find a median or middle number. -​ Likewise, you cannot calculate a mean gender since no numerical value for the data exists. Ordinal Data: -​ Considered categorical. -​ Ordinal data categories have a natural order to them. -​ Example: 1, 2, 3, 4, 5… (Order of lowest to highest) -​ Numbers are assigned to categories but they are arbitrary -​ They are simply used to establish a ranking and there is no absolute zero. -​ While there is an order, it is also unknown how much distance is between each category. -​ -​ -​ -​ -​ -​ -​ -​ -​ The intervals between each number are therefore not necessarily equal. Ordinal scales are often used to measure attitudes and perceptions. -​ Example: A survey may ask how satisfied a customer is on a scale from very dissatisfied to very satisfied. -​ Nurses often use an ordinal scale to get patients to rank their pain on a scale from 1 to 10. -​ This data is ordinal since it is unknown whether the intervals between each value are equal. -​ On a 10-point scale, the difference between a 9 and a 10 is not necessarily perceived to be the same as the difference between a 3 and a 4. All we know is that if the patient rates their pain as an 8 now and a 4 after receiving pain medication, the pain has decreased. We cannot accurately measure how much the pain has decreased since we do not know the difference between the points on the scale. -​ It would be inaccurate to claim that the patient was in twice as much pain before receiving the medication. -​ Likewise, you cannot say that one patient is in twice as much pain as another using this scale. Values in an ordinal scale simply express an order. Order Matters -​ but not the difference between values -​ Unknown distance between rankings Relative rankings: -​ Likert scales -​ Socioeconomic status -​ Pain intensity scale -​ Non-numeric concepts Possible measures of Ordinal data: All nominal level tests, Mode, Median, Percentile, Semiquartile range, Rank order coefficients of correlation. To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode. The mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known. Interval Data: -​ Interval level data is ordered like ordinal data but the intervals between each value are known and equal. -​ Equal distance between values -​ Therefore, the difference between two values is meaningful for interval variables. -​ An accepted unit of measurement. -​ The zero point is arbitrary since a score of zero does not actually mean that the variable does not exist. Zero simply represents an additional point of measurement. -​ Example 1: Tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81. -​ Example 2: Temperature if measured in degrees Fahrenheit or Celsius is another example of interval measurement. On the Fahrenheit scale the difference between a temperature of 37 degrees and 38 degrees is the same difference as between 89 degrees and 90 degrees. The 0 is arbitrary since a temperature of 0 degrees does not mean that there is no temperature. -​ (EXCEPTION: Interval data works on Celcius and Fahrenheit but NOT Kelvin) -​ With interval level scales there is direct, measurable quantity. -​ Zero does not represent the absolute lowest value. Instead, it is point on the scale with numbers both above and below it. -​ Interval scales not only tell us about order, but also about the value between items on a scale. -​ The mean, median and mode can all be calculated with interval data. -​ Possible measures of Interval data: All ordinal tests, Mode, Median, Mean, Standard deviation, Addition and subtraction (CANNOT multiply or divide). Ratio Data: -​ Most precise. Highest level possible for data. -​ Ordered -​ Like interval data, Ratio data is ordered, with known and measurable intervals between each value. -​ -​ Exact values -​ Equal intervals -​ Natural zero -​ When the variable equals 0 it means there is none of that variable. -​ Not arbitrary -​ What differentiates ratio level data from interval level data is that the zero is absolute. -​ Typically this level of measurement is only possible with physical measurements like height, weight, and length. -​ Any statistical tests can be used with ratio level data as long as it fits with the study question and design. -​ It is possible to compare amounts of the variable and make a claim that one is twice as much as the other. -​ When working with ratio variables, but not interval variables, you can look at the ratio of two measurements. -​ Possible measures of Ratio data: Weight, Hight, Pulse, Blood pressure, Time, Degrees in Kelvin -​ Statistics used should be appropriate for the level of data as well as the research question. -​ Classifications of some forms of data are debated. -​ Example 1: Some researchers treat the measurement of intelligence as Ordinal while others treat it as Interval. -​ Example 2: Money in a bank account may be considered ratio since having a balance of 0 means you don't have any money. However, others argue it is interval since it is possible to have a negative balance, which makes the 0 point simply another point of measurement.