Measurements Statistics

1. Measurements Statistics An overview

2. Lesson Objectives • Review • Quantitative descriptive research/ Survey • Level of measurements • Descriptive Statistics

3. Looking at descriptive survey • Define objectives • Define resources available • Identify study population • Identify variables to study • Develop instrument (questionnaire) • Create sampling frame • Select sample • Pilot data collection • Collect data • Analyse data • Communicate results • Use results

4. Levels of measurements • Quantitative and Qualitativevariables • Quantitative variables are measured on an ordinal, interval, ratio scale and nominal scale. • If five-year old subjects were asked to name their favorite color, then the variable would be qualitative. If the time it took them to respond were measured, then the variable would be quantitative

5. Ordinal • Measurements with ordinal scales are ordered in the sense that higher numbers represent higher values. • The intervals between the numbers are not necessarily equal. For example, on a five-point rating scale measuring attitudes toward gun control, the difference between a rating of 2 and a rating of 3 may not represent the same difference as the difference between a rating of 4 and a rating of 5. • There is no "true" zero point for ordinal scales since the zero point is chosen arbitrarily. The lowest point on the rating scale is usually chosen to be 1. It could just as well have been 0 or -5.

6. Interval scale • On interval measurement scales, one unit on the scale represents the same magnitude on the trait or characteristic being measured across the whole range of the scale. • For example, if anxiety were measured on an interval scale, then a difference between a score of 10 and a score of 11 would represent the same difference in anxiety as would a difference between a score of 50 and a score of 51.

7. Interval scale • Interval scales do not have a "true" zero point, however, and therefore it is not possible to make statements about how many times higher one score is than another. For the anxiety scale, it would not be valid to say that a person with a score of 30 was twice as anxious as a person with a score of 15. • A good example of an interval scale is the Fahrenheit scale for temperature. Equal differences on this scale represent equal differences in temperature, but a temperature of 30 degrees is not twice as warm as one of 15 degrees

8. Ratio scale • Ratio scales are like interval scales except they have true zero points. A good example is the Kelvin scale of temperature. This scale has an absolute zero. Thus, a temperature of 300 Kelvin is twice as high as a temperature of 150 Kelvin

9. Nominal scale • Nominal measurement consists of assigning items to groups or categories. • No quantitative information is conveyed and no ordering of the items is implied. • Nominal scales are therefore qualitative rather than quantitative. • Religious preference, race, and sex are all examples of nominal scales. • Frequency distributions are usually used to analyze data measured on a nominal scale. The main statistic computed is the mode. Variables measured on a nominal scale are often referred to as categorical or qualitative variables.

10. Categorizing data • Discrete data: finite options (e.g., labels) • Gender • Female 1 • Male 2 • Discrete: nominal, ordinal, interval • Continuous data: infinite options • Test scores 12 18 23.5 • Continuous: ratio • Discrete data is generally only whole numbers, whilst continuous data can have many decimals

11. Descriptive vs. Inferential Statistics

12. Descriptive Used to summarize a collection of data in a clear and understandable way Inferential Used to draw inferences about a population from a sample “generalize to a larger population” Common methods used Estimation Hypothesis testing Descriptive vs. Inferential Statistics

13. Descriptive Statistics

14. Mean and standard deviation • Central Tendency • Measures the location of the middle or the center of the • Mean - Average • Median: Centre of the distribution • Mode : Most frequently occurring score in a distribution • Standard Deviation • Measure of spread

15. Levels and measures

16. Link LEVELS OF Measurement

17. Describing nominal data • Nominal data consist of labels • e. g 1 = no, 2 = yes • Describe frequencies • Most frequent • Least frequent • Percentages • Bar graphs

18. Frequencies • No. of individuals obtaining each score on a variable • Frequency tables • Graphically ( bar chart, pie chart) • Also %

19. Displaying data for gender

20. Mode • Most common score • Suitable for all types of data including nominal • Example: • Test scores: 16, 18, 19, 18, 22, 20, 28, 18

21. Describing ordinal data • Data shows order e.g ranks • Descriptives • frequencies, mode • Median • Min, max • Display • Bar graph • Stem and leaf

22. Example: Stem and Leaf Plot • Underused. • Powerful • Efficient – e.g., they contain all the data succintly – others could use the data in a stem & leaf plot to do further analysis • Visual and mathematical: As well as containing all the data, the stem & leaf plot presents a powerful, recognizable visual of the data, akin to a bar graph.

23. Example • The data: Math test scores out of 50 points:  35, 36, 38, 40, 42, 42, 44, 45, 45, 47, 48, 49, 50, 50, 50. • Separate each number into a stem and a leaf.  Since these are two digit numbers, the tens digit is the stem and the units digit is the leaf.   • The number 38 would be represented as • Stem 3 Leaf 8 • Group the numbers with the same stems.  List the stems in numerical order.  (If your leaf values are not in increasing order, order them now.)  • Title the graph • To find the median in a stem-and-leaf plot, count off half the total number of leaves.

24. Describing interval data • Interval data are discrete but also treated as ratio/continuous • Descriptives • Mode • Median • Min, max • Mean if treated as continuous

25. Distribution • Describing • Mean • Average, central tendency • Deviation • Variance • Standard deviation • Dispersion • If the bell-shaped curve is steep, the standard deviation is small. • When the data are spread apart and the bell curve is relatively flat, you have a relatively large standard deviation

26. Distribution • Describing • Skewness • a measure of symmetry, or more precisely, the lack of symmetry • Lean, tail • +ve : tail at the right

27. Distribution • Describing • Kurtosis • Flatness/peakedness of distribution • + ve : peaked • data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. • data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak

28. Approx. same skewness, different kurtosis

29. Describing Ratio Data • Can talk meaningfully about ratio data • Measures - central tendency, dispersion

30. Describing Ratio Data • Displaying frequency

31. Sleepiness Let’s LOOK AT data

32. Determining Reliability • Reliability • Reliability is defined as the ability of a measuring instrument to measure the concept in a consistent manner • To determine • Split half analysis- answers on the first half of the questionnaire are compared to the second half of the questionnaire • If there is a high correlation – internally consistent / reliable

33. Determining Reliability Coefficient • Cronbach’s Alpha  • Examines average inter item correlation of the items in the questionnaire • If all items measuring the exact same thing,  = 1 •  = 0.7 or more – reliable • Use SPSS

34. Inferential Statistics Next