For each value of a variable, the frequency of its occurrence in the sample of data is reported. The display of frequency tabulation is often referred to as the frequency distributionįor the sample of scores. Note that three of the inspectors in the sample did not provide a rating for jobsat thereby producing three missing values (= 2.7% of the sample of 112) and leaving 109 inspectors with valid data for the analysis. 6) produces the frequency tabulation shown in Table 5.1. Performing frequency tabulation across the 112 Quality Control Inspectors on this variable using the SPSS Frequencies procedure (Allen et al.
This count records the frequency of occurrence for that specific data value.Ĭonsider the overall job satisfaction variable, jobsat, from the QCI data scenario. Second, the number of times each specific score occurs in the sample is counted.
Basically, frequency tabulation occurs in two stages:įirst, the scores in a set of data are rank ordered from the lowest value to the highest value. Frequency Tabulation and Distributionsįrequency tabulation serves to provide a convenient counting summary for a set of data that facilitates interpretation of various aspects of those data. Measurement levelĪny level of measurement can be used for a variable summarised in frequency tabulations and crosstabulations. To produce an efficient counting summary of a sample of data points for ease of interpretation. Univariate (crosstabulations are bivariate) descriptive. What patterns might be visually detected when looking at various QCI variables singly and together as a set? How variable were the inspectors in their accuracy and speed scores? Were all the accuracy and speed levels relatively close to each other in magnitude or were the scores widely spread out over the range of possible test outcomes? What percentage of inspectors would have ‘failed’ to ‘make the cut’ assuming the industry standard for acceptable inspection accuracy and speed combined was set at 95%?
How frequently were different levels of inspection accuracy and speed observed? What was the shape of the distribution of inspection accuracy and speed scores? What was the range of accuracy and speed scores the lowest and the highest scores? What was the most common accuracy and speed score amongst the inspectors? What was the typical level of accuracy and decision speed for inspectors in the sample? Consider the following questions that Maree might wish to address with respect to decision accuracy and speed scores: Reflect on the QCI research scenario and the associated data set discussed in Chap. What remains after their application is simply for us to interpret and tell the story. These statistical procedures are designed to identify or display specific patterns or trends in the data. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. Along the way, we explore the fundamental concepts of probability and the normal distribution. a histogram, box plot, radar plot, stem-and-leaf display, icon plot or line graph) or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement (e.g., frequency counts, measures of central tendency, variability, standard scores). By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data (e.g.
The purpose of the procedures and fundamental concepts reviewed in this chapter is quite straightforward: to facilitate the description and summarisation of data. This chapter discusses and illustrates descriptive statistics.