How to interpret ordinal data | Achilleas Kostoulas. The following (slightly modified) question was posted as a comment here, but I felt that the answer was too lengthy for the comments section. Our questionnaire is composed of items with a 5 point scale, ranging from “1=strongly disagree” to “5=strongly agree”. For example, we are trying to find out if the respondents agree with [a topic]. The number of respondents who ‘strongly disagree’ are 2, those who ‘disagree’ are 9, those who ‘are undecided’ are 24, those who ‘agree’ are 18 and those who ‘strongly agree’ are 7. How do I interpret this data? For such data, I suggest that you calculate the median and Inter-Quartile Range (IQR) of each item. You can find some instructions on how to do calculate these metrics with SPSS in this page (the procedure is the same for both). Calculating the median First, you arrange the numbers in an order from largest to smallest, like this: Calculating the IQR The IQR is slightly more complicated, but not too hard.
Reporting the data A final caveat Like this: Likert Scaling. « PreviousHomeNext » Like Thurstone or Guttman Scaling, Likert Scaling is a unidimensional scaling method. Here, I'll explain the basic steps in developing a Likert or "Summative" scale. Defining the Focus. As in all scaling methods, the first step is to define what it is you are trying to measure. Because this is a unidimensional scaling method, it is assumed that the concept you want to measure is one-dimensional in nature.
You might operationalize the definition as an instruction to the people who are going to create or generate the initial set of candidate items for your scale. Generating the Items. next, you have to create the set of potential scale items. Rating the Items. . = strongly unfavorable to the concept = somewhat unfavorable to the concept = undecided = somewhat favorable to the concept = strongly favorable to the concept Selecting the Items. Administering the Scale. . = strongly disagree = disagree = undecided = agree = strongly agree Copyright �2006, William M.K.
Four things you probably didn’t know about Likert scales | Achilleas Kostoulas. Likert scales are among the most frequently used instruments in questionnaire surveys. They consist of a statement and a range of pre-defined responses which measure the intensity of one’s feelings towards the statement. Here’s an example: Figure 1 Example of a Likert scale Likert scales are easy for respondents to understand, and easy for researchers to interpret, which accounts for their widespread use in research.
However, despite their popularity (or maybe because of it), they are not always used optimally. 1. Likert scales were created by Rensis Likert, a sociologist at the University of Michigan whose name is properly pronounced “Lick – urt”. 2. Most frequently, Likert scales consist of five items (as in the example above), and seven-item scales are also quite common. Such a practice is problematic for two reasons: Firstly, most respondents tend to avoid voicing extreme opinions – a phenomenon called the central tendency bias.
Figure 2 A ‘forced-choice’ Likert scale 3. 4. [NB. Statistics Roundtable: Likert Scales and Data Analyses. By I. Elaine Allen and Christopher A. Seaman Surveys are consistently used to measure quality. For example, surveys might be used to gauge customer perception of product quality or quality performance in service delivery. Likert scales are a common ratings format for surveys. Statisticians have generally grouped data collected from these surveys into a hierarchy of four levels of measurement: Nominal data: The weakest level of measurement representing categories without numerical representation. Data analyses using nominal, interval and ratio data are generally straightforward and transparent. An underlying reason for analyzing ordinal data as interval data might be the contention that parametric statistical tests (based on the central limit theorem) are more powerful than nonparametric alternatives.
Basics of Likert Scales The ends of the scale often are increased to create a seven-point scale by adding “very” to the respective top and bottom of the five-point scales. Conclusion I. On Likert scales, ordinal data and mean values | Achilleas Kostoulas. Welcome! Chances are that you landed on this page looking for information on Likert scales and averages. If that is the case, you will probably be want to skip directly to the part of this post where I talk about a common mistake people make with ordinal data and mean values. You should also take a look at the list of additional resources. This post has been prompted by an edited collection that I was recently asked to review. Substantive comments on the book have been published elsewhere; but what I want to do in this post, instead, is share some thoughts regarding a common statistical mistake and a common misconception about published works. Specifically, what sparked my interest was one study in the collection, which used Likert scales to record participants’ attitudes towards a certain educational construct.
Likert scales and ordinal data What are Likert scales? Each of these items measures a variable, i.e., a construct about which we want to learn more. Interpreting Likert scales. The dangers of Likert scale data | Colourchat. Imagine that you want to compare two products A and B and you ask the opinions of 100 users via a survey. The table below shows a summary of the survey and the responses. The numbers under product A and product B show the number of people who gave each of the responses on the left-hand side. This is known as a Likert scale and this post will give some thoughts on how to analyse these data. The first thing that is worth mentioning is that there is a simple form of analysis that is relatively uncontentious. However, quite often we assign numbers to the categories (such as 5 = very satisfied, 4 = quite satisfied, 3 = neutral, 2 = quite dissatisfied, and 1 = very dissatisfied) and when this is done we can produce a number for each participant’s response; we can then average this to produce the mean values shown in the figure above.
Is it valid to average the numbers? How much bigger do two averages need to be for an effect? However, can we conclude that both products are received favourably? How to summarise Likert scale data using SPSS | Achilleas Kostoulas. Elsewhere in this blog, I have written that a Likert scale might consist of several overlapping items. For instance, if I want to measure subjects’ attitudes towards sweets, I might ask them to record how they feel about the following statements: In order to interpret these data, we need to summarise the data in the scale. The safest way to do this is by estimating the median value of all the items. Using the same example as above, I need to create a new ‘super-variable’, which shows the mean of items (1), (2) and (3) for each respondent.
In the paragraphs that follow, I will show how to do this, using SPSS. I assume that you will already know how to define variables and values, how to toggle between the numerical expression and verbal descriptor of the values (i.e., you can make SPSS show responses as “strongly agree/agree/disagree/strongly disagree” or as “1/2/3/4”, and how to key in data. Starting out Your starting point will be a dataset similar to Figure 1 below. Merging the variables. How to interpret ordinal data | Achilleas Kostoulas.