10/16/2021 0 Comments Very Likely Likert Scale
Likert Scale is a scale which measures users’ experience on 5, 7 or 9 point scale. If your PI-RADS or Likert score is 1 or 2, this means youre.What statistical tools to use when dealing with Likert scale data? Jimmie Leppink tries to help to answer this question.Likert Scale. People are more likely to agree (maybe people are too nice or shy to disagree) with statements, which can create a response bias.PIRADS or Likert score 5 Its very likely that you have prostate cancer that needs to be treated. Instead of using statements, try to use more questions in likert scales. And, try to keep your likert scale responses in odd numbers as odd numbers will give you a chance to include a neutral response option.
![]() ![]() Very Likely Likert Scale Free To GetLet us take age and shoe size in a large group of people as examples of why this argument does not hold: it is considered perfectly acceptable by many people to calculate an average age or average shoe size even if the data with which we work only show whole numbers, such as ages ‘20’, ‘21’, ‘23’, etcetera. Oh, and for those among us who do not know or forgot what Likert scales are about, here an example:“Please fill in (for each item) the number on the following scale that represents how you feel about using the Statistical Package for the Social Sciences (SPSS)”:1 ( strongly agree) 2 ( agree) 3 ( neither) 4 ( disagree) 5 ( strongly disagree)III: It does everything I would expect it to doTo start, some people say one cannot means and standard deviations but should use medians and interquartile range because Likert responses are integer numbers – typically: ‘1’, ‘2’, ‘3’, ‘4’, and ‘5’ – and thus you cannot compute a mean because that could end in a number with digits after the comma. If people still struggle with the issue afterwards, feel free to get in touch. As this is a question I have been receiving from people lately almost on a weekly basis, I thought it would be good to write an explanation that you can forward to people who are struggling with the issue.The difference between “never” and “sometimes” may not be the same as the difference between “sometimes” and “often” or the difference between “often” and “always”. Examples of variables of ordinal level of measurement are variables that have a clear rank but the distance between ranks is not (necessarily) the same, for instance a question how often people smoke or drink alcohol in a week with possible answer categories “never”, “sometimes”, “often”, and “always”. In other words, there is no rank order here. Country and culture could be examples of variables of nominal level of measurement: there are different countries and cultures but they cannot be classified in terms of one is worth more than another. See for instance this collection of ten responses to a question on the aforementioned scale ranging from ‘1’ to ‘5’:In the case of an equal number of observations, the median is the average of the middle two cases, so here:According to convention, a variable (anything that can vary) can have either of four levels of measurement: nominal, ordinal, interval, and ratio. Moreover, there are cases when the median too results in a number with digit(s) after the comma, even for Likert data.This is why temperature in Kelvin is – just like weight and length – a variable of “ratio” level of measurement: not only is the distance between 0 and 25 the same as the distance between 25 and 50, ‘0’ is a natural zero of the scale and hence 50 is twice as much of 25 (i.e., a ratio of 2 applies here), just like 2 meters is twice as tall as 1 meter and 30 kilograms is twice as heavy as 15 kilograms.So what’s the level of measurement of Likert scales?This is where things start to get tricky: answers to this question have varied from “interval” through “ordinal” to “not even nominal”. That is different for temperature in Kelvin: 0 Kelvin (that is about minus 273 degrees Celsius, very cold!) means absolutely no thermal energy, and 50 Kelvin is twice as much as 25 Kelvin. However, 30 degrees Celsius is not “twice as hot” as 15 degrees Celsius.Consider the following rank order (size) of animals:Bee (strongly agree) – Mouse (agree) – Dog (neither) – Tiger (disagree) – Elephant (strongly disagree)You probably get the point: the difference in size between a mouse and bee is a different one than the difference in size between a dog and a tiger, etcetera. People who prefer ordinal level of measurement may object to treating Likert scales as of interval level of measurement because they argue that the difference between “strongly agree” and “agree” is not necessarily the same as the distance between “disagree” and “strongly disagree”, etcetera. Again others argue that using ‘1’, ‘2’, ‘3’, ‘4’, and ‘5’ creates an interval level of measurement, because 2 minus 1 and 4 minus 3 yield the same outcome: 1. Others are more optimistic and say that there is a clear rank order in “strongly agree”, “agree”, “neither”, “disagree”, and “strongly disagree” and hence are willing to treat Likert scales as of ordinal level of measurement. Evernote skitch app for windowsThree scenarios to keep it short:Scenario A: bad fit under “ordinal” and “interval” -> perhaps the “not even nominal” guys are right.Scenario B: okay fit under “ordinal” but not under “interval” -> perhaps better treat as “ordinal”.Scenario C: okay fit under “ordinal” and “interval” -> perhaps treating as “interval” is not that bad.Luckily, in practice, scenario B and C appear much more frequently than scenario A, making scenario A very unlikely in most cases and leaving the “ordinal” and “interval” camp quarreling in quite some cases. To avoid ending up with a much lengthier text, I will refrain from a detailed presentation of scaling techniques here, but so-called “fit” indices can then be used to see which assumption is perhaps more realistic. Even worse, if the “not even nominal” people are right, we may as well have to throw more than 99% of all studies using Likert scales in the paper bin… or is it maybe not that bad? Well, to examine which assumption – “not even nominal”, “ordinal” or “interval” – is more reasonable, one can use scaling techniques such as multidimensional scaling. ![]() These are cases when using median and interquartile range could be a more sensible approach than using means and standard deviations. Moreover, strong skewness in response (many responses with low values and fewer to the right, or the other way around) or having many responses at the ends of the scale while having fewer in the middle of the scale generally undermines the use of means and standard deviations, even more so when sample sizes are small. “Scenario B” when using scaling techniques).
0 Comments
Leave a Reply. |
AuthorJessie ArchivesCategories |