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How to Use the Likert Scale in Statistical Analysis ~ Statistics Caf. When responding to a Likert questionnaire item, respondents specify their level of agreement to a statement. The scale is named after its inventor, psychologist Rensis Likert. The Likert scale is the sum of responses on several Likert items. Because Likert items are often accompanied by a visual analog scale (e. This is the source of much confusion; it is better, therefore, to reserve the term Likert scale to apply to the summated scale, and Likert item to refer to an individual item. A Likert item is simply a statement which the respondent is asked to evaluate according to any kind of subjective or objective criteria; generally the level of agreement or disagreement is measured.

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Often five ordered response levels are used, although many psychometricians advocate using seven or nine levels; a recent empirical study. In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis. The format of a typical five- level Likert item is:   1. Strongly disagree. Neither agree nor disagree. Strongly agree. Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement. Sometimes a four- point scale is used; this is a forced choice method.

Respondents may avoid using extreme response categories (central tendency bias); agree with statements as presented (acquiescence bias); or try to portray themselves or their organization in a more favorable light (social desirability bias). Designing a scale with balanced keying (an equal number of positive and negative statements) can obviate the problem of acquiescence bias, since acquiescence on positively keyed items will balance acquiescence on negatively keyed items, but central tendency and social desirability are somewhat more problematic. Scoring and analysis.

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After the questionnaire is completed, each item may be analyzed separately or in some cases item responses may be summed to create a score for a group of items. Hence, Likert scales are often called summative scales. Whether individual Likert items can be considered as interval- level data, or whether they should be considered merely ordered- categorical data is the subject of disagreement.

Many regard such items only as ordinal data, because, especially when using only five levels, one cannot assume that respondents perceive all pairs of adjacent levels as equidistant. On the other hand, often (as in the example above) the wording of response levels clearly implies a symmetry of response levels about a middle category; at the very least, such an item would fall between ordinal- and interval- level measurement; to treat it as merely ordinal would lose information. Further, if the item is accompanied by a visual analog scale, where equal spacing of response levels is clearly indicated, the argument for treating it as interval- level data is even stronger. When treated as ordinal data, Likert responses can be collated into bar charts, central tendency summarised by the median or the mode (but some would say not the mean), dispersion summarised by the range across quartiles (but some would say not the standard deviation), or analyzed using non- parametric tests, e. If the summed responses fulfill these assumptions, parametric statistical tests such as the analysis of variance can be applied. These can be applied only when more than 5 Likert questions are summed. The chi- square, Cochran Q, or Mc.

Nemar test are common statistical procedures used after this transformation. Consensus based assessment (CBA) can be used to create an objective standard for Likert scales in domains where no generally accepted standard or objective standard exists. Consensus based assessment (CBA) can be used to refine or even validate generally accepted standards. Level of measurement. The five response categories are often believed to represent an Interval level of measurement.

But this can only be the case if the intervals between the scale points correspond to empirical observations in a metric sense. In fact, there may also appear phenomena which even question the ordinal scale level. For example, in a set of items A,B,C rated with a Likert scale circular relations like A> B, B> C and C> A can appear. This violates the axiom of transitivity for the ordinal scale.

Rasch model. Likert scale data can, in principle, be used as a basis for obtaining interval level estimates on a continuum by applying the polytomous Rasch model, when data can be obtained that fit this model. In addition, the polytomous Rasch model permits testing of the hypothesis that the statements reflect increasing levels of an attitude or trait, as intended. For example, application of the model often indicates that the neutral category does not represent a level of attitude or trait between the disagree and agree categories. Again, not every set of Likert scaled items can be used for Rasch measurement.

The data has to be thoroughly checked to fulfill the strict formal axioms of the model. Pronunciation. Rensis Likert, the developer of the scale, pronounced his name 'lick- urt' with a short . Likert's pronunciation use the short . It is often used to measure respondents' attitudes by asking the extent to which they agree or disagree with a particular question or statement. A typical scale might be . Get your data ready for analysis by coding the responses.

For example, let's say you have a survey that asks respondents whether they agree or disagree with a set of positions in a political party's platform. Each position is one survey question, and the scale uses the following responses: Strongly agree, agree, neutral, disagree, strongly disagree. In this example, we'll code the responses accordingly: Strongly disagree = 1, disagree = 2, neutral = 3, agree = 4, strongly agree = 5. Remember to differentiate between ordinal and interval data, as the two types require different analytical approaches. Mark Felt (2017) Ipod Download on this page. If the data are ordinal, we can say that one score is higher than another. We cannot say how much higher, as we can with interval data, which tell you the distance between two points.

Here is the pitfall with the Likert scale: many researchers will treat it as an interval scale. This assumes that the differences between each response are equal in distance. The truth is that the Likert scale does not tell us that. In our example here, it only tells us that the people with higher- numbered responses are more in agreement with the party's positions than those with the lower- numbered responses.

Begin analyzing your Likert scale data with descriptive statistics. Although it may be tempting, resist the urge to take the numeric responses and compute a mean. Adding a response of ? Fortunately, there are other measures of central tendency we can use besides the mean.

With Likert scale data, the best measure to use is the mode, or the most frequent response. This makes the survey results much easier for the analyst (not to mention the audience for your presentation or report) to interpret. You also can display the distribution of responses (percentages that agree, disagree, etc.) in a graphic, such as a bar chart, with one bar for each response category. Proceed next to inferential techniques, which test hypotheses posed by researchers. There are many approaches available, and the best one depends on the nature of your study and the questions you are trying to answer. A popular approach is to analyze responses using analysis of variance techniques, such as the Mann Whitney or Kruskal Wallis test. Suppose in our example we wanted to analyze responses to questions on foreign policy positions with ethnicity as the independent variable.

Let's say our data includes responses from Anglo, African- American, and Hispanic respondents, so we could analyze responses among the three groups of respondents using the Kruskal Wallis test of variance.

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Revised romanization: Shinui Jeoeul. Hangul: . But after failing to pass the bar exam several times, he finally passes the bar one day, which is also the day when he kills a woman by accident. He decides to confess his crime but he lets himself be convinced by his mother to keep it a secret and not disappoint his father. The woman he killed was the girlfriend of Joon- ha (Song Chang- Eui). Joon- ha’s brother, who had a criminal record, decides to confess to the crime so that Joon- ha won’t have to go to jail and their mother dies from grief after the judge sentences her son to life in prison. Joon- ha becomes driven to pass the bar exam and it takes him three years to finally pass the bar, giving him a chance to prove his brother’s innocence.

After deferring his law trainee program and leaving the country for two years, Sang- yoon returns to Korea and enters the law trainee program where he befriends Joon- ha. He then learns that Joon- ha’s brother is serving a life sentence for a crime that was committed by none other than himself. He also finds out that Joon- ha’s mother died from grief that her son was sentenced to prison. Though he should have asked for forgiveness from Joon- ha, he turns into a wicked person. Later on, he becomes a cold- blooded person who misuses the law to serve his own interests.

When Shin Young- joo (Kim Yu- Mi) finds evidence that tacks the murder on Sang- yoon, he dangles the prospects of marrying her to manipulate her. Joon- ha reopens the murder case of his late girlfriend when he gets furious from the revelation that Woo- bin could be the killer. Unaware that his career is in jeopardy, Woo- bin is promoted as a partner to his law firm.

One day, Woo- bin is stunned when his father confronts him and asks, “Did you kill that woman?”. Cast. Additional Cast Members: External Links.