What Is a Likert Scale? Guide & Examples

This universally accepted scale can be easily understood and applied to various customer satisfaction or employee satisfaction surveys. Both parametric and non-parametric tests consistently provide the same security against false negatives and also offer the same protection against false positives. These patterns are valid for sample sizes of 10, 30, and 200 per group.

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Scale analysis (statistics)

There are different levels of measurement in statistics, and data measured using them can be broadly classified into qualitative and quantitative data. The ratio scale is the 4th level of measurement scale, which is quantitative. It is defined as a quantitative measurement scale in which the difference between the two variables is meaningful. In other words, the variables are measured in an exact manner, not as in a relative way in which the presence of zero is arbitrary. Different types of map scales such as graphic scale, verbal scale, and representative fraction are often combined on a map to convey in more than one way how to interpret distance on the map. In this lesson, three types of map scales were shown in commonly arranged formats.

Researchers use surveys regularly to measure and analyze the quality of products or services. The respondents provide their opinion about the quality of a product/service from high to low or better to worse using two, four, five, or seven levels. The Likert scale allows researchers to obtain quantitative data from qualitative notions such as providing respondents with a statement and asking them to respond on an agree/disagree continuum.

• With a normal distribution of ratio data, parametric tests are best for testing hypotheses.
• It is important to know that the more specific we are in our scope of analysis, the more detailed a view that we have to observe.
• You can mark the scale on the edge of some scrap paper and hold it up to the map to figure out how far apart two points are.
• Some variables, such as temperature, can be measured on different scales.
• Map scales convey the relationship between distance on the map versus distance in reality, while relative scales convey the level of detail that is shown on a map.

You couldn’t expect a map to be an exact match to the landscape or you would need to unfold miles of paper. By creating a smaller image of the actual landscape, the map becomes a useful tool. However, the relationships between places on the map must remain consistent with reality. This is where the map scale comes in, to tell us what a certain distance on a map represents in real space.

Geography Scales

Unlike on an interval scale, a zero on a ratio scale means there is a total absence of the variable you are measuring. The mode is the score that appears most often and the median is the middle score. Both the mode and the median are measures of central tendency, which aims to use a single value to describe an entire set of data.

Before we discuss all four levels of measurement scales in details, with examples, let’s have a quick brief look at what these scales represent. Usually, convergent validity and discriminant validity are assessed jointly for a set of related constructs. For instance, if you expect that an organization’s knowledge is related to its performance, how can you assure that your measure of organizational knowledge is indeed measuring organizational knowledge and not organizational performance ?

The Interval scale quantifies the difference between two variables whereas the other two scales are solely capable of associating qualitative values with variables. Ordinal Scale maintains descriptional qualities along with an intrinsic order but is void of an origin of scale and thus, the distance between variables can’t be calculated. Descriptional qualities indicate tagging properties similar to the nominal scale, in addition to which, the ordinal scale also has a relative position of variables.

Likert Scale Analysis

If we want to know how many cases of the flu were recorded within a particular city, we would want to view it from a local scale. If we wanted to observe weather patterns over some time for several states within a country, we would want to know the regional data for those areas. It is important to know that the more specific we are in our scope of analysis, the more detailed a view that we have to observe. Examples of scale of analysis from local, national, regional and global scales are shown below. Note that for each scale type, many different types of information can be included, but it is the scale or size of the analysis type that gives us the greater context.

Appropriate inferential statistics for ordinal data are, for example, Spearman’s correlation or a chi-square test for independence. You can use inferential statistics to test hypotheses, such as correlations between different responses or patterns in the whole dataset. Furthermore, you need to decide which descriptive statistics and/or inferential statistics may be used to describe and analyze the data obtained from your Likert scale. If you want finer-grained details about one attribute, select unipolar items. If you want to allow a broader range of responses, select bipolar items.

With a normal distribution of ratio data, parametric tests are best for testing hypotheses. After you’ve collected ratio data, you can gather descriptive and inferential statistics. Almost all statistical tests can be performed on ratio data because all mathematical operations are permissible. The ratio level contains all of the features of the other 3 levels.

Constructing scales

For example, if we were to look at the modes for the three questions, we would find that the participants really do care about their popularity status, more so than what others think of them and living up to their peers’ expectations. A Likert scale is a psychological measurement device that is used to gauge attitudes, values, and opinions. It functions by having a person complete a questionnaire that requires them to indicate the extent to which they agree or disagree with a series of statements.

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Traditional models

Spatial aggregation is the combining of data the larger the map is. The more localized a geographic study is focused, the more detailed the study, and the less aggregated the spatial data is. Map scales are useful because they tell us how distance translates on a map to the real world. These strategies can improve the reliability of our measures, even though they will not necessarily make the measurements completely reliable. There are many ways of estimating reliability, which are discussed next.

Hence, reliability and validity are both needed to assure adequate measurement of the constructs of interest. When responding to an item on the Likert Scale, the user responds based explicitly on their agreement or disagreement level. These scales allow for determining the level of agreement or disagreement of the respondents. The Likert scale assumes that the strength and intensity of the experience are linear. Therefore it goes from a complete agreement to a complete disagreement, assuming that attitudes can be measured.

Convergent validity can be established by comparing the observed values of one indicator of one construct with that of other indicators of the same construct and demonstrating similarity between values of these indicators. Discriminant validity is established by demonstrating that indicators of one construct are dissimilar from (i.e., multi-scale analysis have low correlation with) other constructs. At a fundamental level, Ratio scale data is quantitative in nature due to which all quantitative analysis techniques such as SWOT, TURF, Cross-tabulation, Conjoint, etc. can be used to calculate ratio data. What does random and systematic error imply for measurement procedures?