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In survey and market research data collection is an integral step that leads you to make effective business decisions. The ordinal scale is one of the ways marketers collect qualitative data by associating numerical values.
This blog will dive deep into everything about the Ordinal Scale.
Ordinal Scale is listed 2nd in the four ‘Levels of Measurement’, as described by S.S. Stevens. The Ordinal scale includes statistical data type where variables are in order or rank but without a degree of difference between categories.
The ordinal scale contains qualitative data; ‘ordinal’ meaning ‘order’. It places variables in order/rank, only permitting to measure the value as higher or lower in scale. The scale cannot generate a precise comparison between the two categories.
For instance, in a horse race, we only see the ranking of the horses that won as 1st, 2nd, and 3rd. The ranks don’t tell us by how much distance did the first horse win or the third horse lose.
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Data obtained using an ordinal scale can be analyzed using descriptive & inferential statistics. So let’s explore the two different statistical approaches to analyzing ordinal data.
The frequency distribution table can inform the number of times each response was selected.
For example, you ask 30 respondents
Q. I take home-cooked meals to the office. Answers:
|
Frequency Distribution Table
Agreement level | Frequency |
Always | 12 |
Often | 5 |
Sometimes | 6 |
Rarely | 3 |
Never | 4 |
**You can visualize the data in a bar graph. It is important to remember that the categories used in the ordinal scale should be in proper order when displaying data.
To find the Central Tendency, you can calculate the mode or the median.
The mode can be found in almost all ordinal scale data. But, the median can be found in some cases.
Based on the above dataset, the mode is “Always.” Mode is the value that appears most frequently in your dataset.
The value of the median is found in two ways for odd & even-numbered datasets.
Odd-numbered dataset: The median is the middle value of the dataset.
Even-numbered dataset: The median is the mean of the two values in the middle.
In this example, the median would be the value at the 15th, and 16th positions,i.e., “Often”
Non-parametric tests are used in the case of inferential statistics. The following are non-parametric tests you can use to analyze ordinal data.
Non-parametric test | Aim | Samples or Variables |
Mood’s median test | Compare the medians | 2 or more samples |
Mann-Whitney U test | Compare sum of rankings of the scores | 2 independent samples |
Wilcoxon matched-pairs signed-rank test | Compare magnitude & direction of difference between the distribution of scores | 2 dependent samples |
Kruskal- Wallis H test | Compare the mean ranking of scores | 3 or more samples |
Spearmen’s rho, or rank correlation coefficient | Correlate 2 variables | 2 ordinal variables |
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We have compiled types of ordinal scales you can use to gather customer data.
Familiarity:
The familiarity scale can help you gather the level of knowledge your respondents have about the topic.
Very Familiar | Quite Familiar | Moderately Familiar | Somewhat Familiar | Not at all Familiar |
A brand can use this type of ordinal scale to test how familiar their audiences are with the brand’s product.
Agreement:
This scale can help determine how much your respondents agree/disagree with your statement.
Strongly Agree | Agree | Neutral | Disagree | Strongly Disagree |
A company can evaluate employee opinion on work-life balance. The degree of agreement or disagreement can help identify how the company can improve employee perception.
Frequency
This ordinal scale can inform you how often an activity is performed to help you evaluate the behavior pattern.
Always | Often | Sometimes | Rarely | Never |
A coffee brand can survey prospective customers about how often they drink coffee in a week. The brand can use the result to segment customers based on their coffee intake and develop strategies suitable for each segment.
Satisfaction:
The best way to understand how satisfied your customers, employees, and prospects are with your services and products.
Very Satisfied | Satisfied | Neutral | Dissatisfied | Very Dissatisfied |
Gather customer satisfaction at every touchpoint in their journey using this scale to determine which touchpoint needs improvement and where you are excelling.
Likelihood:
This scale helps you understand how likely the respondents will perform the suggested activity.
Certainly | Most-likely | Maybe | Unlinkey | Not at all |
A company can use this to identify which customers are most likely to recommend your product to friends and family members.
Ordinal scale is used to collect data so that researchers can infer conclusions. It helps us infer how many respondents are satisfied or dissatisfied with your services. The ordinal scale does not offer the reason behind the response.
However, the ordinal scale can help you gauge customer response quickly and collect data fast. You can create short surveys using an ordinal scale for initial research.
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