For example:. This technique provides the respondent with the opportunity to give a nonrestrictive answer to the question. In this case, the question is usually followed by a text box.
Although, the text box may be given other restrictions like the length of words or characters, input type, language etc. These restrictions serve more as a guide to answering correctly than a limitation placed on the respondent's answer. Cons of Open-Ended Questions. This technique gives room for more than one answer to a question. It puts into consideration the fact that humans may fall into different categories of classification at a time.
This technique is usually implemented with the use of checkboxes. That way, the responders can only check 3 boxes. Below are some examples of Multiple Response Choice questions with and without restrictions. What toppings would you like on your pizza? In example I. However, the second example gives the respondents the opportunity to choose as many toppings as they like. This technique combines the characteristics of open-ended questions and multiple response choice questions to make up its own.
In other words, we can say Close-Open ended questions are close multiple choice questions with an open end. The hybrid nature of this technique makes it the most flexible of the three.
Similar to the first 2, the close-open ended technique may also have some restrictions. Below are some examples of Close-Open ended questions. Which of the following African countries do you reside in? Others, specify. In this example, there are no restrictions placed on the response. A restriction was placed on the multiple response section of this example. Here, the responders can only check 3 boxes out of the 5 available options. However, there is no restriction on the open-ended section.
With Formplus' data collection tool, you can use any of the above techniques to collect Nominal Data with online surveys or questionnaires. Sign Up for Free. Nominal data can be analysed using the grouping method.
The input nominal variables are grouped together and classified into different categories. For each category, we calculate the percentage or frequency mode of the input variables. After this analysis, nominal data can now be interpreted as a bar chart or pie chart. The analysis of Nominal Data is based on the percentage and frequency distribution due to its qualitative nature.
Even if arranged in ascending or descending order, the mean cannot be calculated. When analysing Nominal data, we summarise it into a frequency distribution table, that shows the categories and their counts.
That is, the table lists the responses and the number of times they appear in the data set. The table can be analysed through some graphical techniques, namely; pie chart and bar chart. These techniques are applicable for both the entire data in the table and a sample selected from it. A bar chart is mainly used for analysing Nominal data. It graphically represents the frequency of each response as a bar rising vertically from the horizontal axis.
The height of each bar is directly proportional to the frequency of the corresponding response. The pie chart is also used to analyse Nominal data. It is used to represent the percentage frequency of each sample in a set of Nominal data.
Although, both the bar chart and pie chart is used for analysing nominal data, they are used in different cases depending on the factor that is being considered. The pie chart is mainly used when the researcher is considering the percentage or fraction while the bar chart is used when the researcher is considering frequency mode of the distribution. Therefore, the mean and standard deviation cannot be evaluated for these data types.
However, the use of parametric statistics for ordinal data may be permissible in some cases. This is done with methods that are a close substitute to mean and standard deviation. Examples of nominal data include country, gender, race, hair color etc.
Note that the nominal data examples are nouns, with no order to them while ordinal data examples come with a level of order. The data to be collected from Example I. Although they are all non-parametric, these tests differ from each other. This difference is partly influenced by the ordered nature of ordinal data. Nominal data analyisis is done by grouping input variables into categories and calculating the percentage or mode of the distribution, while ordinal data is analysed by computing the mode, median and other positional measures like quartiles, percentiles, etc.
Although discouraged, ordinal data is sometimes analysed using parametric statistics, with methods which are a close substitute to mean and standard deviation. The different nominal data collection techniques we have include; open ended questions, multiple response choice questions and close-open ended questions, while ordinal data is collected using likert scale, interval scale, rating scale etc.
Even though these collection techniques differ from each other, a single questionnaire could use both nominal and ordinal data collection techniques. Use a single questionnaire to collect both nominal and ordinal data occurs in the event that the researchers need to collect both nominal and ordinal data.
Nominal data are categorical in nature, while ordinal data are in between categorical and quantitative. This is because we sometimes assign quantitative values to ordinal data. Although we cannot perform any arithmetic operation with ordinal numbers, it is quite different from nominal data which does not have any quantitative value at all.
Ordinal data is mainly used to carry out investigations that involve getting people's views or opinion on some matter, while nominal data is used for research that involve getting personal data of a person e. Consider a restaurant who needs to collect customer's data before and after service. Nominal data of the customer's name, phone number and order will be taken by the restaurant before service. After service, the restaurant will take ordinal data of the customer's feedback about the service rendered.
Nominal data give the respondents the freedom to freely express themselves and give adequate information. Ordinal data, on the other hand, does not give respondents the freedom to express themselves. Rather, they are restricted to particular options to choose from. However, this restriction gives researchers access to concise data, by eliminating any possibility of having irrelevant data.
The disadvantage to giving the respondents the freedom to express themselves is that researchers have to deal with a lot of irrelevant data. Although ordinal data ensures that researchers don't have to deal with irrelevances, it doesn't give enough information. When collecting customer feedback, for instance, a business gets informed about customer's satisfaction levels but is ignorant about what influenced their feelings.
This information may not be enough to assist the company in improving its customer service. Nominal data collection does not include rating scales, which is very common with ordinal data collection.
This is mainly because it does not have an order. The rating scales in ordinal data have an order which is used to rate variables. However, these rating scales do not have a specific or predefined difference for each member of the list. Nominal data collection techniques are not as user-friendly as ordinal data collection techniques.
For open-ended and closed-open-ended questions, respondents may have to type their inputs, something many respondents find tiring and time-consuming.
Also, smiley and other user-friendly features can be integrated into ordinal data collection forms, making it user-friendly. This may not be the same with nominal data. Ordinal variables restrict responders to some predefined set of options, with nominal data doing the same in some cases depending on which data collection technique is used. The multiple-choice option questions restrict responders to predefined options, while the open-ended and closed-open-ended questions don't.
For example, when asking respondents to choose a gender with a predefined option of male and female, the closed-open-ended questions allow other genders to identify themselves. This way, the questionnaire understands non-binary gender and is all-gender inclusive.
Nominal data and ordinal data are both groups of non-parametric variables used to store information. They are both classified under categorical data. The characteristics of nominal and ordinal data are similar in some aspects.
For instance, they are both qualitative, have an inconclusive mean value, and have a conclusive mode. These similarities are all based on the fact that they are both categorical data. There are two main types of data which are categorical and numerical data. Nominal and ordinal data are two of the four sub-data types, and they both fall under categorical data.
Categorical data can be counted, grouped, and sometimes ranked in order of importance. With categorical data, information can be placed into groups to bring some sense of order or understanding. Nominal and Ordinal data have 2 categories each, namely; the matched category and the unmatched category. It can be numbers, words, measurements, observations, or even just descriptions of things. Basically, data are two types: constant and variable. Due to unchangeable property, constant is not used and only variable is used for summary measures and analysis.
There are four types of variables: nominal, ordinal, discrete, and continuous. The first two are called qualitative data and the last two are quantitative data. The first two nominal and ordinal are assessed in terms of words or attributes called qualitative data, whereas discrete and continuous variables are part of the quantitative data. Qualitative variable also called categorical variable shows the quality or properties of the data.
It is represented by a name, a symbol, or a number code. These scales are mutually exclusive no overlap and none of them have any numerical significance. It is two types: nominal and ordinal. Nominal variable : Nominal data are simply names or properties having two or more categories, and there is no intrinsic ordering to the categories, i. Ordinal variable : An ordinal variable is similar to a nominal variable.
The difference between the two is that there is a clear ordering in the data, i. For example, ordinal scales are seen in questions that call for ratings of quality very good, good, fair, poor, very poor , agreement strongly agree, agree, disagree, strongly disagree , economic status low, medium, and high , etc.
All the ranking data including Likert scales, Bristol stool scale, and all the other scales which are ranked between 0 and 10 are also called ordinal data. Quantitative variable is the data that show some quantity through numerical value. Quantitative data are the numeric variables e. Age, blood pressure, body temperature, hemoglobin level, and serum creatinine level are some examples of quantitative data. It is also called metric data.
It is two types: discrete and continuous. Discrete variable : Discrete variable is the quantitative data, but its values cannot be expressed or presented in the form of a decimal; for example, number of males, number of females, number of patients, and family size cannot expressed in decimal in meaningful way. Continuous data : Data are measured in values and can be quantified and presented in decimals. Age, height, weight, body mass index, serum creatinine, heart rate, systolic blood pressure, and diastolic blood pressure are some examples.
The variables such as heart rate, platelet count, respiration rate, systolic blood pressure, and diastolic blood pressure are in fact discrete measuring in complete number but are considered continuous because of large number of possible values. Data presentation plays a crucial role in research. The researchers can convince their research to the reader by the effective data presentation. Basically, there are two types of data presentation: numerical and graphical.
There are various types of numerical presentation of the data including arranging them into ascending order, descending order, and classification of the data in the tabular form. Graphs are a common method to visually illustrate relationships in the data. A chart, also called a graph, is a graphical representation of the data, in which the data are represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart.
Graphs enable us in studying the cause-and-effect relationship between two variables. Graphs help measure the extent of change in one variable when another variable changes by a certain amount. There are various types of graphical presentation given below. A bar graph is the presentation of data using rectangular bars, with heights or lengths proportional to the values that they represent.
The reader can easily compare the quantity by observing the length of the bar. In bar graph, the bars may be plotted either horizontally or vertically. In the x-axis, use categorical variable, while in y-axis, use numerical values. Bar graph is three types: simple, adjacent, and cumulative. The last two are also called multiple bar graph. Table of contents Levels of measurement Examples of nominal data How to collect nominal data How to analyze nominal data.
The level of measurement indicates how precisely data is recorded. There are 4 hierarchical levels: nominal, ordinal , interval , and ratio. The higher the level, the more complex the measurement. Nominal data is the least precise and complex level. It does not have a rank order, equal spacing between values, or a true zero value.
Nominal data can be expressed in words or in numbers. In social scientific research, nominal variables often include gender, ethnicity, political preferences or student identity number.
Variables that can be coded in only 2 ways e. Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing. See editing example. If the variable you are interested in has only a few possible labels that capture all of the data, use closed-ended questions.
If your variable of interest has many possible labels, or labels that you cannot generate a complete list for, use open-ended questions. Then, you can gather some descriptive statistics about your data set.
These help you assess the frequency distribution and find the central tendency of your data. But not all measures of central tendency or variability are applicable to nominal data. To organize this data set, you can create a frequency distribution table to show you the number of responses for each category of political preference.
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