Looking for an answer to the question: **Are age groups nominal or ordinal?** On this page, we have gathered for you the most accurate and comprehensive information that will fully answer the question: **Are age groups nominal or ordinal?**

"**Nominal**" **data** involves naming or identifying **data**; because the word "**nominal**" shares a Latin root with the word "name" and has a similar sound, **nominal** **data**'s function is easy to remember. "**Ordinal**" **data** involves placing information into an order, and "**ordinal**" and "order" sound alike, making the function of **ordinal** **data** also easy to remember.

**Nominal data** is qualitative data that is grouped into one category from several categories created. For example regarding gender (male and female) is an example of nominal data. Ordinal Data is qualitative data that is grouped into a sequence or ranking. For example education level data (elementary, junior high, high school, university).

Age is, technically, continuous and ratio. A person's age does, after all, have a meaningful zero point (birth) and is continuous if you measure it precisely enough. It is meaningful to say that someone (or something) is 7.28 year old. One may also ask, is gender nominal ordinal interval or ratio?

It is important to change it to either nominal or ordinal or keep it as scale depending on the variable the data represents. In fact, the three procedures that follow all provide some of the same statistics. An Example in SPSS: Satisfaction With Health Services, Health, and Age . Age is classified as nominal data.

Age is, technically, continuous and ratio. A person's age does, after all, have a meaningful zero point (birth) and is continuous if you measure it precisely enough. It is meaningful to say that someone (or something) is 7.28 year old.

Age is, technically, continuous and ratio. A person's age does, after all, have a meaningful zero point (birth) and is continuous if you measure it precisely enough. It is meaningful to say that someone (or something) is 7.28 year old.

One question students often have is: Is “age” considered an interval or ratio variable? The short answer: Age is considered a ratio variable because it has a “true zero” value.

Month should be considered qualitative nominal data. With years, saying an event took place before or after a given year has meaning on its own. There is no doubt that a clear order is followed in which given two years you can say with certainty, which year precedes which. As for months, on their own, you cannot.

Age can be considered as a continuous, ratio variable.

Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn't have data on your respondent's individual ages – you'd only know how many were between 18-24, 25-34, etc.

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.

Is age nominal or ordinal in SPSS? Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn't have data on your respondent's individual ages – you'd only know how many were between 18-24, 25-34, etc.

In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables.

Categorical variables take category or label values and place an individual into one of several groups. ... In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values.

Is Age Discrete or Continuous? Technically speaking, age is a continuous variable because it can take on any value with any number of decimal places. If you know someone's birth date, you can calculate their exact age including years, months, weeks, days, hours, seconds, etc.

A great example of this is a variable like age. Age is, technically, continuous and ratio. A person's age does, after all, have a meaningful zero point (birth) and is continuous if you measure it precisely enough.

1:474:33SPSS: Understand Ordinal, Nominal & Scale (aka Level of measurment)YouTube

Scale . A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.

5. Age is also a variable that can be measured on an interval scale. For example if A is 15 years old and B is 20 years old, it not only clear than B is older than A, but B is elder to A by 5 years.

Is Age Discrete or Continuous? Technically speaking, age is a continuous variable because it can take on any value with any number of decimal places.

One question that students often have is: Is age considered a qualitative or quantitative variable? The short answer: Age is a quantitative variable because it represents a measurable quantity.

Laurel ⭐ Answeregy Expert

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Donnell ⭐ Answeregy Expert

Age can be both nominal and ordinal data depending on the question types. I.e "How old are you" is a used to collect nominal data while "Are you the first born or What position are you in your family" is used to collect ordinal data.

Stephenie ⭐ Answeregy Expert

Herein, are age groups nominal or ordinal? Age is frequently collected as ratio data, but can also be collected as ordinal data. Are years nominal or ordinal? Ordinal variables are categorical. Finally, year can be a nominal variable. You might have data on the year of death of a number of people. Nominal variables are categorical.

Ashlynn ⭐ Answeregy Expert

Age can be both nominal and ordinal data depending on the question types. I.e "How old are you" is a used to collect nominal data while "Are you the first born or What position are you in your family" is used to collect ordinal data. Age becomes ordinal data when there's some sort …

Franklin ⭐ Answeregy Expert

Age can be both nominal and ordinal data depending on the question types. I.e "How old are you" is used to collect nominal data while "Are you the firstborn or What position are you in your family" is used to collect ordinal data. Age becomes ordinal data when there's some sort of order to it. What Is The Best Tool For Collecting Nominal and Ordinal Data?

Heather ⭐ Answeregy Expert

Age group voting patterns in the last presidential election would be an example of nominal data. Take the age groups and describe if they tended to vote Democrat, Republican, Independent, and so on. Another example is take the same age groups and determine the brand of cars they typically purchase such as GM, Ford, Toyota, Kia and so forth.

Marcus ⭐ Answeregy Expert

In this scenario, age would be treated as an ordinal variable because a natural order exists among the potential values. We would say 0-19 years old is younger than 20-39 years old, which is younger than 40-50 years old, which is younger than 60+ years old.

Ashlee ⭐ Answeregy Expert

Then, is age nominal or ordinal? There is no order associated with values on nominal variables. [Ratio] Age is at the ratio level of measurement because it has an absolute zero value and the difference between values is meaningful. For example, a person who is 20 years old has lived (since birth) half as long as a person who is 40 years old.

Simon ⭐ Answeregy Expert

Age can be both nominal and ordinal data depending on the question types. I.e “How old are you” is a used to collect nominal data while “Are you the first born or What position are you in your family” is used to collect ordinal data. Age becomes ordinal data when there’s some sort of order to it….

Quan ⭐ Answeregy Expert

among groups of people or types of texts. 2. In each case, the independent variable is measured using a nominal scale and the research question or hypothesis is about the differences between the nominal categories with respect to some other variable; the dependent variable may be measured using a nominal, ordinal, interval, or ratio scale. a.

Allen ⭐ Answeregy Expert

If you’re new to the world of quantitative data analysis and statistics, you’ve most likely run into the four horsemen of levels of measurement: nominal, ordinal, interval and ratio.And if you’ve landed here, you’re probably a little confused or uncertain about them. Don’t stress – in this post, we’ll explain nominal, ordinal, interval and ratio levels of measurement in simple ...

Chester ⭐ Answeregy Expert

There seems to be some confusion over variable being measured (age is ratio, period) and the way in which it is measured (always a discrete variable, so the measurement by ranges of ages is ordinal, but age is ratio).

Sierra ⭐ Answeregy Expert

Age group voting patterns in the last presidential election would be an example of nominal data. Take the age groups and describe if they tended to vote Democrat, Republican, Independent, and so on. Another example is take the same age groups and determine the brand of cars they typically purchase such as GM, Ford, Toyota, Kia and so forth.

Katharine ⭐ Answeregy Expert

Nominal, ordinal, interval, and ratio data. Going from lowest to highest, the 4 levels of measurement …

Lance ⭐ Answeregy Expert

Classify each of the following as N, nominal; O, ordinal; or I/R, interval/ratio data: a. zip code of your local address ... To study tooth decay a researcher takes a sample at random but with the stipulation that all age groups are represented proportionally. d. A researcher administers a survey instrument to several large classes meeting at 8 ...

Tori ⭐ Answeregy Expert

In the Mann-Whitney U test, researchers can conclude which variable of one group is bigger or smaller than another variable of a randomly selected group. While in the Kruskal–Wallis H test, researchers can analyze whether two or more ordinal groups have the same median or not. Learn about: Nominal vs. Ordinal Scale. Ordinal Scale Examples

Tabatha ⭐ Answeregy Expert

Nominal, when there is no natural ordering among the categories. Common examples would be gender, eye color, or ethnicity. Ordinal, when there is a natural order among the categories, such as, ranking scales or letter grades. However, ordinal variables are still …

Alexander ⭐ Answeregy Expert

Sir, In a strongly worded letter, Mondal[] affirms that age is indeed a number that can be categorized in groups.I agree. In fact, I had stated at least 3 specific circumstances in which this may be desirable: For administrative purposes; when the data cannot be accurately recorded; and when the data are skewed.[]A classical example for the categorization of continuous data is for public ...

Maxim ⭐ Answeregy Expert

Least satisfaction is expressed by the 45–54 age group, of whom approximately 37% gave the highest scores compared with 61% of the youngest group and 60% of the eldest group ( Figure 16 ). Figure 16: Cross-Tabulation of Opinion About Current State of Health Services in the UK and Age Group, 2016 European Social Survey.

Ronda ⭐ Answeregy Expert

Nominal, ordinal, interval, and ratio scales can be defined as the 4 measurement scales used to capture and analyze data from surveys, questionnaires, and similar research instruments. All of the scales use multiple-choice questions. Psychologist Stanley Smith Stevens created these 4 levels of measurement in 1946 and they’re still the most ...

Bobby ⭐ Answeregy Expert

The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. 4 For example, if age is reduced from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 ...

Venus ⭐ Answeregy Expert

Categorical data is divided into groups or categories. ... In our previous post nominal vs ordinal data, we provided a lot of examples of nominal variables ... Age group (under 12 years old, 12-17 years old, 18-24 years old, 25-34 years old, 35-44 years old and etc.)

Delbert ⭐ Answeregy Expert

Nominal Scale and Ordinal Scale are two of the four variable measurement scales.Both these measurement scales have their significance in surveys/questionnaires, polls, and their subsequent statistical analysis.The difference between Nominal and Ordinal scale has a great impact on market research analysis methods due to the details and information each of them has to offer.

Brett ⭐ Answeregy Expert

$\begingroup$ In fact ratio scale is defined, according to Stevens, by the family of transformations that doesn't change the information content/meaning. For interval scales it's linear transformations f(x)=ax+b, for ratio scales only f(x)=ax. It all depends on whether you think that age can be meaningfully transformed by adding/subtracting a constant.

Katherine ⭐ Answeregy Expert

For each question state the data type ( categorical, discrete numerical, or continuous numerical) and measurement level ( Nominal, ordinal, interval, ratio) on a scale 1-5 assess the current job market for your undergraduate major. 1=very bad, 5=very good

Caitlyn ⭐ Answeregy Expert

The same property can be measured on different scales; for example, age can be measured in years (ratio scale), placed into young, middle-aged, and elderly age groups (ordinal scale), or classified as economically productive (ages 16 to 64) and dependent (under …

Mariah ⭐ Answeregy Expert

Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn’t have data on your respondent’s individual ages – you’d only know how many were between 18-24, 25-34, etc.

Grant ⭐ Answeregy Expert

Nominal, ordinal and scale is a way to label data for analysis. While nominal and ordinal are types of categorical labels, scale is different. In SPSS, we can specify the level of measurement as: scale (numeric data on an interval or ratio scale) ordinal; nominal. Nominal and ordinal data can be either string alphanumeric or numeric.

Janette ⭐ Answeregy Expert

There are two types of categorical data (see Figure 1): 1. Ordinal 2. Nominal Created by ASK (2012) Page 2 of 6. Ordinal If the data have a meaningful order or rank then the variable is ordinal ...

Francis ⭐ Answeregy Expert

Group 1: 98 (0), 1, 2; Group 2: 51 (0), 1, 48 (2). The median in both cases is 0, but from the Mann-Whitney test P<0.0001. Only if we are prepared to make the additional assumption that the difference in the two groups is simply a shift in location (that is, the distribution of the data in one group is simply shifted by a fixed amount from the ...

Orville ⭐ Answeregy Expert

Nominal and Ordinal Data. First Chloe collects information about the different types of flowers she's growing, and the colors of each. She counts 4 roses, 6 daisies, 3 sunflowers, and 4 lilies.

Florence ⭐ Answeregy Expert

•Significant relationship between age and weight (r = .476, p = .001); height and weight (r = .672, p = .001) •Control for height, every year adds 1.71 lbs. ... •Level of Measurement: nominal or ordinal •Independent groups •Observed frequencies vs. Expected frequencies

Scotty ⭐ Answeregy Expert

Is age nominal or ordinal in SPSS? Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn’t have data on your respondent’s individual ages – you’d only know how many were between 18-24, 25-34, etc.

Ashley ⭐ Answeregy Expert

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Billy ⭐ Answeregy Expert

5. What kind of data (nominal, ordinal, interval or ratio) does each of the following represent? D) Age group (infant, toddler, school-age) 11) Errors (in msec) in) Coffee serving size (short, tall, grande, venti, trenta) IV) Scote on the Myers-Briggs Personnlity Inventory (Extroverted Introverted, Sensing/Intuiting.

Florinda ⭐ Answeregy Expert

Discretizing a continuous variable transforms a scale variable into an ordinal categorical variable by splitting the values into three or more groups based on several cut points. In the sample dataset, the variable CommuteTime represents the amount of time (in minutes) it takes the respondent to commute to campus.

Eddy ⭐ Answeregy Expert

I'll examine three groups of people's perceptions through 5-point Likert scales. My level of measurement is ordinal in nature. And I want to see the significant difference across the three groups.

Cleo ⭐ Answeregy Expert

Examples of nominal data include country, gender, race, hair color etc. of a group of people, while that of ordinal data includes having a position in class as “First” or “Second”. Note that the nominal data examples are nouns, with no order to them while ordinal data examples come with a level of order.

Kristie ⭐ Answeregy Expert

possible age groups; that is, 11–20, 21–30 etc. 3 Systematically go through the results and place a stroke in the tally column each time a particular age group is noted. 4 Write the total tally of strokes for each age group in the frequency column. 5 Calculate the total of the frequency column. b …

Natashia ⭐ Answeregy Expert

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Kristal ⭐ Answeregy Expert

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Eddie ⭐ Answeregy Expert

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Devan ⭐ Answeregy Expert

B. Was Massachusetts's Smoke-Free Workplace Laws effective in reducing AMI mortality rate across gender and age groups? ANSWER: In the above example, we are looking at the association between two nominal variables. One is smoking ban (Before, After) and the …

Anibal ⭐ Answeregy Expert

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Elwin ⭐ Answeregy Expert

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