## Qualitative and quantitative models for ordinal data analysis

The Quantitative and quantitative variables Are properties that can change and whose fluctuation is observable in some way. In this way, qualitative variables speak of properties that can not be measured with numbers and the quantitative ones include those to which a numerical value can be assigned Bonton, The branch of science that is responsible for studying the behavior of qualitative and quantitative variables is statistics.

In this way, it analyzes the numerically measurable variables and the abstractions that can not be measured and whose estimation depends on the individual who perceives them Statistics, Within the qualitative variables we can find two types: nominal and ordinal.

The first type refers to those variables that do not have an order criterion, while the second type obeys the variables that follow a pattern of order or belong to a scale of value. The quantitative variables are classified as discrete and continuous, the first being those defined by a finite number of elements 1, 2, 3, etc.

Qualitative variables include all observable qualities or characteristics of a group or population that can not be measured numerically.

They are usually associated with a physical attribute quality of a group of individuals. These variables can be divided into two types: nominal lacking a criterion of order and ordinal they have a criterion of order Andale, Statistics How To, Nominal qualitative variables are those that lack or do not admit a criterion of order and do not have an assigned numerical value.

An example of such variables may be marital status married, single, divorced, widowed. Ordinary qualitative variables are known as semi-quantitative variables. Although they allude to attributes or qualities that lack a numerical value, they are classified within a scale of value.

An example of this type of variables can be the result of a sport competition first, second or third place. Terror, a concept that can not be measured. This is a nominal qualitative variable, since it can not be measured numerically.

Fear is a variable that changes according to the person who feels it and fluctuates depending on the situation in which they feel when reacting to a particular event or event. Hunger can not be measured numerically, so it is considered as a qualitative ordinal variable. This variable can only be perceived by the person who feels it and can be classified in a lot, little or nothing, according to the specific situation or time.

This variable is a concept that can only be measured by the individual who interprets it. Beauty is a quality that lacks numerical value and can not be classified within a ladder. Therefore, it is a nominal qualitative variable. The marital status of a person is a nominal qualitative variable to which a numerical value can not be assigned.

It is a concept that has no specific order established. This variable can not be measured numerically because it depends on the value that each person assigns. Happiness is a property that each individual feels subjectively and there is no tool to measure the degree of happiness that a person can feel. This variable can not be measured numerically and is expressed in attitudes and point moments. The variable that determines how useful an object is is clearly qualitative.

In this way, utility is perceived by each individual according to a specific situation. This is a qualitative ordinal variable, since there is a categorical classification that assigns a place within a competition. In this way the gold, silver and bronze medals denote the place occupied in a competition without the necessity of assigning a numeric value to the results. This variable is qualitative since the creativity can not be measured numerically.

Likewise, it is a factor that varies from person to person depending on the specific moment in which it takes place. Quantitative variables, as the name implies, are those that can be expressed by a numerical value. In this way it is feasible to perform operations and mathematical calculations with them.Statistics is all about study and collection of data. In our earlier topic, we have discussed primary and secondary data.

Primary data is the data acquired by the researcher to address the problem at hand, which is classified as qualitative data and quantitative data.

Qualitative data is a data concerned with descriptions, which can be observed but cannot be computed. On the contrary, quantitative data is the one that focuses on numbers and mathematical calculations and can be calculated and computed. These data types are used in a number of fields like marketing, sociology, business, public health and so on. Take a read of this article to know the difference between qualitative and quantitative data. Basis for Comparison Qualitative Data Quantitative Data Meaning Qualitative data is the data in which the classification of objects is based on attributes and properties.

Quantitative Data is the type of data which can be measured and expressed numerically. How many or How much? Sample Small number of non-representative samples Large number of representative samples Outcome Develops initial understanding. Recommends final course of action. Qualitative Data refers to the data that provides insights and understanding about a particular problem.

It can be approximated but cannot be computed. Hence, the researcher should possess complete knowledge about the type of characteristic, prior to the collection of data. The nature of data is descriptive and so it is a bit difficult to analyze it.

This type of data can be classified into categories, on the basis of physical attributes and properties of the object. The data is interpreted as spoken or written narratives rather than numbers. It is concerned with the data that is observable in terms of smell, appearance, taste, feel, texture, gender, nationality and so on. The methods of collecting qualitative data are:.

### Difference Between Qualitative Analysis and Quantitative Analysis

Quantitative Data, as the name suggests is one which deals with quantity or numbers. It refers to the data which computes the values and counts and can be expressed in numerical terms is called quantitative data.

In statistics, most of the analysis are conducted using this data. Quantitative data may be used in computation and statistical test.

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It is concerned with measurements like height, weight, volume, length, size, humidity, speed, age etc. The tabular and diagrammatic presentation of data is also possible, in the form of charts, graphs, tables, etc. Further, the quantitative data can be classified as discrete or continuous data.

The fundamental points of difference between qualitative and quantitative data are discussed below:. So, for the collection and measurement of data, any of the two methods discussed above can be used. Although both have its merits and demerits, i.Between qualitative and quantitative analysis, one can identify a key difference. Let us approach this topic in the following manner. When conducting research, it is very important to decide on the methodology depending upon the focus of study.

There are two ways to go about an analysis, qualitative analysis, and quantitative analysis. There are many who cannot differentiate between the two concepts and think of them as same which is incorrect. If analysis can be thought of as a continuum, quantitative analysis lies at one extreme and qualitative would obviously lie at the other extreme. This article will explain the differences between the two terms decisively to remove all kinds of doubts. Qualitative Analysis is used when the researcher wishes to analyze data that are subjective and not numerical.

This includes data such as life experiences, opinions, attitudesetc. Let us examine this through an example. In the social sciences, a study is being conducted to comprehend the experience of victims of sexual abuse. The researcher gathers information which reveals the subjective experience of victims. They describe the event, their attitudes, their emotionsetc.

These cannot be analyzed through statistics, and should be interpreted with the usage of qualitative means. Hence, the researcher turns to qualitative analysis. Qualitative research often involves the study of behavior and the reasons behind it. This type of analysis is more concerned with how and why instead of what, where and when which are more focused upon when conducting quantitative research.

What is Quantitative Analysis? Unlike in the case of qualitative analysis, in the quantitative analysis the data is analyzed through statistical means. Let us comprehend this through an example. If s person is made to undergo a blood test, and it confirms that alcohol percentage is 0.

Thus, the major difference between a quantitative and qualitative analysis pertains to numbers and quantities. The names of the two types of analysis are an indicator of the way analysis is conducted. When the analysis involves measurements and statistics, it is a quantitative analysis. On the other hand, a non numerical analysis that deals with quality for example comparing traitsspecies, genus, etc.

If you have to describe the color of a solution in chemistry lab, you are doing qualitative analysis, whereas if you wish to find out the quantity of solute needed in the solution to turn the color of the solution faint green, you are involved in quantitative analysis.

Quantitative Analysis: In the quantitative analysis the data is analyzed through statistical means. Qualitative Analysis: This can be used to explore attitudes, behavior, nature of experience, etc.

Quantitative Analysis: This can be used for presenting percentages or any form of statistically significant data.

## Quantitative Data: Definition, Types, Analysis and Examples

Her research interests are mainly in the fields of Sociology, Applied linguistics, Sociolinguistics, and Linguistic anthropology. She is currently employed as a lecturer.

Leave a Reply Cancel reply.This paper presents an overview of an approach to the quantitative analysis of qualitative data with theoretical and methodological explanations of the two cornerstones of the approach, Alternating Least Squares and Optimal Scaling. Using these two principles, my colleagues and I have extended a variety of analysis procedures originally proposed for quantitative interval or ratio data to qualitative nominal or ordinal data, including additivity analysis and analysis of variance; multiple and canonical regression; principal components; common factor and three mode factor analysis; and multidimensional scaling.

The approach has two advantages: a If a least squares procedure is known for analyzing quantitative data, it can be extended to qualitative data; and b the resulting algorithm will be convergent. Three completely worked through examples of the additivity analysis procedure and the steps involved in the regression procedures are presented. This is a preview of subscription content, log in to check access.

Rent this article via DeepDyve. Tenenhaus, M. Principal components analysis of qualitative variables. Report No. Google Scholar. Les Cahiers de Recherche No. A normalized cone regression approach to alternating least squares algorithms. Unpublished note, University of Leiden, b. A program for principal components analysis of mixed data which uses the alternating least squares method.

Unpublished mimeo, Leiden University, Young, F.

Intro to Hypothesis Testing in Statistics - Hypothesis Testing Statistics Problems \u0026 Examples

The general Euclidean Model. Benzecri, J. Bock, R. Methods and applications of optimal scaling. Burt, C. The factorial analysis of qualitative data.

British Journal of Psychology, Statistical Section, 3— Scale analysis and factor analysis.Qualitative data collection process may be assessed through two different points of view—that of the questionnaire and the respondents. There are different methods of analysis which vary according to the type of data we are investigating. In statistics, there are two main types of data, namely; quantitative data and qualitative data. For the sake of this article, we will be considering one of these two, which is the qualitative data.

Qualitative data is a type of data that describes information. It is investigative and also often open-ended, allowing respondents to fully express themselves. Numbers like national identification number, phone number, etc. Examples of qualitative data include sex male or femalename, state of origin, citizenship, etc.

A more practical example is a case whereby a teacher gives the whole class an essay that was assessed by giving comments on spelling, grammar, and punctuation rather than score.

Qualitative Data can be divided into two types, namely; Nominal and Ordinal Data. In statistics, nominal data also known as nominal scale is a classification of categorical variables, that do not provide any quantitative value. It is sometimes referred to as labelled or named data.

This is not true in some cases where nominal data takes a quantitative value. However, this quantitative value lacks numeric characteristics. Unlike, interval or ratio data, nominal data cannot be manipulated using available mathematical operators. For example, a researcher may need to generate a database of the phone numbers and location of a certain number of people. An online survey may be conducted using a closed open-ended question.

The best way to collect this data will be through closed open-ended options.

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The country code will be a closed input option, while the phone number will be open. Thus, ordinal data is a collection of ordinal variable s.

For example, the data collected from asking a question with a Likert scale is ordinal. Other examples of ordinal data include the severity of a software bug critical, high, medium, lowfastness of a runner, hotness of food, etc.

In some cases, ordinal data is classified as a quantitative data type or said to be in between qualitative and quantitative. This is because ordinal data exhibit both quantitative and qualitative characteristics.Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods.

In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is shown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling matrices.

For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process defining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such kind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of the chosen model specification is outlined.

In this paper some aspects are discussed how data of qualitative category type, often gathered via questionnaires and surveys, can be transformed into appropriate numerical values to enable the full spectrum of quantitative mathematical-statistical analysis methodology.

Therefore the impacts of the chosen valuation-transformation from ordinal scales to interval scales and their relations to statistical and measurement modelling are studied. This is applied to demonstrate ways to measure adherence of quantitative data representation to qualitative aggregation assessments-based on statistical modelling.

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Finally an approach to evaluate such adherence models is introduced. Concurrent a brief epitome of related publications is given and examples from a case study are referenced. This differentiation has its roots within the social sciences and research. A brief comparison of this typology is given in [ 12 ].

An elaboration of the method usage in social science and psychology is presented in [ 4 ]. A precis on the qualitative type can be found in [ 5 ] and for the quantitative type in [ 6 ]. A comprehensive book about the qualitative methodology in social science and research is [ 7 ]. Since both of these methodic approaches have advantages on their own it is an ongoing effort to bridge the gap between, to merge, or to integrate them.

The research on mixed method designs evolved within the last decade starting with analysis of a very basic approach like using sample counts as quantitative base, a strict differentiation of applying quantitative methods to quantitative data and qualitative methods to qualitative data, and a significant loose of context information if qualitative data e.

The main mathematical-statistical method applied thereby is cluster-analysis [ 10 ]. Model types with gradual differences in methodic approaches from classical statistical hypothesis testing to complex triangulation modelling are collected in [ 11 ]. Recently, it is recognized that mixed methods designs can provide pragmatic advantages in exploring complex research questions.

However, the analytic process of analyzing, coding, and integrating unstructured with structured data by applying quantizing qualitative data can be a complex, time consuming, and expensive process.

In [ 12 ], Driscoll et al. A way of linking qualitative and quantitative results mathematically can be found in [ 13 ]. There are fuzzy logic-based transformations examined to gain insights from one aspect type over the other.Qualitative data can be defined as the type of data that characterizes and approximates but cannot measure the properties, attributes, or characteristics of a phenomenon or a thing. The data can also be recorded and observed are generally non-numerical in value.

The scientists doing any type of research implement the qualitative data to chalk out the characterizations of polling demographics.