When Should I Use Qualitative Or Quantitative Data
Put simply, whether you use qualitative or quantitative data depends on the data analytics project youre undertaking. Here, well discuss which projects are better suited to which data.
Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.
- Do you want to understand something, such as a concept, experience, or opinions? Use qualitative data.
- Do you want to confirm or test something, such as a theory or hypothesis? Use quantitative data.
- Are you taking on research? You may benefit from a mixed methods approach to data collection.
You may find that more often than not, both types of data are used in projects, in order to gain a clear overall imageintegrating both the numbers side and human side of things.
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Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.
- Direct comparisons of results
The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.
- Large samples
Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.
- Hypothesis testing
Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.
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What Are The Main Differences Between Quantitative And Qualitative Data
The main differences between quantitative and qualitative data lie in what they tell us, how they are collected, and how they are analyzed. Lets summarize the key differences before exploring each aspect in more detail:
- Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
- Quantitative data tells us how many, how much, or how often . Qualitative data can help us to understand the why or how behind certain behaviors, or it can simply describe a certain attributefor example, The postbox is red or I signed up to the email newsletter because Im really interested in hearing about local events.
- Quantitative data is fixed and universal, while qualitative data is subjective and dynamic. For example, if something weighs 20 kilograms, that can be considered an objective fact. However, two people may have very different qualitative accounts of how they experience a particular event.
- Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing.
- Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.
Correlation Is Not Causation
A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:
- The study was a true experiment
- The independent variable can be manipulated
- The dependent variable can be measured through a ratio or a scale
So when you read reports about “gender was linked to whatever”, you need to remember that gender is NOT a cause of the “whatever” in question here. There is just an apparent relationship, but the true cause of the difference is hidden.
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Representational Theory Of Measurement
Representational theory of measurement, developed in the social sciences, formalizes axiomatic conditions by which empirical relational structures can be mapped onto symbolic relational structures, especially numerical ones . For measurement, these many-to-one mappings must be performed such that the study phenomenas properties and their interrelations are appropriately represented by the properties and interrelations of the signs used as data . Permissible transformations specify how the numerical representations can be further transformed without breaking the mapping between the empirical relations under study and the numerical ones generated .
Figure 5. Representational theory of measurement. Key elements of representational systems frequently used in psychological and social-science concepts of measurement.
In physical sciences and engineering, representational theory plays no role, however, despite its applicability . This may be because it formalizes initial stages of measurement and important conditions of measurability but does not stipulate any particular measurement procedures . Another problem concerns establishing measurability because not just any mapping of numbers onto empirical relational structures constitutes measurement. But the appropriateness of particular numerical representations is often only assumed rather than established, thereby reducing the interpretability of the generated symbolic representation regarding the empirical phenomena under study .
Quantitative Research In Psychology
- Brian Stucky – RAND Corporation, USA
Quantitative psychology is a branch of psychology developed using certain methods and approaches which are designed to answer empirical questions, such as the development of measurement models and factor analysis. While quantitative psychology is often associated with the use of statistical models and psychological measurement research methods, this five volume set draws together the key conceptual and methodological techniques and addresses each research question at length. Each volume is accompanied by an introduction which contextualises the subject area, giving an understanding of established theories and how they are continuing to develop in one of the most fundamental and broadly researched psychological fields.
These volumes are an excellent resource for academics and scholars who will benefit from the framing provided by the editorial introduction and overview, and will also appeal to advanced students and professionals studying or using quantitative psychological methods in their research.
Volume One: Statistical hypothesis testing and power
Volume Two: Measurement
Volume Three: Research Design and sampling
Volume Four: Statistical Tests
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Exemplar Essayhow To Write A 8
Evaluate the use of quantitative data in Psychology.
- A 8-mark âevaluateâ question awards 4 marks for describing the use of quantitative data and 4 marks for evaluating it . You need a conclusion to get a mark in the top band . “Quantitative data” turns up in the Specification for the Social and Cognitive Approaches but you need to know how data is used in all the Approaches.
- Notice that for a 8-mark answer you donât have to include everything about quantitative data. I havenât mentioned standard deviation or frequencies or the difference between bar charts and histograms. But it is a balanced answer – half description, half application.
Measures Of Central Tendency
- A mean can be a number that doesn’t exist in the set and this can cause it to make no sense. For example, the average family has 1.9 children, but how can you be 0.9 of a child?
- Means are easily “skewed” by extreme high or extreme low scores called “outliers”. For example, the average wage in the country is yanked up by a few billionaires who are staggeringly rich. If there are outliers, the mean may lack validity.
- Unlike the mean, the mode is always an actual score from the set. For example, the mode is for British families to have 1 child it’s the most common family size.
- Unlike the mean, there could be more than one mode if several different score “tie” for popularity. This can be confusing.
- The mode helps us interpret the mean if mode and mean are similar, it suggests there is standard distribution of data, but if they are far apart it suggests the data is skewed
- The median may or may not be an actual number from the set, depending on whether the set has an odd number of scores and an actual middle number.
- Unlike the mean, the median is not skewed by outliers
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How Is Quantitative Data Collected
Now we know what quantitative data is, we can start to think about how analysts actually work with it in the real world. Before the data can be analyzed, it first needs to be generated or collected. So how is this done?
Researchers will often conduct experiments and studies in order to gather quantitative data and test certain hypotheses. A psychologist investigating the relationship between social media usage and self-esteem might devise a questionnaire with various scalesfor example, asking participants to rate, on a scale of one to five, the extent to which they agree with certain statements. If the survey reaches enough people, the psychologist ends up with a large sample of quantitative data which they can then analyze.
Data analysts and data scientists are less likely to conduct experiments, but they may send out questionnaires and surveysit all depends on the sector theyre working in. Usually, data professionals will work with naturally occurring quantitative data, such as the number of sales per quarter, or how often a customer uses a particular service.
Some common methods of data collection include:
- Analytics tools, such as Google Analytics
- Probability sampling
- Open-source datasets on the web
Quantitative Data Generation With Rating Methods Versus Observational Methods: Possibilities And Limitations For Fulfilling Metrological Criteria
The TPS-Paradigms frameworks and the metrological criteria of scientific quantification are now applied to deconstruct the demands that different methods of quantification impose on data-generating persons, contrasting rating methods with behavioral observations . These elaborations are illustrated by the example of individual-specific behaviors as study phenomena . To be specific to individuals, behavioral patterns must vary among individuals and these differences must be stable over some time . But neither differential nor temporal patterns can be directly perceived at any moment. As behaviors are transient, fluctuating and dynamic, individual behavior patterns cannot be straightforwardly measured either . This considerably complicates quantifications of individual-specific behaviors.
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Training In Quantitative And Measurement Psychology
Since mathematics is the foundation of all statistical analysis, quantitative psychologists must have ample training in this field. While all advanced education in psychology requires some training in statistics, for the quantitative psychologist the mathematical focus is vital and profound. Hence the PhD is considered the optimal degree for this field. There are several dozen universities in North America that offer specific PhD degrees in Quantitative Psychology, which is also called Psychometrics or Psychometric Methodology a number of others offer advanced work in Quantitative Psychology along with a substantial study in other areas of Psychology.
Advantages Of Quantitative Research
Quantitative researchers aim to create a general understanding of behavior and other phenomena across different settings and populations. Quantitative studies are often fast, focused, scientific and relatable.4
The speed and efficiency of the quantitative method are attractive to many researchers. Data computing equipment makes it possible to process and analyze data quickly, even with large sample sizes. Surveys, polls, statistical analysis software and weather thermometers are all examples of instruments used to collect and measure quantitative data.
Researchers can clearly communicate quantitative results using unbiased statistics. When performed correctly, an analysis allows researchers to make predictions and generalizations to larger, more universal populations outside the test sample.1 This is particularly useful in social science research.
Quantitative studies are focused. The goals and design of the study are determined from the beginning, and the research serves to test the initial theory and determine whether it is true or false. Researchers like the control and simplicity.
If well designed, quantitative studies are relatable in the sense that they are designed to make predictions, discover facts and test existing hypotheses. Researchers use these studies to test theories about how or why certain events occur by finding evidence that supports or disproves the theories.
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Problematic Assumptions In Psychometrics
The numerals into which psychometricians rigidly recode raters ticks on the scales do not constitute measurement-based quantifications. Rasch analysis and conjoint measurement, often assumed to enable quantitative measurement with rating data , are only methods for modeling data once they have been generated. These methods show that rating data, as recoded and interpreted by the researchers can exhibit particular quantitative properties . But these properties are obtained through rigorous psychometric variable selection that align the data generation process to statistical assumptions rather than to properties of the actual objects of research, thus precluding object-dependence.
This entails a twofold break in traceability in the triadic interactions involved in human-generated data generationfirst, to raters interpretation and use of the rating scales as methods, and second, to their perceptions and interpretations of the actual phenomena and properties under study. As a consequence, quantitative properties ascertained in psychometric analyses cannot be attributed to the actual referents of the raw data as conceived by the raters who have generated these data .
Relationship With Qualitative Methods
In most physical and biological sciences, the use of either quantitative or qualitative methods is uncontroversial, and each is used when appropriate. In the social sciences, particularly in sociology, social anthropology and psychology, the use of one or other type of method can be a matter of controversy and even ideology, with particular schools of thought within each discipline favouring one type of method and pouring scorn on to the other. The majority tendency throughout the history of social science, however, is to use eclectic approaches-by combining both methods. Qualitative methods might be used to understand the meaning of the conclusions produced by quantitative methods. Using quantitative methods, it is possible to give precise and testable expression to qualitative ideas. This combination of quantitative and qualitative data gathering is often referred to as mixed-methods research.
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What Does A Quantitative Psychologist Do
Quantitative psychologists must be interested in psychology as well as statistics and mathematical models. A quantitative psychologists role is to develop and interpret research that helps psychologists, organizations, nonprofits and governmental agencies more thoroughly understand human behavior.
The development of this research is essential for advancing the field of psychology and ensuring that key stakeholders have accurate information to work with. Those who practice quantitative psychology in this manner usually have no direct contact with patients, but their data helps inform their treatment and outcomes. The specifics of this role will be determined by the industry in which they work.1
Quantitative Methods In Psychology: Inevitable And Useless
- Institute of Psychology, Tallinn University, Tallinn, Estonia
Science begins with questions. Everybody can have questions, and even answers to them. What makes science special is its method of answering questions. Therefore a scientist must ask questions both about the phenomenon to be understood and about the method. There are actually not one or two but four principal questions that should be asked by every scientist when conducting studies :
1. What do I want to know, what is my research question?
2. Why I want to have an answer to this question?
3. With what specific research procedures can I answer my question?
4. Are the answers to three first questions complementary, do they make a coherent theoretically justified whole?
First, there should be a question about some phenomenon that needs an answer. Next, the need for an answer should be justified in science it is quite possible to ask wrong questions, which answers do not help understanding the studied phenomena. Vygotsky gave in his colorful language an ironic example of answering scientifically wrong questions:
One can multiply the number of citizens of Paraguay with the number of versts from Earth to Sun and divide the result with the average length of life of an elephant and conduct this whole operation without a flaw even in one number and yet the number found in the operation can confuse anybody who would like to know the national income of that country
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Why Quantitative Psychology Is Important
All sciences depend upon the accurate analysis of data in order to derive results that are verifiable. As one of the social sciences, psychology is no exception to this rule. For every experiment involving the collection and study of psychological data, an appropriate method of analysis must be developed. Thats the job of the quantitative and measurement psychologist. The quantitative psychologist can design measurement tools that are reliable and accurate these may include various kinds of tests and studies that can be shown to produce solid results. The quantitative psychologist can help develop and focus a proposed research project in such a way that it produces valid results able to be generalized for a larger population, according to the American Psychological Association.
A well-known example of the kind of methodology developed by quantitative psychologists for the use of other researchers is the Intelligence Quotient and the tests designed to measure it. The information acquired by psychologists through IQ testing has been applied in a number of areas outside psychology, including education, health and medicine, and criminal justice.
Major Tasks Still Laying Ahead
As interpretations of rating scales are based on everyday knowledge with its fuzzy and flexible categories, any interpretation of rating data can appear plausible . But the purpose of scientific measurement is to quantify phenomena in the real worldnot to construe a possible match with data that can be generated even in absence of the persons, phenomena and properties under study. Therefore, traceability is a fundamental requirement for scientific quantification that should be implemented systematically also in the methods used to generate quantitative data in psychology and the social sciences. This article started to elaborate some principles by which this can be achieved.
Psychologists and social scientists must finally investigate how people actually understand and use rating scales to generate quantitative data in research and applied contexts. Exploring raters mental processes and the meanings they attribute to items and scale categories is key to specifying the representational systems underlying rating data, which, in many fields, make up much of the current empirical data basis.
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