What Are The Types Of Quantitative Research – Throughout our lives, we are exposed to many factors that can affect our health and well-being. But what kinds of factors influence specific health outcomes? And what should we do when we get sick? Healthcare researchers spend their working lives answering a variety of clinical research questions, and they do this by conducting specific studies. The findings of these studies will ultimately lead to the development of programs that can help save lives.
Table adapted from: Grimes, D.A., & Schulz, K.F. (2002). “Clinical Research Overview: Laying the Ground”. Lancet, 359(9300), 57-61
What Are The Types Of Quantitative Research
In clinical research, study design is the plan for selecting research subjects and collecting data. These study designs fall into two different categories:
Methods Of Data Collection In Quantitative Research (biostatistik)
Investigators determine the level of effect (for example, trials to test the effectiveness of a new drug), and these trials are classified as randomized (comparative or controlled studies) and randomized trials (study without a control group).
These studies focus on exposures that are already present in the population and assess the impact of the exposure on this group. These studies are divided into analytical and descriptive.
Not all types of research are appropriate for answering a specific clinical question. For example, if you want to investigate the effects of maternal smoking on fetal development, a randomized controlled trial would not be appropriate because identifying a disease or possibly exposing a person to an ethical harm is not appropriate. In this case, an observational study is more appropriate.
In this series of lessons, we will look at each type of study design, the types of clinical questions they investigate, and the methods used in each study design. A valuable tool for problem solving.
Quantitative Research And Surveys
Information is all around us and is becoming more and more important. Different types of data define more and more of our interactions with the world around us – from internet usage, car purchases, to the algorithms behind the news channels we watch. .
One of the most common and well-known types of data is quantitative data, or data represented by numbers or numerical values.
This guide goes into detail about what quantitative data is, what it can be used for, how it is collected, its advantages and disadvantages, and more.
Quantitative data is data that can be counted or measured – in other words, quantified – and given a numerical value.
Qualitative Research And Quantitative Research
Quantitative data is used when the researcher needs to quantify and answer questions such as “what,” “how much,” and “how often.” This type of data is often used in mathematical calculations, algorithms or statistical analysis.
In product management, UX design or software engineering, quantitative data can be product acceptance rate (percentage), conversion (number) or page load speed (per unit of time). ) or other indicators. In the context of shopping, quantitative data might be the number of customers who bought a certain item. When it comes to vehicles, the quantitative data can be the capacity of the vehicle.
Quantitative data is anything that can be counted with specific units and numbers. Thus, among many others, some examples of quantitative data include:
There are many differences between qualitative and quantitative data – each represents a very different set of data and is used in different situations. Often, they are also used together to provide comprehensive insights.
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As we have described, quantitative data includes numbers; it can be counted or measured unconditionally. On the other hand, qualitative data is descriptive data expressed in words or pictures. So, when quantitative data is used for statistical analysis, qualitative data is grouped by topic.
However, qualitative information is expressed by describing or labeling certain adjectives, such as “chocolate milk”, “blue eyes” and “red flowers”. In these examples, the adjectives chocolate, blue, and red are qualitative information because they tell us about objects that cannot be quantified.
Quantitative data consists of numerical values and has numerical properties and can easily perform operations such as addition and subtraction. The nature of quantitative data means that its validity can be verified and evaluated using mathematical methods.
All quantitative data can be measured numerically as shown above. But this type of data can also be divided into specific categories.
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There are two types of quantitative data: discrete and continuous. Continuous data can be divided into interval data and scale data.
As for quantitative data, discrete data is data that can only take certain fixed values. Although discrete data does not have to be represented by integers, there are restrictions on how they can be represented.
Discrete data is often best represented using a chart, graph, or bar chart like the one shown below.
On the other hand, persistent data can take any value and change over time. This type of data can be endlessly and meaningfully broken down into smaller and smaller chunks.
The Complete Guide To Quantitative Market Research
Because data is constantly changing over time, data insights are best represented by a line chart or grouped into categories as shown below.
Interval data is data that can be measured along a continuum where there is an equal and significant distance between each point on the scale. Spacing data is always numerically expressed as the normal and equal distance between two points. These numbers are called whole numbers.
Scale data has all the properties of interval data, but unlike interval data, scale data also has real zeros. For example, weight in grams is a type of scale data because it is measured on a continuous scale with equal distances between each value and the scale starts at 0.0.
Ratio data is so named because the ratio of two measurements can be effectively interpreted, while the two measurements cannot be directly compared with intervals.
Chapter 4: Quantitative Methods (part 1)
For example, a 6-pound object weighs twice as much as a 3-pound object. However, this rule does not apply to zero-interval data. For example, an SAT score of 700 is not twice as good as an SAT score of 350 because the scale does not start at zero.
Likewise, 40º is not twice as hot as 20º. It is said that 0º is incorrect as a reference point for comparing two temperatures.
Both qualitative and quantitative research can yield valuable findings, but choosing the type of data to collect is important based on the nature and objectives of your research.
If what you want to study or measure can be counted and expressed in numbers, then quantitative research is probably the best fit. For example, quantitative methods are used to calculate the demographics of a city – how many people live there, age, nationality, income, etc.
Choosing Between Quantitative Vs Qualitative Research
Qualitative data is defined as non-digital data such as language, text, video, voice recordings and photographs. This data can be collected through qualitative research and methods such as interviews, survey questions, observations, focus groups or diary accounts.
Conducting qualitative research involves the collection, analysis, and interpretation of non-quantitative qualitative data (such as color, flavor, or any other descriptive aspect). Qualitative analysis methods include thematic analysis, coding, and content analysis.
If what you want to understand is subjective or measured on a scale, you will need to conduct qualitative research and qualitative analysis.
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To use the city example above, determining why the population of the city is happy or unhappy that you need to describe them requires qualitative data.
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Conclusion: The goal of qualitative research is to understand how people perceive their social reality. It is commonly used in fields such as psychology, social sciences and sociology, education studies, anthropology, political science, etc.
In some cases, such as when trying to understand why users leave your site, it is useful to evaluate both quantitative and qualitative data. Understanding what users are doing on your site as well as why they are doing it (or when they feel like doing it) will give you the information you need to improve your site experience.
Quantitative data is most useful when trying to understand something that can be counted and expressed numerically.
There are many ways to collect quantitative data using common methods, including surveys and questionnaires. They can generate both quantitative and qualitative data depending on the questions asked.
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Once data is collected and analyzed, it can be used to examine patterns, predict the future, and draw conclusions.
For example, a survey of 100 consumers about where they plan to shop for the holidays shows that 45 of them plan to shop online and the remaining 55 plan to shop in-store.
Surveys and questionnaires are commonly used in quantitative and qualitative research because they are both efficient and relatively easy to create and distribute. With a number of easy-to-use tools, conducting online surveys is a quick and convenient method of research.
These types of surveys are useful for gathering in-depth feedback from users and customers, especially for understanding how people feel about a certain product, service, or experience. For example, many e-commerce companies send out post-purchase surveys to find out how customers feel about the transaction — and if there are any areas they could improve on.