Research data can be placed into two broad categories: quantitative or qualitative. .
Quantitative data are used when a researcher is trying to quantify a problem, or address the "what" or "how many" aspects of a research question. It is data that can either be counted or compared on a numeric scale. For example, it could be the number of first year students at Macalester, or the ratings on a scale of 1-4 of the quality of food served at Cafe Mac. This data are usually gathered using instruments, such as a questionnaire which includes a ratings scale or a thermometer to collect weather data. Statistical analysis software, such as SPSS, is often used to analyze quantitative data.
Qualitative data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form. For example, it could be notes taken during a focus group on the quality of the food at Cafe Mac, or responses from an open-ended questionnaire. Qualitative data may be difficult to precisely measure and analyze. The data may be in the form of descriptive words that can be examined for patterns or meaning, sometimes through the use of coding. Coding allows the researcher to categorize qualitative data to identify themes that correspond with the research questions and to perform quantitative analysis.
Research topics may be approached using either quantitative or qualitative methods. Choosing one method or the other depends on what you believe would provide the best evidence for your research objectives. Researchers sometimes choose to incorporate both qualitative and quantitative data in their research since these methods provide different perspectives on the topic. : You want to know the locations of the most popular study spaces on Macalester's campus, and why they are so popular. To identify the most popular spaces, you might count the number of students studying in different locations at regular time intervals over a period of days or weeks. This quantitative data would answer the question of how many people study at different locations on campus. To understand why certain locations are more popular than others, you might use a survey to ask students why they prefer these locations. This is qualitative data. |
Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data.
What is qualitative research, what is quantitative research.
Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university.
Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief.
Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair.
Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies.
Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews.
These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article.
Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure.
How are the approaches of quantitative and qualitative research different?
In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated.
Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis.
Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research.
Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further.
Some example methods of quantitative research methods or sources include, but are not limited to, the following:
The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches.
Researchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples:
Qualitative research questions:.
These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis.
In addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.”
If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach.
When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers?
Below is a step-by-step overview of how to analyze qualitative data.
There are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats.
Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview.
Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology.
Qualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision:
Quantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study.
If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills.
With flexible course schedules, convenient online classes , scholarships and financial aid , and an inclusive military-friendly culture, higher education has never been more achievable or accessible. At National University, you’ll find opportunities to challenge and hone your research skills in more than 75 accredited graduate and undergraduate programs and fast-paced credential and certificate programs in healthcare, business, engineering, computer science, criminal justice, sociology, accounting, and more.
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No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.
Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.
Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.
Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.
Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.
Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.
Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.
Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.
An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.
Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.
On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.
When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.
For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.
It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.
In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.
Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .
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American University, EdD in Education Policy and Leadership
Edutopia, “2019 Education Research Highlights”
Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”
iMotion, “Qualitative vs. Quantitative Research: What Is What?”
Scribbr, “Qualitative vs. Quantitative Research”
Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”
Typeform, “A Simple Guide to Qualitative and Quantitative Research”
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Coghlan, D., Brydon-Miller, M. (2014). The SAGE encyclopedia of action research (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406
What is the purpose of quantitative research?
The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.
Allen, M. (2017). The SAGE encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411
How do I know if the study is a quantitative design? What type of quantitative study is it?
Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?
Studies do not always explicitly state what kind of research design is being used. You will need to know how to decipher which design type is used. The following video will help you determine the quantitative design type.
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While quantitative research is based on numbers and mathematical calculations (aka quantitative data ), qualitative research is based on written or spoken narratives (or qualitative data ). Qualitative and quantitative research techniques are used in marketing , sociology , psychology , public health and various other disciplines.
Qualitative | Quantitative | |
---|---|---|
Purpose | The purpose is to explain and gain insight and understanding of phenomena through intensive collection of narrative data Generate hypothesis to be test , inductive. | The purpose is to explain, predict, and/or control phenomena through focused collection of numerical data. Test hypotheses, deductive. |
Approach to Inquiry | subjective, holistic, process- oriented | Objective, focused, outcome- oriented |
Hypotheses | Tentative, evolving, based on particular study | Specific, testable, stated prior to particular study |
Research Setting | Controlled setting not as important | Controlled to the degree possible |
Sampling | Purposive: Intent to select “small, ” not necessarily representative, sample in order to get in-depth understanding | Random: Intent to select “large, ” representative sample in order to generalize results to a population |
Measurement | Non-standardized, narrative (written word), ongoing | Standardized, numerical (measurements, numbers), at the end |
Design and Method | Flexible, specified only in general terms in advance of study Nonintervention, minimal disturbance All Descriptive— History, Biography, Ethnography, Phenomenology, Grounded Theory, Case Study, (hybrids of these) Consider many variable, small group | Structured, inflexible, specified in detail in advance of study Intervention, manipulation, and control Descriptive Correlation Causal-Comparative Experimental Consider few variables, large group |
Data Collection Strategies | Document and artifact (something observed) that is collection (participant, non-participant). Interviews/Focus Groups (un-/structured, in-/formal). Administration of questionnaires (open ended). Taking of extensive, detailed field notes. | Observations (non-participant). Interviews and Focus Groups (semi-structured, formal). Administration of tests and questionnaires (close ended). |
Data Analysis | Raw data are in words. Essentially ongoing, involves using the observations/comments to come to a conclusion. | Raw data are numbers Performed at end of study, involves statistics (using numbers to come to conclusions). |
Data Interpretation | Conclusions are tentative (conclusions can change), reviewed on an ongoing basis, conclusions are generalizations. The validity of the inferences/generalizations are the reader’s responsibility. | Conclusions and generalizations formulated at end of study, stated with predetermined degree of certainty. Inferences/generalizations are the researcher’s responsibility. Never 100% certain of our findings. |
Qualitative research gathers data that is free-form and non-numerical, such as diaries, open-ended questionnaires, interviews and observations that are not coded using a numerical system.
On the other hand, quantitative research gathers data that can be coded in a numerical form. Examples of quantitative research include experiments or interviews/questionnaires that used closed questions or rating scales to collect information .
Qualitative data and research is used to study individual cases and to find out how people think or feel in detail. It is a major feature of case studies.
Quantitative data and research is used to study trends across large groups in a precise way. Examples include clinical trials or censuses.
Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well. It is hard, for example, to conduct in-depth interviews with thousands of people or to analyze their responses to open-ended questions. But it is relatively easier to analyze survey responses from thousands of people if the questions are closed-ended and responses can be mathematically encoded in, say, rating scales or preference ranks.
Conversely, qualitative research shines when it is not possible to come up with closed-ended questions. For example, marketers often use focus groups of potential customers to try and gauge what influences brand perception, product purchase decisions, feelings and emotions . In such cases, researchers are usually at very early stages of forming their hypotheses and do not want to limit themselves to their initial understanding. Qualitative research often opens up new options and ideas that quantitative research cannot due to its closed-ended nature.
Qualitative data can be difficult to analyze, especially at scale, as it cannot be reduced to numbers or used in calculations. Responses may be sorted into themes, and require an expert to analyze. Different researchers may draw different conclusions from the same qualitative material.
Quantitative data can be ranked or put into graphs and tables to make it easier to analyze.
Data is being generated at an increasing rate because of the expansion in the number of computing devices and the growth of the Internet . Most of this data is quantitative and special tools and techniques are evolving to analyze this " big data ".
The following diagram illustrates the effects of positive and negative feedback on Qualitative vs Quantitative research:
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When undertaking any type of research study, the data collected will fall into one of two categories: qualitative or quantitative. But what exactly is the difference between these two data types and research methodologies?
Put simply, quantitative data deals with numbers, objective facts and measurable statistics. For example, quantitative data provides specifics on values like website traffic metrics, sales figures, survey response rates, operational costs, etc.
Qualitative data , on the other hand, reveals deeper insights into people‘s subjective perspectives, experiences, beliefs and behaviors. Instead of numbers, qualitative findings are expressed through detailed observations, interviews, focus groups and more.
Now let‘s explore both types of research to understand how and when to apply these methodologies.
The purpose of qualitative research is to comprehend human behaviors, opinions, motivations and tendencies through an in-depth exploratory approach. Qualitative studies generally seek to answer "why" and "how" questions to uncover deeper meaning and patterns.
Some examples of popular qualitative methods include:
The benefit of these techniques is collecting elaborate and descriptive qualitative data based on personal experiences rather than just objective facts and figures. This reveals not just what research participants are doing but more importantly, why they think, feel and act in certain ways.
For example, an open-ended survey may find that 52% of respondents felt "happy" about using a particular smartphone brand. But in-depth interviews would help uncover exactly why they feel this way by collecting descriptive details on their user experience.
In essence, qualitative techniques like interviews and ethnographic studies add crucial context . This allows us to delve deeper into research problems to gain meaningful insights.
Unlike qualitative methods, quantitative research relies primarily on the collection and analysis of objective, measurable numerical data. This structured empirical evidence is then manipulated using statistical, graphical and mathematical techniques to derive patterns, trends and conclusions.
Some examples of quantitative methods include:
For instance, an online survey may find that 74% of respondents rate a particular laptop 4 or higher on a 5-point scale for quality. Or an experiment might determine that a revised checkout process increases e-commerce conversion rates by 14.5%.
The benefit of quantitative data is that it generates hard numbers and statistics that allow objective measurement and comparison between groups or changes over time. But the limitation is it lacks detailed insights into the subjective reasons and context behind the data.
Qualitative | Quantitative |
---|---|
Textual data | Numerical data |
In-depth insights | Hard facts/stats |
Subjective | Objective |
Detailed contexts | Generalizable data |
Explores "why/how" | Tests "what/when" |
Interviews, focus groups | Surveys, analytics |
Qualitative and quantitative methodologies have differing strengths and limitations. Expert researchers argue both approaches play an invaluable role when combined effectively .
Qualitative research allows rich exploration of perceptions, motivations and ideas through open-ended inquiry. This generates impactful insights but typically with smaller sample sizes focused on depth over breadth.
Quantitative statistically analyzes empirical evidence to uncover patterns and test hypotheses. This lends generalizable support to relationships between variables but risks losing contextual qualitative detail.
In short, qualitative informs the human perspectives while quantitative informs the overarching trends. Together this approaches a problem from both a granular and big-picture level for robust conclusions.
Mixing qualitative and quantitative techniques leverages the strengths while minimizing the weaknesses of both approaches. This integration can happen sequentially in phases or concurrently in parallel strands:
Sequential Mixed Methods
Concurrent Mixed Methods
This intermixing provides corroboration between subjective qualitative themes and hard quantitative figures to produce actionable insights.
Let‘s look at two examples of effective mixed methods research approaches.
Hospital patient experience analysis.
A hospital administrator seeks to improve patient satisfaction rates.
Quantitative Data
Qualitative Data
Combined Analysis
Statistical survey analysis coupled with patient interview narratives provides a robust perspective into precisely which issues most critically impact patient experience and what solutions may have the greatest impact.
A technology company designs a new smartphone app prototype.
Metrics prove what features customers interact with most while qualitative findings explain why they choose to use or abandon certain app functions. This drives effective product refinement.
As demonstrated, thoughtfully blending quantitative and qualitative techniques can provide powerful multifaceted insights.
Qualitative and quantitative research encompass differing but complementary methodological paradigms for understanding our world through data.
Qualitative research allows inquiry into the depths of human complexities – perceptions, stories, symbols and meanings. Meanwhile, quantitative methods enable us to zoom out and systematically analyze empirical patterns.
Leveraging both modes of discovery provides a nuanced perspective for unlocking insights. As analyst John Tukey noted, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data."
Rather than blindly following statistics alone, factoring in qualitative details allows us to carefully interpret the context and meaning behind the numbers.
In closing, elegantly integrating quantitative precision with qualitative awareness offers a multilayered lens for conducting research and driving data-savvy decisions.
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This guide will introduce the types of data analysis used in quantitative research, then discuss relevant examples and applications in the finance industry.
Table of Contents
What is quantitative data analysis and what is it for .
Quantitative data analysis is the process of interpreting meaning and extracting insights from numerical data , which involves mathematical calculations and statistical reviews to uncover patterns, trends, and relationships between variables.
Beyond academic and statistical research, this approach is particularly useful in the finance industry. Financial data, such as stock prices, interest rates, and economic indicators, can all be quantified with statistics and metrics to offer crucial insights for informed investment decisions. To illustrate this, here are some examples of what quantitative data is usually used for:
Ultimately, quantitative data analysis helps investors navigate the complex financial landscape and pursue profitable opportunities.
Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data , like text, images, or audio recordings to gain a deeper understanding of experiences, opinions, and motivations. Here’s a table summarizing its key differences between quantitative data analysis:
Types of Data Used | Numerical data: numbers, percentages, etc. | Non-numerical data: text, images, audio, narratives, etc |
Perspective | More objective and less prone to bias | More subjective as it may be influenced by the researcher’s interpretation |
Data Collection | Closed-ended questions, surveys, polls | Open-ended questions, interviews, observations |
Data Analysis | Statistical methods, numbers, graphs, charts | Categorization, thematic analysis, verbal communication |
Focus | and | and |
Best Use Case | Measuring trends, comparing groups, testing hypotheses | Understanding user experience, exploring consumer motivations, uncovering new ideas |
Due to their characteristics, quantitative analysis allows you to measure and compare large datasets; while qualitative analysis helps you understand the context behind the data. In some cases, researchers might even use both methods together for a more comprehensive understanding, but we’ll mainly focus on quantitative analysis for this article.
Once you have your data collected, you have to use descriptive statistics or inferential statistics analysis to draw summaries and conclusions from your raw numbers.
As its name suggests, the purpose of descriptive statistics is to describe your sample . It provides the groundwork for understanding your data by focusing on the details and characteristics of the specific group you’ve collected data from.
On the other hand, inferential statistics act as bridges that connect your sample data to the broader population you’re truly interested in, helping you to draw conclusions in your research. Moreover, choosing the right inferential technique for your specific data and research questions is dependent on the initial insights from descriptive statistics, so both of these methods usually go hand-in-hand.
With sophisticated descriptive statistics, you can detect potential errors in your data by highlighting inconsistencies and outliers that might otherwise go unnoticed. Additionally, the characteristics revealed by descriptive statistics will help determine which inferential techniques are suitable for further analysis.
One of the key statistical tests used for descriptive statistics is central tendency . It consists of mean, median, and mode, telling you where most of your data points cluster:
Another statistic to test in descriptive analysis is the measures of dispersion , which involves range and standard deviation, revealing how spread out your data is relative to the central tendency measures:
The shape of the distribution will then be measured through skewness.
While the core measures mentioned above are fundamental, there are additional descriptive statistics used in specific contexts, including percentiles and interquartile range.
Let’s illustrate these concepts with a real-world example. Imagine a financial advisor analyzing a client’s portfolio. They have data on the client’s various holdings, including stock prices over the past year. With descriptive statistics they can obtain the following information:
By calculating these descriptive statistics, the advisor gains a quick understanding of the client’s portfolio performance and risk distribution. For instance, they could use correlation analysis to see if certain stock prices tend to move together, helping them identify expansion opportunities within the portfolio.
While descriptive statistics provide a foundational understanding, they should be followed by inferential analysis to uncover deeper insights that are crucial for making investment decisions.
Inferential statistics analysis is particularly useful for hypothesis testing , as you can formulate predictions about group differences or potential relationships between variables , then use statistical tests to see if your sample data supports those hypotheses.
However, the power of inferential statistics hinges on one crucial factor: sample representativeness . If your sample doesn’t accurately reflect the population, your predictions won’t be very reliable.
Here are some of the commonly used tests for inferential statistics in commerce and finance, which can also be integrated to most analysis software:
If you’re a financial analyst studying the historical performance of a particular stock, here are some predictions you can make with inferential statistics:
Understanding these inferential analysis techniques can help you uncover potential relationships and group differences that might not be readily apparent from descriptive statistics alone. Nonetheless, it’s important to remember that each technique has its own set of assumptions and limitations . Some methods are designed for parametric data with a normal distribution, while others are suitable for non-parametric data.
Now that we have discussed the types of data analysis techniques used in quantitative research, here’s a quick guide to help you choose the right method and grasp the essential steps of quantitative data analysis.
Choosing between all these quantitative analysis methods may seem like a complicated task, but if you consider the 2 following factors, you can definitely choose the right technique:
The data used in quantitative analysis can be categorized into two types, discrete data and continuous data, based on how they’re measured. They can also be further differentiated by their measurement scale. The four main types of measurement scales include: nominal, ordinal, interval or ratio. Understanding the distinctions between them is essential for choosing the appropriate statistical methods to interpret the results of your quantitative data analysis accurately.
Discrete data , which is also known as attribute data, represents whole numbers that can be easily counted and separated into distinct categories. It is often visualized using bar charts or pie charts, making it easy to see the frequency of each value. In the financial world, examples of discrete quantitative data include:
Discrete data usually use nominal or ordinal measurement scales, which can be then quantified to calculate their mode or median. Here are some examples:
Conversely, continuous data can take on any value and fluctuate over time. It is usually visualized using line graphs, effectively showcasing how the values can change within a specific time frame. Examples of continuous data in the financial industry include:
Source: Freepik
The measurement scale for continuous data is usually interval or ratio . Here is breakdown of their differences:
You also need to make sure that the analysis method aligns with your specific research questions. If you merely want to focus on understanding the characteristics of your data set, descriptive statistics might be all you need; if you need to analyze the connection between variables, then you have to include inferential statistics as well.
Step 1: data collection .
Depending on your research question, you might choose to conduct surveys or interviews. Distributing online or paper surveys can reach a broad audience, while interviews allow for deeper exploration of specific topics. You can also choose to source existing datasets from government agencies or industry reports.
Raw data might contain errors, inconsistencies, or missing values, so data cleaning has to be done meticulously to ensure accuracy and consistency. This might involve removing duplicates, correcting typos, and handling missing information.
Furthermore, you should also identify the nature of your variables and assign them appropriate measurement scales , it could be nominal, ordinal, interval or ratio. This is important because it determines the types of descriptive statistics and analysis methods you can employ later. Once you categorize your data based on these measurement scales, you can arrange the data of each category in a proper order and organize it in a format that is convenient for you.
Based on the measurement scales of your variables, calculate relevant descriptive statistics to summarize your data. This might include measures of central tendency (mean, median, mode) and dispersion (range, standard deviation, variance). With these statistics, you can identify the pattern within your raw data.
Then, these patterns can be analyzed further with inferential methods to test out the hypotheses you have developed. You may choose any of the statistical tests mentioned above, as long as they are compatible with the characteristics of your data.
Now that you have the results from your statistical analysis, you may draw conclusions based on the findings and incorporate them into your business strategies. Additionally, you should also transform your findings into clear and shareable information to facilitate discussion among stakeholders. Visualization techniques like tables, charts, or graphs can make complex data more digestible so that you can communicate your findings efficiently.
We’ve compiled some commonly used quantitative data analysis tools and software. Choosing the right one depends on your experience level, project needs, and budget. Here’s a brief comparison:
Easiest | Beginners & basic analysis | One-time purchase with Microsoft Office Suite | |
Easy | Social scientists & researchers | Paid commercial license | |
Easy | Students & researchers | Paid commercial license or student discounts | |
Moderate | Businesses & advanced research | Paid commercial license | |
Moderate | Researchers & statisticians | Paid commercial license | |
Moderate (Coding optional) | Programmers & data scientists | Free & Open-Source | |
Steep (Coding required) | Experienced users & programmers | Free & Open-Source | |
Steep (Coding required) | Scientists & engineers | Paid commercial license | |
Steep (Coding required) | Scientists & engineers | Paid commercial license |
So how does this all affect the finance industry? Quantitative finance (or quant finance) has become a growing trend, with the quant fund market valued at $16,008.69 billion in 2023. This value is expected to increase at the compound annual growth rate of 10.09% and reach $31,365.94 billion by 2031, signifying its expanding role in the industry.
Quant finance is the process of using massive financial data and mathematical models to identify market behavior, financial trends, movements, and economic indicators, so that they can predict future trends.These calculated probabilities can be leveraged to find potential investment opportunities and maximize returns while minimizing risks.
There are several common quantitative strategies, each offering unique approaches to help stakeholders navigate the market:
This strategy aims for high returns with low volatility. It employs sophisticated algorithms to identify minuscule price discrepancies across the market, then capitalize on them at lightning speed, often generating short-term profits. However, its reliance on market efficiency makes it vulnerable to sudden market shifts, posing a risk of disrupting the calculations.
This strategy identifies and invests in assets based on factors like value, momentum, or quality. By analyzing these factors in quantitative databases , investors can construct portfolios designed to outperform the broader market. Overall, this method offers diversification and potentially higher returns than passive investing, but its success relies on the historical validity of these factors, which can evolve over time.
This approach prioritizes portfolio balance above all else. Instead of allocating assets based on their market value, risk parity distributes them based on their risk contribution to achieve a desired level of overall portfolio risk, regardless of individual asset volatility. Although it is efficient in managing risks while potentially offering positive returns, it is important to note that this strategy’s complex calculations can be sensitive to unexpected market events.
Quant analysts are beginning to incorporate these cutting-edge technologies into their strategies. Machine learning algorithms can act as data sifters, identifying complex patterns within massive datasets; whereas AI goes a step further, leveraging these insights to make investment decisions, essentially mimicking human-like decision-making with added adaptability. Despite the hefty development and implementation costs, its superior risk-adjusted returns and uncovering hidden patterns make this strategy a valuable asset.
Advantages of quantitative data analysis, minimum bias for reliable results.
Quantitative data analysis relies on objective, numerical data. This minimizes bias and human error, allowing stakeholders to make investment decisions without emotional intuitions that can cloud judgment. In turn, this offers reliable and consistent results for investment strategies.
Quantitative analysis generates precise numerical results through statistical methods. This allows accurate comparisons between investment options and even predictions of future market behavior, helping investors make informed decisions about where to allocate their capital while managing potential risks.
By analyzing large datasets and identifying patterns, stakeholders can generalize the findings from quantitative analysis into broader populations, applying them to a wider range of investments for better portfolio construction and risk management
Quantitative research is more suited to analyze large datasets efficiently, letting companies save valuable time and resources. The softwares used for quantitative analysis can automate the process of sifting through extensive financial data, facilitating quicker decision-making in the fast-paced financial environment.
Limited scope .
By focusing on numerical data, quantitative analysis may provide a limited scope, as it can’t capture qualitative context such as emotions, motivations, or cultural factors. Although quantitative analysis provides a strong starting point, neglecting qualitative factors can lead to incomplete insights in the financial industry, impacting areas like customer relationship management and targeted marketing strategies.
Breaking down complex phenomena into numerical data could cause analysts to overlook the richness of the data, leading to the issue of oversimplification. Stakeholders who fail to understand the complexity of economic factors or market trends could face flawed investment decisions and missed opportunities.
In conclusion, quantitative data analysis offers a deeper insight into market trends and patterns, empowering you to make well-informed financial decisions. However, collecting comprehensive data and analyzing them can be a complex task that may divert resources from core investment activity.
As a reliable provider, TEJ understands these concerns. Our TEJ Quantitative Investment Database offers high-quality financial and economic data for rigorous quantitative analysis. This data captures the true market conditions at specific points in time, enabling accurate backtesting of investment strategies.
Furthermore, TEJ offers diverse data sets that go beyond basic stock prices, encompassing various financial metrics, company risk attributes, and even broker trading information, all designed to empower your analysis and strategy development. Save resources and unlock the full potential of quantitative finance with TEJ’s data solutions today!
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Data analysis in qualitative research, theertha raj, august 30, 2024.
While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.
This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.
Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video.
This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.
A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews
Qualitative data is vital for several reasons:
Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.
This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.
Qualitative and quantitative data analyses differ in several key ways:
The three main components of qualitative data analysis are:
These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.
Step 1: organize your data.
Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.
Immerse yourself in the data. Read through everything multiple times.
Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.
Pick out direct quotes from your data to illustrate key points.
Explain what your results mean in the context of your research and existing literature.
Als discuss, identify and try to eliminate potential biases or limitations in your analysis.
Summarize main insights and their implications.
Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.
Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.
Grounded Theory Developing theory from data through iterative coding and analysis.
Discourse Analysis Examining language use and meaning in social contexts.
Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.
Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.
When it comes to collecting qualitative data, researchers primarily rely on four methods.
Researchers often use multiple methods to gain a comprehensive understanding of their topic.
Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria.
Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor.
Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences.
Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.
The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions.
By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context.
Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.
Researchers face several challenges when conducting qualitative data analysis.
Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult.
There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge.
The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.
G2 rating: 4.6/5
Pricing: Starts at $30 monthly.
Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job.
Here’s how Looppanel’s features can help with qualitative data analysis:
Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.
G2 rating: 4.7/5
Pricing: Free version available, with the Plus version costing $20 monthly.
ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:
ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.
ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:
G2 rating: 4.7/5 Pricing: Custom
Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.
It’s key data analysis features include:
G2 rating: 4.1/5 Pricing: Custom
NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:
NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.
Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.
Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.
There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps.
Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect?
Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.
Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.
Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.
A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.
First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions.
NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can.
While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.
You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.
Here’s a guide that can help you decide.
First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.
NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.
While qualitative data analysis is often iterative, it generally follows these four main steps:
1. Data Collection: Gathering raw data through interviews, observations, or documents.
2. Data Preparation: Organizing and transcribing the collected data.
3. Data Coding: Identifying and labeling important concepts or themes in the data.
4. Interpretation: Drawing meaning from the coded data and developing insights.
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Speaker 1: Welcome to this overview of quantitative research methods. This tutorial will give you the big picture of quantitative research and introduce key concepts that will help you determine if quantitative methods are appropriate for your project study. First, what is educational research? Educational research is a process of scholarly inquiry designed to investigate the process of instruction and learning, the behaviors, perceptions, and attributes of students and teachers, the impact of institutional processes and policies, and all other areas of the educational process. The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes. Quantitative research differs from qualitative research in several ways. It works to achieve different goals and uses different methods and design. This table illustrates some of the key differences. Qualitative research generally uses a small sample to explore and describe experiences through the use of thick, rich descriptions of detailed data in an attempt to understand and interpret human perspectives. It is less interested in generalizing to the population as a whole. For example, when studying bullying, a qualitative researcher might learn about the experience of the victims and the experience of the bully by interviewing both bullies and victims and observing them on the playground. Quantitative studies generally use large samples to test numerical data by comparing or finding correlations among sample attributes so that the findings can be generalized to the population. If quantitative researchers were studying bullying, they might measure the effects of a bully on the victim by comparing students who are victims and students who are not victims of bullying using an attitudinal survey. In conducting quantitative research, the researcher first identifies the problem. For Ed.D. research, this problem represents a gap in practice. For Ph.D. research, this problem represents a gap in the literature. In either case, the problem needs to be of importance in the professional field. Next, the researcher establishes the purpose of the study. Why do you want to do the study, and what do you intend to accomplish? This is followed by research questions which help to focus the study. Once the study is focused, the researcher needs to review both seminal works and current peer-reviewed primary sources. Based on the research question and on a review of prior research, a hypothesis is created that predicts the relationship between the study's variables. Next, the researcher chooses a study design and methods to test the hypothesis. These choices should be informed by a review of methodological approaches used to address similar questions in prior research. Finally, appropriate analytical methods are used to analyze the data, allowing the researcher to draw conclusions and inferences about the data, and answer the research question that was originally posed. In quantitative research, research questions are typically descriptive, relational, or causal. Descriptive questions constrain the researcher to describing what currently exists. With a descriptive research question, one can examine perceptions or attitudes as well as more concrete variables such as achievement. For example, one might describe a population of learners by gathering data on their age, gender, socioeconomic status, and attributes towards their learning experiences. Relational questions examine the relationship between two or more variables. The X variable has some linear relationship to the Y variable. Causal inferences cannot be made from this type of research. For example, one could study the relationship between students' study habits and achievements. One might find that students using certain kinds of study strategies demonstrate greater learning, but one could not state conclusively that using certain study strategies will lead to or cause higher achievement. Causal questions, on the other hand, are designed to allow the researcher to draw a causal inference. A causal question seeks to determine if a treatment variable in a program had an effect on one or more outcome variables. In other words, the X variable influences the Y variable. For example, one could design a study that answered the question of whether a particular instructional approach caused students to learn more. The research question serves as a basis for posing a hypothesis, a predicted answer to the research question that incorporates operational definitions of the study's variables and is rooted in the literature. An operational definition matches a concept with a method of measurement, identifying how the concept will be quantified. For example, in a study of instructional strategies, the hypothesis might be that students of teachers who use Strategy X will exhibit greater learning than students of teachers who do not. In this study, one would need to operationalize learning by identifying a test or instrument that would measure learning. This approach allows the researcher to create a testable hypothesis. Relational and causal research relies on the creation of a null hypothesis, a version of the research hypothesis that predicts no relationship between variables or no effect of one variable on another. When writing the hypothesis for a quantitative question, the null hypothesis and the research or alternative hypothesis use parallel sentence structure. In this example, the null hypothesis states that there will be no statistical difference between groups, while the research or alternative hypothesis states that there will be a statistical difference between groups. Note also that both hypothesis statements operationalize the critical thinking skills variable by identifying the measurement instrument to be used. Once the research questions and hypotheses are solidified, the researcher must select a design that will create a situation in which the hypotheses can be tested and the research questions answered. Ideally, the research design will isolate the study's variables and control for intervening variables so that one can be certain of the relationships being tested. In educational research, however, it is extremely difficult to establish sufficient controls in the complex social settings being studied. In our example of investigating the impact of a certain instructional strategy in the classroom on student achievement, each day the teacher uses a specific instructional strategy. After school, some of the students in her class receive tutoring. Other students have parents that are very involved in their child's academic progress and provide learning experiences in the home. These students may do better because they received extra help, not because the teacher's instructional strategy is more effective. Unless the researcher can control for the intervening variable of extra help, it will be impossible to effectively test the study's hypothesis. Quantitative research designs can fall into two broad categories, experimental and quasi-experimental. Classic experimental designs are those that randomly assign subjects to either a control or treatment comparison group. The researcher can then compare the treatment group to the control group to test for an intervention's effect, known as a between-subject design. It is important to note that the control group may receive a standard treatment or may receive a treatment of any kind. Quasi-experimental designs do not randomly assign subjects to groups, but rather take advantage of existing groups. A researcher can still have a control and comparison group, but assignment to the groups is not random. The use of a control group is not required. However, the researcher may choose a design in which a single group is pre- and post-tested, known as a within-subjects design. Or a single group may receive only a post-test. Since quasi-experimental designs lack random assignment, the researcher should be aware of the threats to validity. Educational research often attempts to measure abstract variables such as attitudes, beliefs, and feelings. Surveys can capture data about these hard-to-measure variables, as well as other self-reported information such as demographic factors. A survey is an instrument used to collect verifiable information from a sample population. In quantitative research, surveys typically include questions that ask respondents to choose a rating from a scale, select one or more items from a list, or other responses that result in numerical data. Studies that use surveys or tests need to include strategies that establish the validity of the instrument used. There are many types of validity that need to be addressed. Face validity. Does the test appear at face value to measure what it is supposed to measure? Content validity. Content validity includes both item validity and sampling validity. Item validity ensures that the individual test items deal only with the subject being addressed. Sampling validity ensures that the range of item topics is appropriate to the subject being studied. For example, item validity might be high, but if all the items only deal with one aspect of the subjects, then sampling validity is low. Content validity can be established by having experts in the field review the test. Concurrent validity. Does a new test correlate with an older, established test that measures the same thing? Predictive validity. Does the test correlate with another related measure? For example, GRE tests are used at many colleges because these schools believe that a good grade on this test increases the probability that the student will do well at the college. Linear regression can establish the predictive validity of a test. Construct validity. Does the test measure the construct it is intended to measure? Establishing construct validity can be a difficult task when the constructs being measured are abstract. But it can be established by conducting a number of studies in which you test hypotheses regarding the construct, or by completing a factor analysis to ensure that you have the number of constructs that you say you have. In addition to ensuring the validity of instruments, the quantitative researcher needs to establish their reliability as well. Strategies for establishing reliability include Test retest. Correlates scores from two different administrations of the same test. Alternate forms. Correlates scores from administrations of two different forms of the same test. Split half reliability. Treats each half of one test or survey as a separate administration and correlates the results from each. Internal consistency. Uses Cronbach's coefficient alpha to calculate the average of all possible split halves. Quantitative research almost always relies on a sample that is intended to be representative of a larger population. There are two basic sampling strategies, random and non-random, and a number of specific strategies within each of these approaches. This table provides examples of each of the major strategies. The next section of this tutorial provides an overview of the procedures in conducting quantitative data analysis. There are specific procedures for conducting the data collection, preparing for and analyzing data, presenting the findings, and connecting to the body of existing research. This process ensures that the research is conducted as a systematic investigation that leads to credible results. Data comes in various sizes and shapes, and it is important to know about these so that the proper analysis can be used on the data. In 1946, S.S. Stevens first described the properties of measurement systems that allowed decisions about the type of measurement and about the attributes of objects that are preserved in numbers. These four types of data are referred to as nominal, ordinal, interval, and ratio. First, let's examine nominal data. With nominal data, there is no number value that indicates quantity. Instead, a number has been assigned to represent a certain attribute, like the number 1 to represent male and the number 2 to represent female. In other words, the number is just a label. You could also assign numbers to represent race, religion, or any other categorical information. Nominal data only denotes group membership. With ordinal data, there is again no indication of quantity. Rather, a number is assigned for ranking order. For example, satisfaction surveys often ask respondents to rank order their level of satisfaction with services or programs. The next level of measurement is interval data. With interval data, there are equal distances between two values, but there is no natural zero. A common example is the Fahrenheit temperature scale. Differences between the temperature measurements make sense, but ratios do not. For instance, 20 degrees Fahrenheit is not twice as hot as 10 degrees Fahrenheit. You can add and subtract interval level data, but they cannot be divided or multiplied. Finally, we have ratio data. Ratio is the same as interval, however ratios, means, averages, and other numerical formulas are all possible and make sense. Zero has a logical meaning, which shows the absence of, or having none of. Examples of ratio data are height, weight, speed, or any quantities based on a scale with a natural zero. In summary, nominal data can only be counted. Ordinal data can be counted and ranked. Interval data can also be added and subtracted, and ratio data can also be used in ratios and other calculations. Determining what type of data you have is one of the most important aspects of quantitative analysis. Depending on the research question, hypotheses, and research design, the researcher may choose to use descriptive and or inferential statistics to begin to analyze the data. Descriptive statistics are best illustrated when viewed through the lens of America's pastimes. Sports, weather, economy, stock market, and even our retirement portfolio are presented in a descriptive analysis. Basic terminology for descriptive statistics are terms that we are most familiar in this discipline. Frequency, mean, median, mode, range, variance, and standard deviation. Simply put, you are describing the data. Some of the most common graphic representations of data are bar graphs, pie graphs, histograms, and box and whisker graphs. Attempting to reach conclusions and make causal inferences beyond graphic representations or descriptive analyses is referred to as inferential statistics. In other words, examining the college enrollment of the past decade in a certain geographical region would assist in estimating what the enrollment for the next year might be. Frequently in education, the means of two or more groups are compared. When comparing means to assist in answering a research question, one can use a within-group, between-groups, or mixed-subject design. In a within-group design, the researcher compares measures of the same subjects across time, therefore within-group, or under different treatment conditions. This can also be referred to as a dependent-group design. The most basic example of this type of quasi-experimental design would be if a researcher conducted a pretest of a group of students, subjected them to a treatment, and then conducted a post-test. The group has been measured at different points in time. In a between-group design, subjects are assigned to one of the two or more groups. For example, Control, Treatment 1, Treatment 2. Ideally, the sampling and assignment to groups would be random, which would make this an experimental design. The researcher can then compare the means of the treatment group to the control group. When comparing two groups, the researcher can gain insight into the effects of the treatment. In a mixed-subjects design, the researcher is testing for significant differences between two or more independent groups while subjecting them to repeated measures. Choosing a statistical test to compare groups depends on the number of groups, whether the data are nominal, ordinal, or interval, and whether the data meet the assumptions for parametric tests. Nonparametric tests are typically used with nominal and ordinal data, while parametric tests use interval and ratio-level data. In addition to this, some further assumptions are made for parametric tests that the data are normally distributed in the population, that participant selection is independent, and the selection of one person does not determine the selection of another, and that the variances of the groups being compared are equal. The assumption of independent participant selection cannot be violated, but the others are more flexible. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the method of analysis for a quasi-experimental design. When choosing a t-test, the assumptions are that the data are parametric. The analysis of variance, or ANOVA, assesses whether the means of more than two groups are statistically different from each other. When choosing an ANOVA, the assumptions are that the data are parametric. The chi-square test can be used when you have non-parametric data and want to compare differences between groups. The Kruskal-Wallis test can be used when there are more than two groups and the data are non-parametric. Correlation analysis is a set of statistical tests to determine whether there are linear relationships between two or more sets of variables from the same list of items or individuals, for example, achievement and performance of students. The tests provide a statistical yes or no as to whether a significant relationship or correlation exists between the variables. A correlation test consists of calculating a correlation coefficient between two variables. Again, there are parametric and non-parametric choices based on the assumptions of the data. Pearson R correlation is widely used in statistics to measure the strength of the relationship between linearly related variables. Spearman-Rank correlation is a non-parametric test that is used to measure the degree of association between two variables. Spearman-Rank correlation test does not assume any assumptions about the distribution. Spearman-Rank correlation test is used when the Pearson test gives misleading results. Often a Kendall-Taw is also included in this list of non-parametric correlation tests to examine the strength of the relationship if there are less than 20 rankings. Linear regression and correlation are similar and often confused. Sometimes your methodologist will encourage you to examine both the calculations. Calculate linear correlation if you measured both variables, x and y. Make sure to use the Pearson parametric correlation coefficient if you are certain you are not violating the test assumptions. Otherwise, choose the Spearman non-parametric correlation coefficient. If either variable has been manipulated using an intervention, do not calculate a correlation. While linear regression does indicate the nature of the relationship between two variables, like correlation, it can also be used to make predictions because one variable is considered explanatory while the other is considered a dependent variable. Establishing validity is a critical part of quantitative research. As with the nature of quantitative research, there is a defined approach or process for establishing validity. This also allows for the findings transferability. For a study to be valid, the evidence must support the interpretations of the data, the data must be accurate, and their use in drawing conclusions must be logical and appropriate. Construct validity concerns whether what you did for the program was what you wanted to do, or whether what you observed was what you wanted to observe. Construct validity concerns whether the operationalization of your variables are related to the theoretical concepts you are trying to measure. Are you actually measuring what you want to measure? Internal validity means that you have evidence that what you did in the study, i.e., the program, caused what you observed, i.e., the outcome, to happen. Conclusion validity is the degree to which conclusions drawn about relationships in the data are reasonable. External validity concerns the process of generalizing, or the degree to which the conclusions in your study would hold for other persons in other places and at other times. Establishing reliability and validity to your study is one of the most critical elements of the research process. Once you have decided to embark upon the process of conducting a quantitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods were used. Consider the strengths and weaknesses of the various data collection and analysis methods. Next, review the literature on quantitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in quantitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with quantitative research. Make sure that you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques that you will use in your study. Thank you for watching.
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The ability to properly analyze and understand numbers has become very valuable, especially in today’s time.
Analyzing numerical data systematically involves thoughtfully collecting, organizing, and studying data to discover patterns, trends, and connections that can guide important choices.
Analyzing numbers is useful for learning from information. It applies stats methods and computational processes to study and make sense of data so you can spot patterns, connections, and how things change over time – giving insight to guide decisions.
At the core, quantitative analysis builds on math and stats fundamentals to turn raw figures into meaningful knowledge.
The process usually starts with gathering related numbers and organizing them neatly. Then analysts use different statistical techniques like descriptive stats, predictive modeling, and more to pull out valuable lessons.
Descriptive stats provide a summary of the key details, like averages and how spread out the numbers are. This helps analysts understand the basics and find any weird outliers.
Inferential stats allow analysts to predict broader trends based on a sample. Things like hypothesis testing , regression analysis, and correlation investigations help identify significant relationships.
Machine learning and predictive modeling have also enhanced working with numbers. These sophisticated methods let analysts create models that can forecast outcomes, recognize patterns across huge datasets, and uncover hidden insights beyond basic stats alone.
Leveraging data-based evidence supports more informed management of resources.
The first step in any quantitative data analysis is collecting the relevant data. This involves determining what data is needed to answer the research question or business objective.
Data can come from a variety of sources such as surveys, experiments, observational studies, transactions, sensors, and more.
Once the data is obtained, it typically needs to go through a data preprocessing or data cleaning phase.
Real-world data is often messy, containing missing values, errors, inconsistencies, and outliers that can negatively impact the analysis if not handled properly. Common data cleaning tasks include:
The goal of data cleaning is to ensure that quantitative data analysis techniques can be applied accurately to high-quality data. Proper data collection and preparation lays the foundation for reliable results.
In addition to cleaning, the data may need to be structured or formatted in a way that statistical software and data analysis tools can read it properly.
For large datasets, data management principles like establishing data pipelines become important.
Descriptive statistics is a crucial aspect of quantitative data analysis that involves summarizing and describing the main characteristics of a dataset.
This branch of statistics aims to provide a clear and concise representation of the data, making it easier to understand and interpret.
Descriptive statistics are typically the first step in analyzing data, as they provide a foundation for further statistical analyses and help identify patterns, trends, and potential outliers.
The most common descriptive statistics measures include:
Descriptive statistics play a vital role in data exploration and understanding the initial characteristics of a dataset.
They provide a summary of the data, allowing researchers and analysts to identify patterns, detect potential outliers, and make informed decisions about further analyses.
However, it’s important to note that descriptive statistics alone do not provide insights into the underlying relationships or causal mechanisms within the data.
To draw meaningful conclusions and make inferences about the population, inferential statistics and advanced analytical techniques are required.
While descriptive statistics provide a summary of data, inferential statistics allow you to make inferences and draw conclusions from that data.
Inferential statistics involve taking findings from a sample and generalizing them to a larger population. This is crucial when it is impractical or impossible to study an entire population.
The core of inferential statistics revolves around hypothesis testing . A hypothesis is a statement about a population parameter that needs to be evaluated based on sample data.
The process involves formulating a null and alternative hypothesis, calculating an appropriate test statistic, determining the p-value, and making a decision whether to reject or fail to reject the null hypothesis.
Some common inferential techniques include:
T-tests – Used to determine if the mean of a population differs significantly from a hypothesized value or if the means of two populations differ significantly.
ANOVA ( Analysis of Variance ) – Used to determine if the means of three or more groups are different.
Regression analysis – Used to model the relationship between a dependent variable and one or more independent variables. This allows you to understand drivers and make predictions.
Correlation analysis – Used to measure the strength and direction of the relationship between two variables.
Inferential statistics are critical for quantitative research, allowing you to test hypotheses, establish causality, and make data-driven decisions with confidence in the findings.
However, the validity depends on meeting the assumptions of the statistical tests and having a properly designed study with adequate sample sizes.
The interpretation of inferential statistics requires care. P-values indicate the probability of obtaining the observed data assuming the null hypothesis is true – they do not confirm or deny the hypothesis directly. Effect sizes are also crucial for assessing the practical significance beyond just statistical significance.
Quantitative data analysis goes beyond just describing and making inferences about data – it can also be used to build predictive models that forecast future events or behaviors.
Predictive modeling uses statistical techniques to analyze current and historical data to predict unknown future values.
Some of the key techniques used in predictive modeling include regression analysis , decision trees , neural networks, and other machine learning algorithms.
Regression analysis is used to understand the relationship between a dependent variable and one or more independent variables.
It allows you to model that relationship and make predictions. More advanced techniques like decision trees and neural networks can capture highly complex, non-linear relationships in data.
Machine learning has become an integral part of quantitative data analysis and predictive modeling. Machine learning algorithms can automatically learn and improve from experience without being explicitly programmed.
They can identify hidden insights and patterns in large, complex datasets that would be extremely difficult or impossible for humans to find manually.
Some popular machine learning techniques used for predictive modeling include:
Predictive models have a wide range of applications across industries, from forecasting product demand and sales to identifying risk of customer churn to detecting fraud.
With the rise of big data , machine learning is becoming increasingly important for building accurate predictive models from large, varied data sources.
To effectively perform quantitative data analysis, having the right tools and software is essential. There are numerous options available, ranging from open-source solutions to commercial platforms.
The choice depends on factors such as the size and complexity of the data, the specific analysis techniques required, and the budget.
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Quantitative data analysis techniques find widespread applications across numerous domains and industries. Here are some notable examples:
Businesses rely heavily on quantitative methods to gain insights from customer data, sales figures, market trends, and operational metrics.
Techniques like regression analysis help model customer behavior, while clustering algorithms enable customer segmentation. Forecasting models allow businesses to predict future demand, inventory needs, and revenue projections.
Analysis of clinical trial data, disease prevalence statistics, and patient outcomes employs quantitative methods extensively.
Hypothesis testing determines the efficacy of new drugs or treatments. Survival analysis models patient longevity. Data mining techniques identify risk factors and detect anomalies in healthcare data.
Marketing teams use quantitative data from surveys, A/B tests, and online behavior tracking to optimize campaigns. Regression models predict customer churn or likelihood to purchase.
Sentiment analysis derives insights from social media data and product reviews. Conjoint analysis determines which product features impact consumer preferences.
Quantitative finance relies on statistical models for portfolio optimization, derivative pricing, risk quantification, and trading strategy formulation. Value at Risk (VaR) models assess potential losses. Monte Carlo simulations evaluate the risk of complex financial instruments.
From political polls to consumer surveys, quantitative data analysis techniques like weighting, sampling, and survey data adjustment are critical. Researchers employ methods like factor analysis, cluster analysis, and structural equation modeling .
Case study 1: netflix’s data-driven recommendations.
Netflix extensively uses quantitative data analysis, particularly machine learning, to drive its recommendation engine.
By mining user behavior data and combining it with metadata about movies and shows, they build predictive models to accurately forecast what a user would enjoy watching next.
The adoption of sabermetrics and analytics by baseball teams like the Oakland Athletics, as depicted in the movie Moneyball, revolutionized player scouting and strategy.
By quantifying player performance through new statistical metrics, teams could identify undervalued talent and gain a competitive edge.
Quantitative data analysis is a powerful toolset that allows organizations to derive valuable insights from their data to make informed decisions.
By applying the various techniques and methods discussed, such as descriptive statistics, inferential statistics , predictive modeling , and machine learning, businesses can gain a competitive edge by uncovering patterns, trends, and relationships hidden within their data.
However, it’s important to note that quantitative data analysis is not a one-time exercise. As businesses continue to generate and collect more data, the analysis process should be an ongoing, iterative cycle.
If you’re looking to further enhance their quantitative data analysis capabilities, there are several potential next steps to consider:
Implementing robust data governance policies and adhering to ethical guidelines can help organizations maintain trust and accountability.
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The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language. Quantitative research collects numerical ...
Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
4. Quantitative vs qualitative data: methods of analysis. Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.
At a Glance. Psychologists rely on quantitative and quantitative research to better understand human thought and behavior. Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions. Quantitative research involves collecting and evaluating numerical data.
Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality. Qualitative research allows you to understand concepts or experiences. Let's look at how you'll use these approaches in a research project a bit closer: Formulating a hypothesis.
Quantitative research is the opposite of qualitative research, which involves collecting and analyzing non-numerical data (e.g., text, video, or audio). Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc. Quantitative research question examples
For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches ...
Quantitative variables must use numbers. Think QUANTITY. A critical difference between qualitative vs quantitative data is that you can order the quantitative observations but not the qualitative observations. Quantitative variables can be continuous measurements on a scale or discrete counts. Learn more about continuous vs. discrete data.
When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions. On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships.
It can help add a 'why' element to factual, objective data. Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data. This qualitative data is called unstructured data by researchers.
Qualitative data can lay the foundation for quantitative analysis by creating defined categories in which to explore data later on. Deciding what category boundaries to set is a judgment call, but the category's relevance is only as good as the quality of the qualitative research that underpins it.
Qualitative data is commonly used in survey research, interviews, and observational studies, as it dives deeply into participant motives, attitudes, and actions. Open-ended questions in surveys capture complex replies, whereas interviews allow for direct involvement for a deeper understanding.
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...
This is an important cornerstone of the scientific method. Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average.
Research data can be placed into two broad categories: quantitative or qualitative. Quantitative data are used when a researcher is trying to quantify a problem, or address the "what" or "how many" aspects of a research question.It is data that can either be counted or compared on a numeric scale. For example, it could be the number of first year students at Macalester, or the ratings on a ...
What Is Qualitative vs. Quantitative Study? Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the "how" and "why" of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify ...
Educators use qualitative research in a study's exploratory stages to uncover patterns or new angles. Form Strong Conclusions with Quantitative Research. Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.
What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns.Quantitative research gathers a range of numeric data.
Qualitative data provides details and context to better understand individual responses, while quantitative data can supply the cumulative results you need to prove the general ideas or hypotheses of your research. To get the best results from these methods in your surveys, it's important to understand the differences between them. Let's ...
How To Analyze Qualitative vs. Quantitative Data. Another of the similarities of qualitative and quantitative research is that both look for patterns in the data they collect that point to a relationship between elements. Both qualitative and quantitative data are instrumental in supporting existing theories and developing new ones.
While quantitative research is based on numbers and mathematical calculations (aka quantitative data), qualitative research is based on written or spoken narratives (or qualitative data).Qualitative and quantitative research techniques are used in marketing, sociology, psychology, public health and various other disciplines.
Quantitative Research: A Data-Driven Approach. Unlike qualitative methods, quantitative research relies primarily on the collection and analysis of objective, measurable numerical data. This structured empirical evidence is then manipulated using statistical, graphical and mathematical techniques to derive patterns, trends and conclusions. ...
Although quantitative data analysis is a powerful tool, it cannot be used to provide context for your research, so this is where qualitative analysis comes in. Qualitative analysis is another common research method that focuses on collecting and analyzing non-numerical data, like text, images, or audio recordings to gain a deeper understanding ...
Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research. It's key data analysis features include: Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
Different Types of Data 1. *Qualitative Data*: Non-numeric data that describes qualities or characteristics. Examples include colors, names, labels, and other categorical data. It can be further divided into: - *Nominal Data*: Categories without any specific order (e.g., gender, nationality). - *Ordinal Data*: Categories with a meaningful order but no fixed intervals between them (e.g ...
The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes.
Interpretative approaches are usually associated with qualitative social science but are equally applicable to the analysis of quantitative data. In interpretive quantitative research, statistics ...
Healthcare and Biomedical Research with Quantitative Data Analysis. Analysis of clinical trial data, disease prevalence statistics, and patient outcomes employs quantitative methods extensively. ... While this article has focused primarily on quantitative data analysis, incorporating qualitative data sources, such as customer feedback, social ...