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. Show
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Quantitative research is at risk for research biases including information bias, omitted variable bias, sampling bias, or selection bias. Qualitative researchQualitative research is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories. Qualitative research is also at risk for certain research biases including the Hawthorne effect, observer bias, recall bias, and social desirability bias. The differences between quantitative and qualitative researchQuantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.
Data collection methodsQuantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s). Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies, your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe). However, some methods are more commonly used in one type or the other. Quantitative data collection methods
Qualitative data collection methods
When to use qualitative vs. quantitative researchA rule of thumb for deciding whether to use qualitative or quantitative data is:
For most research topics you can choose a qualitative, quantitative or mixed methods approach. Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach; your research question(s); whether you’re doing experimental, correlational, or descriptive research; and practical considerations such as time, money, availability of data, and access to respondents. Research questionHow satisfied are students with their studies?Quantitative research approachYou survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?” You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”. Qualitative research approachYou conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?” Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns. Mixed methods approachYou conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale. It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends. How to analyze qualitative and quantitative dataQualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data. Analyzing quantitative dataQuantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables. Applications such as Excel, SPSS, or R can be used to calculate things like: Analyzing qualitative dataQualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers. Some common approaches to analyzing qualitative data include: Frequently asked questions about qualitative and quantitative researchWhat is data collection?
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
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Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects information from existing and potential customers using sampling methods and sending out online surveys, online polls, and questionnaires, for example. The results of which can be depicted in the form of numerical. After careful understanding of these numbers to predict the future of a product or service and make changes accordingly. An example of quantitative research is the survey conducted to understand the amount of time a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey template can be administered to ask questions like how much time a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions. Gather research insights Quantitative outcome research is mostly conducted in the social sciences using the statistical methods used above to collect quantitative data from the research study. In this research method, researchers and statisticians deploy mathematical frameworks and theories that pertain to the quantity under question. Quantitative research templates are objective, elaborate, and many times, even investigational. The results achieved from this research method are logical, statistical, and unbiased. Data collection happened using a structured method and was conducted on larger samples that represent the entire population. Quantitative research characteristicsSome distinctive characteristics of quantitative research are:
Quantitative research examplesSome examples of quantitative research are:
What are the advantages of quantitative research?There are many advantages of quantitative research. Some of the major advantages of why researchers use this method in market research are:
Gather research insights Best practices to conduct quantitative researchHere are some best practices to conduct quantitative research
As mentioned above, quantitative research is data-oriented. There are two methods to conduct quantitative research. They are:
Primary MethodsThere are four different types of quantitative research methods: Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks, as well as the process flow. They are: A. Techniques and Types of StudiesThere are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are: Survey Research is the most fundamental tool for all quantitative outcome research methodologies and studies. Surveys used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys, etc. Every small and big organization intends to understand what their customers think about their products and services, how well are new features faring in the market and other such details. By conducting survey research, an organization can ask multiple survey questions, collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis. A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. Traditionally, survey research was conducted face-to-face or via phone calls but with the progress made by online mediums such as email or social media, survey research has spread to online mediums as well. Traditionally, survey research was conducted face-to-face or via phone calls but with the progress made by online mediums such as email or social media, survey research has spread to online mediums as well. There are two types of surveys, either of which can be chosen based on the time in-hand and the kind of data required: Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research. Throughout the survey, this one variable will stay constant.
Longitudinal surveys: Longitudinal surveys are also observational surveys but, unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought-processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.
A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other and what are the changes that are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established. Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original set up. The impact of one of these variables on the other is observed along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results. Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated. Example of Correlational Research Questions:
This research method mainly depends on the factor of comparison. Also called quasi-experimental research, this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural set up. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind. Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relation that exists between two or more variables. Statistical analysis is used to distinctly present the outcome obtained using this quantitative research method. Example of Causal-Comparative Research Questions:
Also known as true experimentation, this research method is reliant on a theory. Experimental research, as the name suggests, is usually based on one or more theories. This theory has not been proven in the past and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences.Traditional research methods are more effective than modern techniques. There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted. After establishing the statement, efforts are made to understand whether it is valid or invalid. This type of quantitative research method is mainly used in natural or social sciences as there are various statements which need to be proved right or wrong.
Gather research insights B. Data collection methodologiesThe second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection with the use of surveys and polls. Data collection methodologies: Sampling methodsThere are two main sampling methods for quantitative research: Probability and Non-probability sampling. A theory of probability is used to filter individuals from a population and create samples in probability sampling. Participants of a sample are chosen random selection processes. Each member of the target audience has an equal opportunity to be selected in the sample. There are four main types of probability sampling:
Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the involvement of the researcher, not all the members of a target population have an equal probability of being selected to be a part of a sample. There are five non-probability sampling models:
Data collection methodologies: Using surveys & pollsOnce the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research. A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can be reached depending on the research time and research objective make it one of the most important aspects of conducting quantitative outcome research. Fundamental levels of measurement – nominal, ordinal, interval and ratio scales There are four measurement scales that are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which, no multiple-choice questions can be created. Hence, it is crucial to understand these levels of measurement to be able to develop a robust survey. Use of different question types To conduct quantitative research, close-ended questions have to be used in a survey. They can be a mix of multiple question types including multiple-choice questions like semantic differential scale questions, rating scale questions, etc. Survey Distribution and Survey Data Collection In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:
Survey example An example of a survey is short customer satisfaction (CSAT) survey template that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.
Polls are a method to collect feedback with the use of close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls. Both of these are used to collect data from a large sample size but using basic question types like a multiple-choice question. C. Data analysis techniquesThe third aspect of primary quantitative research design is data analysis. After the collection of raw data, there has to be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the objective of the research and establish the statistical relevance of the results. It is important to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed. It is then required to select precise statistical analysis methods such as SWOT, Conjoint, Cross-tabulation, etc. to analyze the quantitative data.
Inferential statistics methods such as confidence interval, the margin of error, etc. can then be used to provide results. Secondary MethodsSecondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of research. This research method involves the collection of quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data that is collected from primary quantitative research as well as aid in strengthening or proving or disproving previously collected data. Following are five popularly used secondary quantitative research methods:
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