limitations of quantitative research method

Limitations and Weaknesses of Quantitative Research

• Post author: Edeh Samuel Chukwuemeka ACMC
• Post published: August 16, 2021
• Post category: Scholarly Articles

Limitations and Weaknesses of Quantitative Research: Research entails the collection of materials for  academic or other purposes. It is a process of gathering information and data to solve or existing problem or prevent future problems. Research works can be done via two methods. Qualitative research or quantitative research.

Qualitative research involves the carrying out of research by gathering non-numerical data. For example, gathering of video evidence, texts or messages for analysis. On the other hand, quantitative research is the process where by numerical data are collected and analyzed. It is effectively used to find patterns and averages as well as generalising a finding or result to a wider population. Quantitative research is mostly used in natural and social sciences such as biology, psychology, economics, among others.

Quantitative research could be carried out using any four methods of researching which are descriptive research, correlational research, experimental research or survey research. In descriptive, one seeks to know the ‘what’ of a thing rather than the ‘why’ of such thing. It tries to describe the various components of an information.

Correlational research involves the research between two variables to ascertain the relationship between the variables. It understudies the impact of one variable on the other. On the other hand, an experimental research is one that uses scientific methods to establish the relationship between groups of variables. That is, it tries to establish a cause-effect relationship between the various variables under study.

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Finally, the survey research which is most widely used involves the preparation of set questionnaires, interviews and polls to which answers are provided by a segment of the target population and then, conclusions are drawn from such answers given. Survey research studies the relationship between various variables in a given research.

One of the major benefit of  quantitative research method is that it makes one arrive at a well considered conclusion since samples are collected from those who are directly affected by the research. The data collected are majorly converted to a numerical form which aids in statistical analysis. Also, quantitative research is more convenient for projects with scientific and social science inclinations.

Also see: Advantages and Disadvantages of quantitative and qualitative Research

Weaknesses of Quantitative Research

Notwithstanding the benefits of quantitative research, the research method has its own weaknesses and limitations. This is because the method is not applicable and convenient in all cases of research. Thus, using a quantitative research method in a research where qualitative research method should be used will not produce the needed result.

To this end, some of the weaknesses and limitations of quantitative research are highlighted below.

1. It Requires a Large Number of Respondents: In the course of carrying out a quantitative research, recourse has to be made to a large number of respondents. This is because you are sampling a section of a population to get their views, which views will be seen as that of the general population. In doing this, a huge number of respondents have to be consulted so as to get a fair view or percentage of the target population.

For example, if one wishes to carry out a quantitative research in Nigeria as to her acceptance of a policy of the government, one will need to consult wider. This is because Nigeria has a population of over 200 million people and the opinions of a few thousands cannot pass out as that of 200 million people. In the light of this, more respondents will be required to be interviewed so as to enable one get a fair view of the population.

Large number of respondents is thus, one of the weaknesses or limitations of quantitative research as a small sampling of a section of the target population might not be of much help to the research.

2. It is time consuming: Unlike qualitative research which has to do with analysis of already prepared data, quantitative research demands that you source for and collate the data yourself while converting such data collected into a numerical form for proper analysis. This process is time consuming. Again, the task of sending out questionnaires to respondents and waiting for answers to such questionnaires might be time consuming as most respondents will reply late or may not even reply at all.

Great patience is therefore needed in carrying out a quantitative research. It is therefore not always a good method of research in cases of urgencies as the time to get responses might take too long.

Also see: Major characteristics of customary laws

3. It requires huge resources: Quantitative research requires huge investment of time, money and energy. It is time consuming just as it also involve huge financial commitments.

In carrying out quantitative research, one needs to get your questions prepared, sent out and also followed up to ensure that such is answered. Also, some respondents might demand to be paid before giving their inputs to such a research. An example is the trending online surveys in which the target respondents are paid for every survey they carry out for a researcher.

4. Difficulty in Analyzing the Data Collected: Data are collected from respondents and then converted into statistics. This usually poses as a limitation to a researcher who is not an expert in statistics. Analysis of collected data is also demanding and time consuming. A researcher needs to make such information collected into numerical data and correlate them with the larger population. Where this is not properly done, it means that the outcome might be false or misleading.

Also, due to the fact that a researcher might not have control over the environment he is researching in, as any such environment is susceptible to change at any point in time, the outcome of his research might be inconsistent.

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5. Outcomes of quantitative research is usually limited: In quantitative research, the outcomes are usually limited. This is because the outcome is usually based on what the researcher wants. This limited outcome is due to the structured pattern of the questionnaires. Questionnaires usually have close ended questions which gives a respondent little or no opportunity of explanations. Thus, the answers provided are limited to the questions asked and nothing more.

6. Data outcomes are usually generalised : As noted earlier, quantitative research is usually conducted on a section of a target population and not on the whole population. The outcome of this research is then generalised as the view of the entire population. What this portends is that the views of  few respondents in that research is seen as that of the general populace. Such views from them might be biased or insincere, yet they are seen as that of the entire population.

In the light of this, the fallacy of hasty generalisation is prone to be committed in a quantitative research. Generalisation of the views of a section of the population might not be the best as their views may be biased.

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In conclusion, quantitative research is a veritable means of conducting research especially in the fields of natural sciences and social sciences. This is because it mostly has a one on one interaction between the researcher and the various respondents as it majorly studies behavior. This advantage notwithstanding, the research method has its own  limitations and weaknesses. These limitations and weaknesses often times affect the quality of a research which is done using the quantitative method of research.

Edeh Samuel Chukwuemeka, ACMC, is a lawyer and a certified mediator/conciliator in Nigeria. He is also a developer with knowledge in various programming languages. Samuel is determined to leverage his skills in technology, SEO, and legal practice to revolutionize the legal profession worldwide by creating web and mobile applications that simplify legal research. Sam is also passionate about educating and providing valuable information to people.

13 Pros and Cons of Quantitative Research Methods

Quantitative research utilizes mathematical, statistical, and computational tools to derive results. This structure creates a conclusiveness to the purposes being studied as it quantifies problems to understand how prevalent they are.

It is through this process that the research creates a projectable result which applies to the larger general population.

Instead of providing a subjective overview like qualitative research offers, quantitative research identifies structured cause-and-effect relationships. Once the problem is identified by those involved in the study, the factors associated with the issue become possible to identify as well. Experiments and surveys are the primary tools of this research method to create specific results, even when independent or interdependent factors are present.

These are the quantitative research pros and cons to consider.

List of the Pros of Quantitative Research

1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine. That means the information under study can be analyzed very quickly when compared to other research methods. The need to separate systems or identify variables is not as prevalent with this option either.

2. The samples of quantitative research are randomized. Quantitative research uses a randomized process to collect information, preventing bias from entering into the data. This randomness creates an additional advantage in the fact that the information supplied through this research can then be statistically applied to the rest of the population group which is under study. Although there is the possibility that some demographics could be left out despite randomization to create errors when the research is applied to all, the results of this research type make it possible to glean relevant data in a fraction of the time that other methods require.

3. It offers reliable and repeatable information. Quantitative research validates itself by offering consistent results when the same data points are examined under randomized conditions. Although you may receive different percentages or slight variances in other results, repetitive information creates the foundation for certainty in future planning processes. Businesses can tailor their messages or programs based on these results to meet specific needs in their community. The statistics become a reliable resource which offer confidence to the decision-making process.

4. You can generalize your findings with quantitative research. The issue with other research types is that there is no generalization effect possible with the data points they gather. Quantitative information may offer an overview instead of specificity when looking at target groups, but that also makes it possible to identify core subjects, needs, or wants. Every finding developed through this method can go beyond the participant group to the overall demographic being looked at with this work. That makes it possible to identify trouble areas before difficulties have a chance to start.

5. The research is anonymous. Researchers often use quantitative data when looking at sensitive topics because of the anonymity involved. People are not required to identify themselves with specificity in the data collected. Even if surveys or interviews are distributed to each individual, their personal information does not make it to the form. This setup reduces the risk of false results because some research participants are ashamed or disturbed about the subject discussions which involve them.

6. You can perform the research remotely. Quantitative research does not require the participants to report to a specific location to collect the data. You can speak with individuals on the phone, conduct surveys online, or use other remote methods that allow for information to move from one party to the other. Although the number of questions you ask or their difficulty can influence how many people choose to participate, the only real cost factor to the participants involves their time. That can make this option a lot cheaper than other methods.

7. Information from a larger sample is used with quantitative research. Qualitative research must use small sample sizes because it requires in-depth data points to be collected by the researchers. This creates a time-consuming resource, reducing the number of people involved. The structure of quantitative research allows for broader studies to take place, which enables better accuracy when attempting to create generalizations about the subject matter involved. There are fewer variables which can skew the results too because you’re dealing with close-ended information instead of open-ended questions.

List of the Cons of Quantitative Research

1. You cannot follow-up on any answers in quantitative research. Quantitative research offers an important limit: you cannot go back to participants after they’ve filled out a survey if there are more questions to ask. There is a limited chance to probe the answers offered in the research, which creates fewer data points to examine when compared to other methods. There is still the advantage of anonymity, but if a survey offers inconclusive or questionable results, there is no way to verify the validity of the data. If enough participants turn in similar answers, it could skew the data in a way that does not apply to the general population.

2. The characteristics of the participants may not apply to the general population. There is always a risk that the research collected using the quantitative method may not apply to the general population. It is easy to draw false correlations because the information seems to come from random sources. Despite the efforts to prevent bias, the characteristics of any randomized sample are not guaranteed to apply to everyone. That means the only certainty offered using this method is that the data applies to those who choose to participate.

3. You cannot determine if answers are true or not. Researchers using the quantitative method must operate on the assumption that all the answers provided to them through surveys, testing, and experimentation are based on a foundation of truth. There are no face-to-face contacts with this method, which means interviewers or researchers are unable to gauge the truthfulness or authenticity of each result.

A 2011 study published by Psychology Today looked at how often people lie in their daily lives. Participants were asked to talk about the number of lies they told in the past 24 hours. 40% of the sample group reported telling a lie, with the median being 1.65 lies told per day. Over 22% of the lies were told by just 1% of the sample. What would happen if the random sampling came from this 1% group?

4. There is a cost factor to consider with quantitative research. All research involves cost. There’s no getting around this fact. When looking at the price of experiments and research within the quantitative method, a single result mist cost more than $100,000. Even conducting a focus group is costly, with just four groups of government or business participants requiring up to $60,000 for the work to be done. Most of the cost involves the target audiences you want to survey, what the objects happen to be, and if you can do the work online or over the phone.

5. You do not gain access to specific feedback details. Let’s say that you wanted to conduct quantitative research on a new toothpaste that you want to take to the market. This method allows you to explore a specific hypothesis (i.e., this toothpaste does a better job of cleaning teeth than this other product). You can use the statistics to create generalizations (i.e., 70% of people say this toothpaste cleans better, which means that is your potential customer base). What you don’t receive are specific feedback details that can help you refine the product. If no one likes the toothpaste because it tastes like how a skunk smells, that 70% who say it cleans better still won’t purchase the product.

6. It creates the potential for an unnatural environment. When carrying out quantitative research, the efforts are sometimes carried out in environments which are unnatural to the group. When this disadvantage occurs, the results will often differ when compared to what would be discovered with real-world examples. That means researchers can still manipulate the results, even with randomized participants, because of the work within an environment which is conducive to the answers which they want to receive through this method.

These quantitative research pros and cons take a look at the value of the information collected vs. its authenticity and cost to collect. It is cheaper than other research methods, but with its limitations, this option is not always the best choice to make when looking for specific data points before making a critical decision.

Limitations and weakness of quantitative research methods

According to Saunders et al. (2009), research methodology serves as the backbone of a research study. Quantitative research’s main purpose is the quantification of the data. It allows generalisations of the results by measuring the views and responses of the sample population. Every research methodology consists of two broad phases namely planning and execution (Younus 2014). Therefore, it is evident that within these two phases, there are likely to have limitations which are beyond our control (Simon 2011). 

Improper representation of the target population

As mentioned in the article , improper representation of the target population might hinder the researcher from achieving its desired aims and objectives. Despite applying an appropriate sampling plan representation of the subjects is dependent on the probability distribution of observed data. This may lead to a miscalculation of probability distribution and lead to falsity in the proposition.

A study purports to check the proportion of females aged between 20-30 years who are applying make-up ranges of international brands. The target population, in this case, is the women belonging to the said age group, with both professional and non-professional backgrounds, residing in Delhi. The sampled population based on the probability distribution has to be calculated against the total females residing in the city (e.g. 400 sampled out of 7,800,615 female populations). However, there is a scope of getting partial information about the range of makeup products from the sampled, owing to its meagre form against the total population. Hence, the results of the study cannot be generalised in context to a larger population, but rather be suggested.

Lack of resources for data collection

Quantitative research methodology usually requires a large sample size. However, due to the lack of resources, this large-scale research becomes impossible. In many developing countries, interested parties (e.g., government or non-government organisations, public service providers, educational institutions, etc.) may lack knowledge and especially the resources needed to conduct thorough quantitative research (Science 2001).

Inability to control the environment

Sometimes researchers face problems to control the environment where the respondents provide answers to the questions in the survey (Baxter 2008). Responses often depend on a particular time which again is dependent on the conditions occurring during that particular time frame.

For example, if data for a study is collected on residents’ perception of development works conducted by the municipality, the results presented for a specific year (say, 2009), will be held redundant or of limited value in 2015. The reasons are, that either the officials have changed or the development scenario has changed (from too effective to minimal effective or vice versa).

Limited outcomes in a quantitative research

The quantitative research method involves a structured questionnaire with close-ended questions. It leads to limited outcomes outlined in the research proposal. So the results cannot always represent the actual occurrence, in a generalised form. Also, the respondents have limited options for responses, based on the selection made by the researcher.

“Does your manager motivates you to take up challenges”

The answer can be Yes / No / Can’t say or Strongly Agree to Strongly disagree. But to know what strategies are applied by the manager to motivate the employee or on what parameters the employee does not feel motivated (if responded no), the researcher has to ask broader questions which somewhat has limited scope in close-ended questionnaires

Expensive and time-consuming

Quantitative research is difficult, expensive and requires a lot of time to perform the analysis. This type of research is planned carefully in order to ensure complete randomization and correct designation of control groups (Morgan 1980). A large proportion of respondents is appropriate for the representation of the target population. So, to achieve in-depth responses on an issue, data collection in quantitative research methodology is often too expensive than the qualitative approach.

To understand the influence of advertising on the propensity of purchase decision of baby foods parents of 5-year old and below in Bangalore, the researcher needs to collect data from 200 respondents. This is time-consuming and expensive, given the approach needed for each of these parents to explain the study’s purpose.

Difficulty in data analysis

The quantitative study requires extensive statistical analysis , which can be difficult to perform for researchers from non-statistical backgrounds. Statistical analysis is based on scientific discipline and hence is difficult for non-mathematicians to perform.

Quantitative research is a lot more complex for social sciences, education, anthropology and psychology. The effective response should depend on the research problem rather than just a simple yes or no response.

To understand the level of motivation perceived by Grade 5 students from the teaching approach taken by their class teachers, mere yes and no might lead to ambiguity in data collection and hence improper results. Instead, a detailed interview or focus group technique might develop in-depth views and perspectives of both the teachers and children.

Requirement of extra resources to analyse the results

The requirements for the successful statistical confirmation of the result are very tough in quantitative research. A hypothesis is proven with few experiments due to which there is ambiguity in the results. Results are retested and refined several times for an unambiguous conclusion (Ong 2003). So it requires extra time, investment and resources to refine the results.

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Research Methods

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Quantitative research methods involve the collection of numerical data and the use of statistical analysis to draw conclusions. This method is suitable for research questions that aim to measure the relationship between variables, test hypotheses, and make predictions. Here are some tips for choosing quantitative research methods:

Identify the research question: Determine whether your research question is best answered by collecting numerical data. Quantitative research is ideal for research questions that can be quantified, such as questions that ask how much, how many, or how often.

Choose the appropriate data collection methods: Select data collection methods that allow you to collect numerical data, such as surveys, experiments, or observational studies. Surveys involve asking participants to respond to a set of standardized questions, while experiments involve manipulating variables to determine their effect on an outcome. Observational studies involve observing and recording behaviors or events in a natural setting.

When choosing a data collection method, it's important to consider the feasibility, reliability, and validity of the method. Feasibility refers to whether the method is practical and achievable within the available resources, while reliability refers to the consistency of the results over time and across different observers or settings. Validity refers to whether the method accurately measures what it's intended to measure.

The sample size: Decide on the sample size that is needed to produce statistically significant results. The sample size is the number of participants in the study. The larger the sample size, the more reliable the results are likely to be. However, a larger sample size also requires more resources and time. Therefore, it's important to determine the appropriate sample size based on the research question and available resources.

Statistical test: Choose the appropriate statistical analysis techniques based on the type of data you have collected and the research question. Common statistical analysis techniques include descriptive statistics, correlation analysis, regression analysis, and t-tests. Descriptive statistics summarize the data using measures such as mean, standard deviation, and frequency. Correlation analysis examines the relationship between two or more variables. Regression analysis examines the relationship between one dependent variable and one or more independent variables. T-tests compare the means of two groups.

When selecting a statistical analysis technique, it's important to consider the assumptions of the technique and whether they are appropriate for the data being analyzed. It's also important to consider the level of statistical significance required to draw meaningful conclusions.

Strength and Limitations

Strength and limitations of quantitative research methods:.

• The use of statistical analysis allows for the identification of patterns and relationships between variables.
• Provides a structured and standardized approach to data collection, allowing for replication of studies and comparisons across studies.
• It can produce reliable and valid results which are generalizable to larger populations.
• Allows for hypothesis testing, making it suitable for research questions that require a cause-and-effect relationship.
• It can produce numerical data, making it easy to summarize and communicate results.

Limitations:

• It may oversimplify complex phenomena by reducing them to numerical data.
• It may not capture the context and subjective experiences of individuals.
• It may not allow for the exploration of new ideas or unexpected findings.
• It may be influenced by researcher bias or the use of inappropriate statistical techniques.
• It may not account for variables that are difficult to measure or control.
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Exploring the benefits and limitations of quantitative research methods in higher, education and sciences: a comprehensive overview.

Introduction:

Quantitative research methods are often favoured for gathering and analysing data in higher education research because of their replicability in their experimental design. Research design is a crucial aspect of conducting research, and three commonly used designs in the quantitative analysis are experimental, quasi-experimental, and non-experimental. These Sampling and data collection are also essential steps in the research process, with probability and non-probability sampling methods and surveys/questionnaires, observation, experimentation, and secondary data analysis being commonly used data collection methods. Data analysis methods include descriptive statistics, inferential statistics, and regression analysis, which help researchers draw conclusions about their research question. This blog article will examine how to explore quantitative studies through my experiences conducting this biology and social sciences research. It will also explore the advantages and disadvantages of the various methods and approaches, discussing the scope and limitations. Quantitative research methods are valuable tools for researchers to understand various phenomena.

Exploring the Three Main Research Designs in Quantitative Analysis

In previous articles, I have outlined how research design is crucial to your project or dissertation. Three research designs are typically used in quantitative analysis: experimental, quasi-experimental, and non-experimental.

Experimental Design:

Experimental design involves manipulating one or more independent variables to observe the effect on the dependent variable. This design is helpful in higher education research to test the effectiveness of a new teaching method or instructional technology, for example, comparing the learning outcomes of traditional lectures versus online learning environments.

Quasi-experimental Design:

A quasi-experimental design is similar to an experimental procedure, but the researcher cannot randomly assign participants to groups. Instead, pre-existing groups are used for comparison purposes. This could be used by looking at flock performances on chicken or turkey growth rates based on an additive introduced to shed rations. Due to flocks having an inherently limited time through the production cycle, a measure of looking at different crops would be the only way to assess productivity changes.

Non-experimental Design:

The non-experimental design does not involve the manipulation of independent variables. Instead, it consists of the observation of naturally occurring phenomena. Often this method may be explored in behavioural research using ethograms and time budgets to measure groups or individual animal responses. This design can be used in higher education research to explore the relationship between a student's socioeconomic class and academic performance.

Sampling: The Art and Science of Participant Selection in Quantitative Research

Sampling refers to the process of selecting participants for a research study. There are two types of sampling methods: probability sampling and non-probability sampling. Probability sampling involves randomly selecting participants from a population, ensuring each member has an equal chance of being selected. Non-probability selection involves selecting participants based on specific criteria, which is useful when the population is too large to choose a random sample or when the research question requires a particular type of participant.

The process of selecting participants for a research project is called sampling. Probability sampling and non-probability sampling are the two types of sampling procedures. Probability sampling entails randomly picking members from a population, guaranteeing that each member has an equal probability of being chosen. Non-probability selection is selecting participants based on specific criteria, which is advantageous when the population is too big to pick a random sample or when the research question necessitates a particular sort of participant.

Unleashing the Full Potential of Data Collection Methods in Quantitative Research

The process of acquiring information for a research study is known as data collection. Surveys/questionnaires, observation, experimentation, and secondary data analysis are quantitative research's four standard data-gathering methods. Surveys/questionnaires entail asking participants to answer standard questions, whereas statements entail monitoring and documenting behaviour. The investigation involves modifying one or more independent factors to maintain the effect on the dependent variable, whereas secondary data analysis is analysing previously acquired data for a new reason.

Both the social and biological sciences benefit from quantitative research methodologies. Quantitative research methods can be used to investigate social phenomena and behaviour in the social sciences, such as political science, sociology, psychology, and economics. Quantitative research methods can be used to examine living creatures and their environments in biological disciplines such as agriculture, medicine, and animal studies.

From Agriculture to Politics: The Versatility of Quantitative Research Methods in Social and Biological Sciences

Quantitative research methods are widely used in many social and biological sciences areas. These strategies entail gathering and analysing numerical data to comprehend patterns and relationships. This blog article will examine concrete examples of how quantitative research methods are employed in many sectors and their benefits and drawbacks.

In political science, quantitative research methods are used to analyse political behaviour, public opinion, and voting trends. If you were studying ecology policy and wanted to gauge the public's reception to your project's proposal, such as their view on swift boxes in domestic properties proposals, a measure that has been explored in Brighton and successfully to benefit a population. Outputs such as the demographic of individuals and understanding their levels of responses to the question.

Researchers utilise surveys and experiments to better understand how individuals make political decisions and how government policies influence society. Sociologists use surveys and experiments to explore inequality, social mobility, and social change. In psychology, quantitative research methods investigate human behaviour and mental processes such as personality, emotion, and cognitive functions. Economic researchers use statistical analysis to analyse consumer behaviour, labour markets, and economic growth. Quantitative research methods are also applied in the biological sciences. Researchers employ experiments and statistical analysis to create new farming practices and boost agricultural sustainability. Whether this is in understanding new production techniques such as vertical farming using grey water, which can be examined to assess the viability of crop production against traditional measures.

Pros and Cons: Understanding the Advantages and Limitations of Quantitative Research Methods

Quantitative research methods offer various advantages, including exact variable measurement, which can lead to more accurate and dependable conclusions. These procedures are straightforward to replicate, which adds to the findings' validity. Moreover, statistical analysis enables the detection of patterns and correlations in data. Yet, quantitative research approaches have several disadvantages. They may need to be adapted for measuring or quantifying complex objects. These approaches may also limit their ability to capture the depth and complexity of human experience, and they may be prone to biases and assumptions that weaken the validity of the findings.

Conclusion:

In conclusion, quantitative research methods are valuable tools for researchers to gather and analyse data to draw conclusions about various phenomena. The research design, sampling, data collection, and data analysis are essential steps in the research process, with experimental, quasi-experimental, and non-experimental techniques commonly used in quantitative analysis. Probability and non-probability sampling methods and surveys/questionnaires, observation, experimentation, and secondary data analysis are widely used data collection methods. Descriptive statistics, inferential statistics, and regression analysis are standard data analysis methods used to draw conclusions about the research question. Quantitative research methods have advantages and disadvantages and are widely used in various fields, including social and biological sciences, to study phenomena and behaviour and develop new techniques and interventions. Despite the limitations, the precise measurement of variables, replicability, and ability to identify patterns and relationships in the data make quantitative research methods a valuable tool for researchers.

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Quantitative Research

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• Leigh A. Wilson 2 , 3  

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

• Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors. 

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce. 

• Quantitative research can be done anonymously. 

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary. 

• Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities. 

• Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

• Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

• Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

• Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

• Tests can be intentionally manipulative.  

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

• Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests. 

• More expensive than other forms of testing. 

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates. 

Key Takeaways 

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing. 

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them. 

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

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.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

• Theoretical: limits the scope, depth, or applicability of a study.
• Methodological: limits the quality, quantity, or diversity of the data.
• Empirical: limits the representativeness, validity, or reliability of the data.
• Analytical: limits the accuracy, completeness, or significance of the findings.
• Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility. 

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines. 

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects. 

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

• An unintentional bias encountered during data collection
• An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research. 

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

• Sample size
• Lack of available or reliable data
• Lack of prior research studies on the topic
• Measure used to collect the data
• Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

• Access to information
• Longitudinal effects
• Cultural and other biases
• Language fluency
• Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

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quantitative research advantages and disadvantages

When it comes to conducting research, there are various methods one can employ. One of the most widely used approaches is quantitative research. This method involves the collection and analysis of numerical data to answer research questions. While quantitative research offers several advantages, it also comes with a set of disadvantages that researchers should consider. In this article, we will explore the advantages and disadvantages of quantitative research and discuss why understanding them is important.

Advantages of Quantitative Research

One of the key advantages of quantitative research is its objectivity. By focusing on numerical data, researchers can minimize bias in their analysis. This makes quantitative research highly reliable and allows for more accurate comparisons between different groups or variables.

Another advantage of quantitative research is its potential for generalizability. By using large sample sizes, researchers can draw conclusions that are more likely to hold true for the larger population. This is particularly useful when studying social or psychological phenomena that affect a wide range of individuals.

Additionally, the replicability of quantitative research is worth mentioning. By using standardized procedures and statistical analyses, researchers can easily replicate studies and assess their validity. This not only helps in verifying the results but also contributes to the overall credibility of the research.

Furthermore, quantitative research enables the application of advanced statistical techniques. This allows researchers to uncover patterns, relationships, and trends in their data that may not be readily apparent. These statistical analyses provide a deeper understanding of the research question and can lead to more robust and comprehensive findings.

Disadvantages of Quantitative Research

Despite its advantages, quantitative research also has some limitations that researchers should be aware of. One of the main disadvantages is the lack of contextual understanding. Since quantitative research relies on numerical measures, it may overlook the underlying factors or contexts that contribute to the observed outcomes. This can limit the depth of understanding.

Another disadvantage is the restrictions in question design. Quantitative research generally relies on structured questionnaires or surveys, which limit the types of questions that can be asked. As a result, important nuances or complexities may be overlooked, leading to a less comprehensive understanding of the research topic.

Data validity concerns are also a significant disadvantage of quantitative research. Self-reported measures, such as surveys, are prone to biases and inaccuracies. Participants may provide socially desirable responses or misinterpret the questions, affecting the reliability and validity of the data collected.

Lastly, quantitative research has a limited scope when it comes to exploring complex social, emotional, or cultural phenomena. These phenomena often involve subjective experiences that cannot be easily quantified. Qualitative methodologies, such as interviews or observations, are better suited for capturing the richness and depth of such phenomena.

Benefits of Knowing the Quantitative Research Advantages and Disadvantages

Understanding the advantages and disadvantages of quantitative research is essential for researchers and practitioners alike. By knowing the strengths and weaknesses of this research approach, researchers can make informed decisions about the methodologies they adopt and the questions they ask. This knowledge can also guide the interpretation and communication of research findings, ensuring a more accurate representation of the data.

For practitioners, knowledge of the advantages and disadvantages of quantitative research is valuable in critically evaluating and synthesizing existing research. It helps them recognize the limitations and potential biases in studies, enabling them to make evidence-based decisions in their respective fields.

Overall, being aware of the advantages and disadvantages of quantitative research promotes a more comprehensive and nuanced understanding of research outcomes. It encourages researchers to consider alternative research approaches when necessary and highlights the importance of triangulating findings from different methodologies to gain a more holistic understanding of complex phenomena.

In conclusion, quantitative research offers several advantages, including objectivity, generalizability, replicability, and the application of advanced statistical techniques. However, it also has some disadvantages, such as the lack of contextual understanding, restrictions in question design, data validity concerns, and a limited scope for exploring certain phenomena. By understanding these advantages and disadvantages, researchers can make informed decisions, interpret findings accurately, and contribute to the development of robust research in their respective fields.

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18 Advantages and disadvantages of quantitative research

Quantitative research is a method of gathering and analyzing numerical data to understand a phenomenon or answer a research question.

It involves the use of quantitative data, which can be measured and analyzed using statistical techniques. In this article, we will explore the advantages and disadvantages of quantitative research.

Quantitative research is a method of empirical research that focuses on the systematic collection and analysis of numerical data. 

It is often used in social sciences, natural sciences, and various fields to gather and analyze data to make generalizations, identify patterns, and test hypotheses.

Here are some advantages and disadvantages of quantitative research:

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Advantages of Quantitative Research

• Objective and Reliable Data : Quantitative research method is based on numerical data, making it more objective and less susceptible to researcher bias compared to qualitative research methods.
• Generalizability : To use quantitative research often involves larger sample sizes, which can lead to more generalizable findings and results that can be applied to broader populations.
• Statistical Analysis : It allows for the use of statistical techniques to analyze data, providing a higher degree of precision and the ability to identify patterns and relationships in the data.
• Replicability : Because of its structured and standardized nature, quantitative research can be easily replicated by other researchers, increasing the reliability of findings.
• Quantifiable Outcomes : It is well-suited for measuring and quantifying variables, making it useful for assessing the impact of interventions or treatments.
• Efficiency : Data collection in quantitative method research can be efficient and less time-consuming, especially when using surveys or structured observations.
• Comparative Analysis : Researchers can compare variables, groups, or conditions to identify differences and associations, which can be useful for making informed decisions with data analysis.
• Objective Conclusions : The numerical data generated in quantitative research allows for clear and objective conclusions, facilitating decision-making and policy development.
• Numerical Representation : It provides data that can be graphically represented, making it easier for non-specialists to understand and interpret the findings.

Disadvantages of Quantitative Research

• Simplification : Quantitative research may oversimplify complex phenomena, as it often focuses on variables that can be easily measured, leaving out nuanced or qualitative aspects.
• Lack of Context : It may not capture the full context or meaning behind the data, as it typically does not explore the "why" and "how" of observed relationships.
• Limited Insight : Quantitative research may not provide in-depth insights into people's motivations, emotions, or experiences, which can be better explored through qualitative research methods.
• Difficulty in Capturing Unobservable Constructs : It may not effectively measure abstract or unobservable constructs, such as attitudes, beliefs, or cultural factors, which are better addressed through qualitative research.
• Risk of Measurement Error : Errors can occur during data collection or analysis, leading to inaccurate results. Researchers must take steps to minimize measurement error.
• Lack of Flexibility : Quantitative research typically follows a structured approach, which may limit the ability to adapt to unexpected findings or explore emergent themes.
• Resource-Intensive : Conducting quantitative research involves be resource-intensive, especially when large sample sizes are required or complex statistical analyses are involved.
• Ethical Concerns : The collection of quantitative data, particularly in surveys or experiments, may raise ethical concerns related to privacy of personal information, informed consent, and the potential for harm.
• Difficulty in Exploring Contextual Factors : Quantitative research may not fully capture the influence of contextual factors, such as culture, history, or environment, on the research topic.

Conclusion of Advantages and Disadvantages of Quantitative Research

Quantitative research is a powerful tool that allows researchers to collect and analyze numerical data to understand a phenomenon or answer research questions.

It offers several advantages, including objectivity, generalizability, real-time analysis, and the ability to analyze large datasets, different from the advantages and disadvantages of qualitative research .

However, it is important to recognize its limitations, such as the lack of qualitative richness, the need for a larger sample size, and the inability to explore complex social phenomena.

Researchers should carefully consider the advantages and disadvantages of quantitative research when selecting a research method for their study.

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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

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Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design.  This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

• Presence of increasing findings as researched
• Being population specific
• A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

• Declare that you wish to identify your limitations of research and explain their importance,
• Provide the necessary depth, explain their nature, and justify your study choices.
• Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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