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. 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. 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. 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. Booth; Gregory G. Colomb; Joseph M. Williams Call Number: Q Seasoned researchers and educators, the authors present an updated third edition of their classic handbook which explains how to build an argument that motivates readers to accept a claim; how to anticipate the reservations of readers and to respond to them appropriately; and how to create introductions and conclusions that answer that most demanding question, "So what?
Research Design by John W. Creswell Call Number: H C The third edition of the bestselling text "Research Design" enables readers to compare three approaches to research - qualitative, quantitative and mixed methods - in a single research methods text. Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic and demographic segmentation parameters. Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval.
This interval is calculated by dividing the population size by the target sample size. There are five non-probability sampling models: Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved. Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Snowball sampling: Snowball sampling is conducted with target audiences, which are difficult to contact and get information.
It is popular in cases where the target audience for research is rare to put together. Using surveys for primary quantitative research A Survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. Fundamental levels of measurement — nominal, ordinal, interval and ratio scales There are four measurement scales that are fundamental to creating a multiple-choice question in a survey.
Use of different question types To conduct quantitative research, close-ended questions have to be used in a survey. Survey Distribution and Survey Data Collection In the above, we have seen the process of building a survey along with the survey design to conduct primary quantitative research. Some of the most commonly used methods are: Email: Sending a survey via email is the most widely used and most effective method of survey distribution.
The response rate is high in this method because the respondents are aware of your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample.
Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher. Embed survey on a website: Embedding a survey in a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand. SMS survey: A quick and time-effective way of conducting a survey to collect a high number of responses is the SMS survey. QuestionPro app: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline. Survey example An example of a survey is short customer satisfaction CSAT survey template that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.
Using polls for primary quantitative research Polls are a method to collect feedback with the use of close-ended questions from a sample. Data analysis techniques The third aspect of primary quantitative research design is data analysis.
Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement. Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study. It is used for understanding the potential of a target market.
Secondary quantitative research methods Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Following are five popularly used secondary quantitative research methods: Data available on the internet: With the high penetration of internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet.
Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data as well as proving the relevance of previously collected data. Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information though. Public libraries have copies of important research that were conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted. Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are a great source to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on economic developments, political agenda, market research, demographic segmentation, and similar subjects.
Quantitative research characteristics Some distinctive characteristics of quantitative research are: Structured tools: Structured tools such as surveys, polls, or questionnaires are used to gather quantitative data. Using such structure methods helps in collecting in-depth and actionable data from the survey respondents. Sample size: Quantitative research is conducted on a significant sample size that represents the target market.
Appropriate sampling methods have to be used when deriving the sample to fortify the research objective Close-ended questions: Closed-ended questions are created per the objective of the research. These questions help collect quantitative data and hence, are extensively used in quantitative research.
Prior studies: Various factors related to the research topic are studied before collecting feedback from respondents. Quantitative data: Usually, quantitative data is represented by tables, charts, graphs, or any other non-numerical form. This makes it easy to understand the data that has been collected as well as prove the validity of the market research. Generalization of results: Results of this research method can be generalized to an entire population to take appropriate actions for improvement.
Quantitative research examples Some examples of quantitative research are: If any organization would like to conduct a customer satisfaction CSAT survey , a customer satisfaction survey template can be used. Through this survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the mind of the customer based on multiple parameters such as product quality, pricing, customer experience, etc.
This data can be collected by asking a net promoter score NPS question , matrix table questions, etc. Another example of quantitative research is an organization that conducts an event, collecting feedback from the event attendees about the value that they see from the event.
By using an event survey template , the organization can collect actionable feedback about satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.
What are the advantages of quantitative research? Some of the major advantages of why researchers use this method in market research are: Collect reliable and accurate data: As data is collected, analyzed, and presented in numbers, the results obtained will be extremely reliable. 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. 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.
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. 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. 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. 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. 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? Reliability and validity are both about how well a method measures something: Reliability refers to the consistency of a measure whether the results can be reproduced under the same conditions.
Validity refers to the accuracy of a measure whether the results really do represent what they are supposed to measure. What is hypothesis testing? Is this article helpful? Pritha Bhandari Pritha has an academic background in English, psychology and cognitive neuroscience. As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics. Other students also liked. A guide to operationalization Operationalization means turning abstract concepts into measurable observations.
It involves clearly defining your variables and indicators. An introduction to descriptive statistics Descriptive statistics summarize the characteristics of a data set. There are three types: distribution, central tendency, and variability. An introduction to inferential statistics While descriptive statistics summarize data, inferential statistics help you come to conclusions and make predictions based on your data.
What is your plagiarism score? Scribbr Plagiarism Checker. Control or manipulate an independent variable to measure its effect on a dependent variable. To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task.
You compare self-ratings of procrastination behaviors between the groups after the intervention. You distribute questionnaires with rating scales to first-year international college students to investigate their experiences of culture shock. To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds. Collect data that has been gathered for other purposes e.
To assess whether attitudes towards climate change have changed since the s, you collect relevant questionnaire data from widely available longitudinal studies.
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