Examples of Methodology in Research Papers (With Definition)
Updated September 30, 2022
Published July 25, 2022
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When researchers record their findings, they often include a methodology section that details the research techniques used and outcomes. When writing a thesis or dissertation, or documenting a project for your employer, including details about methodology assists readers in understanding your findings. Learning more about the concept and reviewing examples of methodology is important for providing insight into the validity and reliability of research.
In this article, we explain why it's important to review examples of methodology, explore what a methodology is, highlight what it includes, learn how it differs from research methods, and discover an example of methodology in a research paper.
Why review examples of methodology?
If you're writing a thesis, it may be useful to review some examples of methodology. By reviewing these examples, you can learn more about research approaches that give credibility to studies. You can also learn more about the language used and the details included, which can help you make your own methodology sections of reports more effective.
What is a methodology in a research paper?
In a research paper, thesis, or dissertation, the methodology section describes the steps you took to investigate and research a hypothesis and your rationale for the specific processes and techniques used to identify, collect, and analyze data. The methodology element of your research report enables readers to assess the study's overall validity and reliability and provides an important insight into two key components, namely your data gathering and analysis techniques and your reason for investigating. When composing this section for a research paper, it's important to keep the topic concise and write in the past tense.
What to include in a methodology section
When developing a methodology for research papers, it's worth considering the following elements:
Type of research
The first part of a methodology section typically outlines the type of research you did, and how you established your research procedures. This section highlights the subject of your study and addresses the type of data necessary to conduct evaluations and research assessments. The methodology section commonly contains the criteria that your experimental investigations followed to provide valid and trustworthy data. The material in this section provides readers with an insight into the methods you used to assess validity and reliability throughout your investigations.
Data collection process
The methodology section also contains a description of how you collected the data. Whether you ran experimental testing on samples, conducted surveys or interviews, or created new research using existing data, this section of your methodology describes what you did and how you did it. Key aspects to mention include how you developed your experiment or survey, how you collected and organized data, and what kind of data you measured. Additionally, you may outline how you set particular criteria for qualitative and quantitative data collection.
Data analysis process
Your approach to data analysis is equally important to the processes of data collection. The term data analysis refers to the procedures you employed to organize, classify, and examine the data gathered throughout your research operations. For instance, when presenting your quantitative approaches, you may add information regarding the data preparation and organization procedures you used and a short description of the statistical tests involved. When presenting your qualitative data analysis techniques, you may prefer to concentrate on how you classified, coded, and applied language, text, and other observations throughout your study.
Resources, materials, and tools
The tools, materials, and other resources necessary for conducting your research and analysis are also important factors to include when outlining your approach. In documenting your processes, it's important to outline your use of software programs, mathematical and statistical formulae, and other instruments that assisted you in your study. Additionally, this area of your approach may describe any unique strategies you used to gather data and identify significant factors. The methods you used to investigate your hypothesis and underlying research questions are also key components of your methodology.
The rationale behind the research
Because the methodology section of your research paper demonstrates to readers why your study is legitimate and important, the final part of this section can concentrate on your justification for the research. Details such as why your studies are important, which sectors they pertain to, and how other researchers might reproduce your findings are critical components of this section. It's important to discuss any strategies you intend to employ to continue reviewing your research and to properly reference the primary and secondary sources you utilized.
Differences between the methodology and research methods
While the methodology section of your research paper contains information about the research techniques you employed, there are many distinctions between the methodology and the actual research methods you used, including:
The overall objective of your approach is distinct from the procedures you used to carry out your study. While the methodology section of your research paper describes your processes in detail, the methods section refers to the specific steps you took to collect and analyze data throughout your research. The methodology acts as a summary that proves the validity and dependability of your procedures, while the methods are the scientific ways to test and reach conclusions about the data you investigate.
The structure of the methodology section differs from how you describe and explain your research and analytic approaches. The methodology section is often located at the beginning of your article and takes the form of a summary or essay in paragraphs, outlining the validity, procedure, and justification for your study. The structure in which you discuss your methods varies according to the type of study, data, and evaluations used. For example, when presenting the methods, you may use a graph or chart to illustrate your results.
The objectives and style of your methodology and research techniques ultimately impact on the material that you present. It's important that your methodology provides a succinct review of your research, methods, and findings. As a result, the methodology section of your paper can include the elements you employed to conduct your investigations. The content of your research paper that describes your methods of data collection and analysis techniques may vary, as it's often required to clarify your scientific approaches and research procedures using lists and visual aids, such as charts or graphs, to supplement the material.
Example of a methodology in a research paper
The following example of a methodology in a research paper provides insight into the structure and content to consider when writing your own:
This research article discusses the psychological and emotional impact of a mental health support program for employees. The program provided prolonged and tailored help to job seekers via a job support agency that kept contact with applicants beyond initial job placement to give different forms of assistance. I chose a 50% random selection of respondents who participated in the employment agency's support program between April and October and met the research criteria I created based on prior and comparable studies.
My colleagues and I randomly allocated the 350 resultant patients to the treatment or control groups, which included life skills development and career training in an in-house workshop setting. My colleagues and I assessed the 350 participants upon admission and again after they reached the 90-day employment requirement. The psychological functioning and self-esteem assessments we conducted revealed considerable evidence of the impact of treatment on both measures, including results that contradicted our original premise.
We discovered that, rather than demonstrating better functioning and higher self-esteem, participants in the therapy group exhibited poorer cognitive and emotional functioning and self-esteem. These findings prompted my study team and me to conclude that people who consider themselves unfulfilled in their jobs often endure a substantial decline in performance as a consequence of increased workplace stress and lower emotional well-being, irrespective of their mental health status.
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Research paper methodology.
The methodology applied to any research endeavors is an important aspect of the project. The methodology must be appropriate for the study to ensure that the results of the study are accurate and pertinent to the subject matter. The purpose of this research is to study the increase of recidivism rates in the U.S.
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Data for this research will be obtained through the Internet and will be a study of the data available concerning the subsequent arrests of offenders who have been arrested and convicted previously. The data mining efforts will include resources such as Google Scholar and the on-line library to obtain studies that have been performed by other researchers concerning this topic. This provides the opportunity to obtain scholarly articles in peer-reviewed journals as well as data that has been published in books. Any agencies, such as the Department of Justice, will be searched to determine if data is available from those sources concerning recidivism rates. Other sources will be utilized provided the data retrieved is applicable to the subject matter of the study and the data is verifiable (Amsberry, 2008).
The methodology that will be employed for the course of this research is offered through the methodology as presented through quantitative research. Quantitative research methodology provides researchers the ability to conduct empirical research through the investigation of variables that are observable and measurable. This methodology is appropriate to test theories, predict outcomes, and determine the relationships that exist between variables through the implementation of statistical analysis. This methodology involves the use of primary data collection and a secondary data analysis. The primary data collection is conducted through the use of electronic methods, such as the Internet, or in person by the researcher. Statistical analysis is applied to the data retrieved to assess the relationships between and among the variables presented. This methodology allows for research to be performed on studies conducted by other authors where the psychometric properties consisting of reliability and validity have been established serving as the basis of measuring the variables. Secondary data analysis is the statistical analysis of data sets that are publicly available as presented by other researchers or organizations. The research question presented must by based on the available data. The methodology selected must be based on the nature of the questions presented, the state of the field, and the feasibility of the research (Capella University, 2015). This methodology allows for the data collection processes as required by an empirical study of the evidence available.
There are no known exclusions for this study as many law enforcement agencies provide this information as a public service. No personal information will be divulged as this research focuses solely on the rate of recidivism and is not concerned with the types of crimes that have been committed. Certain demographical information, such as sociodemographic information may be employed as a means to determine trends in recidivism.
Care does need to be applied to the data obtained to ensure that certain offenses are considered once in order to ensure the accuracy and integrity of the results. This entails obtaining data for specified time frames, such as a certain year, from one source instead of a multitude of sources for the same period of time. If the data is obtained from multiple sources, the results would overinflate the rate of recidivism. Another area of concern is the possibility of incomplete statistics available for any period of time as it will provide an incomplete view of the rate of recidivism. Efforts will be undertaken to ensure the quality and quantity of the data retrieved.
The hypothesis that the rate of recidivism is increasing will be reflected by the number of incidences reported compared to the same length of time for a previous year. The hypothesis will be proven if the rate of recidivism increases and will be disproved if the rate of recidivism decreases.
- Amsberry, D. (2008). What is Empirical Research? Penn State University Libraries. Retrieved from https://www.libraries.psu.edu/psul/researchguides/edupsych/empirical.html
- Capella University (2015). What Are Acceptable Dissertation Research Methods? Retrieved from http://www.capella.edu/blogs/cublog/acceptable-dissertation-research-methods/
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Sample of Methodology in Research Paper
Introduction
The methodology part of the study tries to pinpoint the procedure or set of actions used at each stage of the investigation to arrive at the research’s results and conclusions. Kothari (2004) defines research methodology as “a way to systematically solve the research problem”. He further opines that research methodology may be comprehended as “a science of studying how research is done scientifically” (Kothari, 2004).
Thus the research methodology for the present study would include the data collection methods which would entail the description of the collection of primary as well as secondary data as well as the reason for doing so; the research design which refers to the inductive and deductive reasoning methods along with the qualitative and quantitative techniques for the purpose of the study. The instrument of data collection would also be derived in this section by the help of which the data has been collected – be it an interview, or a questionnaire or a focus group study. Again, the sample selection is the next activity to be undertaken since the data has to be collected from the respondents for further probing. Thus the sample has to be selected depending on various factors like nature of study, intended results, convenience, paucity of time and funds etc. Once the data has been collected the data is analysed with the help of various tools and techniques. Ideally, the step of data analysis consists of the ways in which the data would be put to test so as to derive the intended results. The tools of data analysis may be software or manual. In this case, SPSS would be used to analyse the data with the help of various tests predetermined by the researcher. Lastly, the ethical considerations that the researcher has to keep in mind while working with the sample population would be enumerated followed by a summary of the section.
Data Collection Methods
Primary research.
When a study is performed to reveal original data, it is known as primary research. To do this a new research plan has to be created which consists of collection of data, input of data and ultimately the production as well as analysis of the ensuing results.
Whereas “Original data performed by individual researchers or organizations to meet specific objectives is called primary data” (Schiffman & Kanuk, 2007).
The major advantage to primary research is that the data collected for the study is solely meant for that very study and thus much more precise and “reliable” as well (Das, 2005).
Primary data exhibit comprehensive information and an explanation of the terms mentioned. The method of collection and approximations, if there are any, are detailed in this case (DJS Research, 2005).
But the main drawback is that the procedure may be that it might prove to be highly time consuming and an expensive affair.
Secondary Research
Secondary Research is the research conducted on the basis of the “data that has been previously collected by some agency or person for some purpose and are merely compiled from that source for use in a different connection”. So, data collected by someone else when utilised by another person, is called secondary data as well. Primary data transforms into secondary data when the person who has collected the data uses the already collected data by him/her for some other reason other than for the reason it was originally collected (Roy Choudhury & Bhattacharya, 2006).
Secondary research is generally beneficial when the researcher has time constraint or financial constraints. The investigator is unable to directly collect the data and also when absolute accurateness is not mandatory.
In the case of the present study, the data collected by other researchers as well as the quantitative and also the qualitative approaches have to be overviewed so as to have added knowledge on the area.
In the context of the present study, secondary data will also serve the purpose to some extent – especially while assessing the available literature to reinforce that the present study is not a rather stand alone study on the subject that has yielded the results but available literature also focuses on and has reference to similar topics or has much to offer on the topic of the present study. It will also help them assess industry figures and the like.
Research Design
A research design refers to coming up with the most efficient plan of collecting the information needed for the research (Kumar, 2008). A research design also describes the specification of methods and processes for collecting the information needed. It is the framework for performing the study. Since the research objectives and questions have been formulated, so the research design is to be prepared subsequently (Gupta & Gupta, 2011).
Among the approaches taken in the course of the study, the reasoning based approaches will lend more credibility as to why the courses of action have been in a certain format.
Inductive Reasoning
Taylor, Sinha and Ghoshal (2006) have noted that inductive reasoning is “a process that begins with a specific case and draws from it a conclusion of wider or more general reference”.
Deductive Reasoning
Ghauri and Gronhaug (2002) define deductive reasoning as an approach wherein it is a logical process of deriving a conclusion from a known premise or something known as true.
Again, for the purpose of the study, a combination of quantitative as well as qualitative approaches has been taken. While the qualitative approach consists of scaling technique in terms of letting the respondents choose only one answer for the questions given within the questionnaire, the quantitative approaches, on the other hand, would comprise specific tools like correlation.
Correlation
Correlation, as noted by Wild and Diggines (2009), refers to methods that deal with simultaneous occurrences in variables.
The Instrument for Data Collection
The instrument of data collection chosen is a questionnaire.
Questionnaire
“Questionnaire, whether it is called a schedule, interview form, or measuring instrument, is a formalized set of questions for obtaining information from respondents.” (Malhotra, 2004). Normally, a questionnaire is a type of a package used for collection of data that may also comprise other things.
Interview is an instrument of data collection wherein the researcher interviews the respondent in person to gather the required information. Again, the success of this method depends on the merits of the interviewer since he or she may also note other non verbal reactions of the respondents. However, any bias should be avoided while making such judgments. Also, this is time consuming and costly for research.
In the context of the present study, a survey Questionnaire seems to be the best way to collect information. Since the present study entails gathering information on aspects that are highly qualitative in nature, so, Questionnaire has been used as an instrument. Focus group has been avoided due to paucity of time and other allied resources. On the other hand, the Questionnaire would enable the present study to hand over the same to the prospective respondents so that they can come up with their versions of answers for the questions at their convenient time.
The questionnaire open ended questions, i.e. the answers have to be given by the respondents in their own words. The Questionnaire has been devised in this manner since focus group interviews are not possible and neither is complete enumeration is feasible, so in order to better comprehend the various processes entailed in the study, it seems better to collect answers in the own language of the respondents. The open ended questions would serve to enlighten the study the particularities of the experiences as well as the difficulties faced by the respondents in the light of the context of the present study. Moreover, this may help to suffice the lack of focus group interviews and other in depth qualitative techniques associated with data collection. Also, since the study is related to motives of individuals, so it is best to go for open ended questions.
Sample Selection
Target population for the primary data comprised the general aspirants of the Air Force Officer Qualifying Test (AFOQT).
For secondary data both internal and external sources has been used. The secondary sources stem majorly from the reports, books available in libraries as well as in the market, journals and other publications that focus on same or similar topics as the present study is based.
The questionnaire would be mailed to the respondents electronically since the study focuses on Air Force Officer Qualifying Test (AFOQT), given that the respondents are in dispersed geographical locations, so the best format seems to be to use the same channel of information sharing. However, a low rate of response may be a drawback compared to personal communication. A total of 100 people were chosen to participate in the research, since the rate of response, as mentioned before, has been apprehended to be low, given that the questionnaire has been mailed to the responders electronically.
Data Analysis
Sampling technique.
Due to its inherent simplicity as well as the time constraints for the completion of the research, “Non Probability Judgmental Sampling Technique” would be used for the study.
Non-probability sampling is a sampling method that is not based on probability wherein the probability of selection of each sampling unit is not known (Aggarwal, 2010).
Measurement Tool to be Used
Aptitude test.
Aptitude tests provide a high level of predictive validity (Hardison, Sims & Wong, 2010). The authors have noted that the aptitude based models like AFOQT offer a high level of unbiased as well as effective way of mapping the life experiences of the candidates as well as measure scores that tally with how good or badly is a student likely to fare on the job for which the test is being taken (Hardison, Sims & Wong, 2010).
Predictive Validity
Elmes, Kantowitz and Roediger (2006) have noted that predictive validity relates one measure of behavior to another criterion measure. The connection between selection test results and future performance on the specified job given to the applicant, on the other hand, is known as predictive validity of a selection test (Hardison, Sims & Wong, 2010). Thus it is clear that the score taken on the basis of the performance of the candidate on the job is later correlated with that of the test score in order to find that the predictive validity of the test holds good or not.
The current research focuses on the Air Force Officer Qualifying Test (AFOQT) and those who want to take it. Thus the research methodology adopted for the research has been tailored to suit the specific requirements. While stress has been given on both quantitative and qualitative approaches, inductive reasoning has also been undertaken. A survey questionnaire has been devised to be sent to responders, comprising questions on gaining insights on Air Force Officer Qualifying Test and also the aspirants who want to become Air Force Officers in course of time.
- Kothari, C. (2004). Research Methodology: Methods & Techniques . New Delhi: New Age International
- Taylor, B., Sinha, G. & Ghoshal, T. (2006). Research Methodology. New Delhi: PHI
- Ghauri, P. & Gronhaug, K. (2002). Research Methods in Business Studies: A Practical Guide . New Delhi: Doris Kindersley India Ltd.
- Kumar, R. (2008). Research Methodology . New Delhi: APH Publishing Corporation
- Gupta, M. & Gupta, D. (2011). Research Methodology . New Delhi: PHI
- Schiffman, L. & Kanuk, L. (2007). Consumer Behavior . NJ: Prentice Hall
- Das, N. (2005). Statistical Methods . Calcutta: M. Das & Co.
- DJS Research Ltd. (2005), What is Primary Research? , retrieved on December 15, 2010, from http://www.marketresearchworld.net/index.php?option=com_content&task=view&id=802
- Roy Choudhury, S. & Bhattacharya, D. (2006). Statistical Methods . Kolkata: U.N. Dhur & Sons
- Malhotra, N. (2004). Marketing Research – An Applied Orientation . Delhi: Pearson
- Aggarwal, B. (2010). Business Statistics . New Delhi: Ane Books
- Elmes, D., Kantowitz, B. & Reodiger, H. (2006). Research Methods in Psychology . CA: Wadsworth Cengage Learning
- Hardison, C., Sims, C. & Wong, E. (2010). The Air Force Officer Qualifying Test: Validity, Fairness, and Bias . CA: Rand Corporation
- Wild, J. & Diggines, C. (2009). Marketing Research. Cape Town: Juta & Co. Ltd.
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How To Write The Methodology Chapter
The what, why & how explained simply (with examples).
By: Jenna Crossley (PhD). Reviewed By: Dr. Eunice Rautenbach | September 2021
So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper. But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .
Overview: The Methodology Chapter
- The purpose and function of the methodology chapter
- The importance of the chapter/section
- How to write and structure the methodology chapter
- General advice for writing a great methodology section
What (exactly) is the methodology chapter?
Your methodology chapter is where you highlight the philosophical underpinnings of your research and outline the specific research design choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your research and to justify your design choices .
The methodology chapter should comprehensively describe and justify all the research design choices you made. For example, the type of research you conducted (e.g. qualitative or quantitative ), how you collected your data, how you analysed your data and who or where you collected data from (sampling). We’ll explain all the key design choices later in this post .
Why is the methodology chapter important?
The methodology chapter is important for two reasons:
Firstly, it demonstrates your understanding of research design theory, which is what earns you marks. A flawed research design or methodology would mean flawed results, so this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .
Secondly, the methodology chapter is what helps to make your study replicable – in other words, it allows other researchers to undertake your study using the same design, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.
The methodology chapter is also important because it allows you to identify and discuss any methodological issues or problems you encountered (i.e. limitations), and to explain how you mitigated the impacts of these. Every research project has its limitations and shortcomings , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations. Again, this demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in more detail later in this post.
Need a helping hand?
How to write up the methodology chapter
First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (for example, humanities vs chemistry vs engineering) as well as the university . So, it’s always a good idea to check the guidelines provided by your institution for clarity and, if possible, review past dissertations and theses from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences, especially the social sciences (e.g. psychology).
Before you start writing, we always recommend that you draw up a rough outline , so that you have a clear direction to head in. Don’t just start writing without knowing what will go where. If you do, you’ll most likely end up with a disjointed, poorly flowing narrative . As a result, you’ll waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Start with the end in mind.
Section 1 – Introduction
As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this introduction, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on this blog, your research design needs to align with your research aims, objectives and research questions , so it’s useful to frontload this to remind the reader (and yourself!) what you’re trying to achieve with your design and methodology.
In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect.
Section 2 – The Research Design
The next section of your methodology chapter should present your research design to the reader. In this section, you need to detail and justify all the key design choices in a logical, intuitive fashion. This is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.
Let’s have a look at the most common design choices you’ll need to cover.
Design Choice #1 – Research Philosophy
Research philosophy refers to the underlying beliefs (i.e. world view) regarding how data about a phenomenon should be gathered , analysed and used . Your research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any research design choices.
While several research philosophies exist, two commonly adopted ones are positivism and interpretivism .
Positivism is commonly the underlying research philosophy in quantitative studies. It states that the researcher can observe reality objectively and that there is only one reality, which exists independent of the observer.
Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .
These are just two philosophies (there are many) , but they demonstrate significantly different approaches to research and have a significant impact on all the research design choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.
Design Choice #2 – Research Type
The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive . With inductive research, theory is generated from the ground up (i.e. from the collected data), and therefore these studies tend to be exploratory in terms of approach. Deductive research, on the other hand, starts with established theory and builds onto it with collected data, and therefore these studies tend to be confirmatory in approach.
Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed methods methodology. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . Again, when you write this section up, remember to clearly justify your choices, as they form the foundation of your study.
Design Choice #3 – Research Strategy
Next, you’ll need to discuss your research strategy (i.e., your research “action plan”). This research design choice refers to how you conduct your research based on the aims of your study.
Several research strategies exist, including experiments , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s look at two these, experimental and ethnographic, to see how they contrast.
Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a variable is manipulated). This type of research is undertaken under strict conditions in controlled, artificial environments – for example, within a laboratory. By having firm control over the environment, experimental research often allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying or measuring cause and effect.
Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment. Naturally this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.
As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other design choice, it’s essential to justify why you chose the research strategy you did.
Design Choice #4 – Time Horizon
The next thing you need to cover in your methodology chapter is the time horizon. There are two options here – cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (i.e. cross-sectional) or at multiple points in time (i.e. longitudinal).
The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.
Another important factor is simply the practical constraints – in other words, whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.
Design Choice #5 – Sampling Strategy
Next, you’ll need to discuss your chosen sampling strategy . There are two main categories of sampling, probability and non-probability sampling. Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-randomized (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).
The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample.
Design Choice #6 – Data Collection Method
Next up, you need to explain how exactly you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.
Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.
So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections is therefore very important.
Design Choice #7 – Data Analysis Methods/Techniques
The final major design choice that you need to address is that of analysis techniques . In other words, once you’ve collected your data, how will you go about analysing it. Here it’s important to be specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.
What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . For quantitative studies, you’ll almost always make use of descriptive statistics, and in many cases, you’ll also use inferential statistical techniques (e.g. correlation and regression analysis).
In this section, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . As always, remember to state both what you did and why you did it.
Section 3 – The Methodological Limitations
With the key research design choices outlined and justified, the next step is to discuss the limitations of your design. No research design or methodology is perfect – there will always be trade-offs between the “ideal” design and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.
Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .
In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research design, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations.
Section 4 – Concluding Summary
Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be useful to use a figure to summarise the key design decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).
Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.
Wrapping up
And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).
Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.
If you need a helping hand with your research methodology (or any other section of your dissertation or thesis), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!
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This post is part of our research writing mini-course, which covers everything you need to get started with your dissertation, thesis or research project.
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Methodology in a Research Paper: Definition and Example
Updated December 12, 2022
Published May 11, 2021
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
When researchers document their studies, they typically include a methodology to describe the processes and outcomes of their research. If you're covering a thesis topic, submitting a dissertation or documenting a project for your employer, including a methodology helps summarize your studies for readers who review your work. The methodology is also important to provide insight into the validity and reliability of your research.
In this article, we explore what a methodology is, what to include in this part of your paper and how it differs from your research methods with an example of methodology in a research paper.
What is a methodology in a research paper?
The methodology in a research paper, thesis paper or dissertation is the section in which you describe the actions you took to investigate and research a problem and your rationale for the specific processes and techniques you use within your research to identify, collect and analyze information that helps you understand the problem.
The methodology section of your research paper allows readers to evaluate the overall validity and reliability of your study and gives important insight into two key elements of your research: your data collection and analysis processes and your rationale for conducting your research. When writing a methodology for a research paper, it's important to keep the discussion clear and succinct and write in the past tense.
Quantitative and qualitative methodologies
There are two main approaches to methodology; quantitative and qualitative. Quantitative research methodology relies on concrete facts and data-driven research, and qualitative research methodology relies on non-data-driven research, such as surveys and polls, to identify patterns and trends.
What to include in a methodology
Students, graduates and other researchers often include several key sections within the methodology section. Consider the following elements when developing a methodology in research papers:
Type of research
The first part of a methodology section usually describes the type of research you perform and how you develop your research methods. This section also discusses the question or problem you investigate through your research and the type of data you need to perform evaluations and research assessments. Additionally, the methodology often includes the criteria your experimental studies need to meet to produce valid and reliable evidence. The information you cover in this part of your methodology allows readers to gain insight into how you measure validity and reliability during your studies.
Data collection process
The methodology also includes an explanation of your data collection process. For instance, if you perform experimental tests on samples, conduct surveys or interviews or use existing data to form new studies, this section of your methodology details what you do and how you do it. Several key details to include in this section of a methodology focus on how you design your experiment or survey, how you collect and organize data and what kind of data you measure. You may also include specific criteria for collecting qualitative and quantitative data.
Data analysis process
Your data analysis approaches are also important in your methodology. Your data analysis describes the methods you use to organize, categorize and study the information you collect through your research processes. For instance, when explaining quantitative methods, you might include details about your data preparation and organization methods along with a brief description of the statistical tests you use. When describing your data analysis processes regarding qualitative methods, you may focus more on how you categorize, code and apply language, text and other observations during your analysis.
Resources, materials and tools
The tools, materials and other resources you need for your research and analysis are also important elements to describe in your methodology. Software programs, mathematical and statistical formulas and other tools that help you perform your research are essential in documenting your methodology. This section of your methodology can also detail any special techniques you apply to collect data and identify important variables. Additionally, your approaches to studying your hypothesis and underlying research questions are essential details in your methodology.
Rationale behind the research
Since your methodology aims to show readers why your research is valid and relevant, the last part of this section of your research paper needs to focus on your rationale. Details like why your studies are relevant, what industries your studies relate to and how other researchers can replicate your results are essential components of this part of your methodology. It's important to address any approaches you plan to take to continue evaluating your research over time and to cite the primary and secondary sources you use in your research.
Differences between the methodology and methods
Although the methodology section of your research paper includes details about the methods you use in your research, there are several differences between a methodology and the research methods you apply:
The overall purpose of your methodology differs from the set of methods you use to apply to your research. While the methodology is the entire section of your research paper that describes your processes, the methods refer to the actual steps you take throughout your research to collect and analyze data. The methodology serves as a summary that demonstrates the validity and reliability of your methods, while the methods you detail in this section of your paper are the scientific approaches to test and make conclusions about the data you study.
The format for a methodology differs from the format you use to list and explain your research and analysis methods. The methodology usually appears at the beginning of your paper and looks like a summary or essay in paragraph form detailing your research validity, process and rationale. The format you use to describe your research and analysis methods can take various forms, depending on the type of research, type of data and type of assessments you use.
For instance, when describing the methods you use to perform quantitative and statistical analyses, the format you use may focus on a graph or chart to display your data. Additionally, the methods you describe within each part of your methodology can include tables or lists to demonstrate your research process and outcomes.
The purpose and format ultimately influence the content that you include in both your methodology and your research method details. However, the content within your entire methodology focuses on delivering a concise summary of your research, approaches and outcomes. Therefore, the content of your methodology includes all aspects of performing your studies. The content in your research paper that details your collection and analysis methods differs because it's often necessary to explain your scientific approaches and research processes with lists and visual aids (like charts or graphs) to support the information.
Example of a methodology in a research paper
The following example of a methodology in a research paper can provide additional insight into what to include and how to structure yours:
This research paper explains the psychological and emotional effects of a support program for employees with mental illness. The program involved extended and individualized support for employment candidates through a job support agency that maintained contact with candidates after initial job placement to offer support in various ways. I used a 50% random sampling of individuals who took part in the support program through the job support agency between April and October, and who fit the study criteria I developed from previous and similar studies.
My team and I randomly assigned the resulting 350 cases to either the treatment group or the control group, which comprised life skills development and employment training within an in-house workshop environment. My team and I measured all 350 participants upon intake and again at the 90-day threshold of employment. The psychological functioning and self-esteem measurements we used provided significant data on the effects of treatment within both measures, including opposing outcomes that differed from our initial hypothesis.
We found through our research that instead of improved function and higher self-esteem, the individuals within the treatment group displayed lower levels of cognitive and emotional function and lower self-esteem. These results led my research team and I to conclude that individuals who work in roles they find unfulfilling often experience significant decreases in performance due to higher job stress and diminished emotional well-being, regardless of their mental health conditions.
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- Research Paper Methods Section: Guide and Examples
The research paper methods section is an extremely important part of the research paper.
Among scholars, it is common in undertakings such as dissertations and capstone projects .
It is therefore important to understand what the section entail.
In simple language, the research paper methods section can be termed as:
A research section that describes the actions taken when investigating a research problem and the rationale behind the application of particular techniques and procedures in identifying, selecting, processing, and analyzing information applied in understanding the problem.
Note that in the case of a research proposal , the methods section describes the actions to be taken.
Importance of the Research Paper Methods Section
The research paper methods section is important in numerous ways.
Such ways include:
1. The methods section is critical because the procedures adopted in it determines the reliability of the results obtained and the value of the analysis of findings made.
2. It informs the readers how data collection was done since the methods used affects results.
In this, the methods section lays the foundation for thediscussions section, where it explains the interpretation of the results’ significance.
3. The research paper methods section clearly expresses the reasons for choosing a specific technique or procedure among other different methods that could be used to investigate a research problem.
4. The methods section illustrates the appropriateness of the procedures and methods in achieving the overall aims of the study.
For instance, a good example of participants section in research proposal should include a sample size suitable to support the generalization of findings.
5. It provides the leader with evidence on whether data collection was done in consistency with accepted practices of the particular field.
For instance, if an interview was used to collect data, it is important to know whether it was facilitated honesty from the participant.
Generally, the methods section determines the appropriate types of research instruments for a particular research study.
Approaches to the Methods Section
The research paper methods section could apply two main approaches.
These approaches are:
1. Interpretive approach
Research methods under this category focus on understanding the phenomenon comprehensively and holistically.
They seek to analytically disclose the practices that make meaning to human subjects.
The methods examine how, why, and what about people and strive to demonstrate how the practices by human subjects are arranged with the intention of generating observable outcomes.
These methods enable the researcher to identify their connection with the phenomenon being investigated.
The approaches are more subjective and therefore require careful scrutiny of entailed research study variables.
2. Empirical-analytical approach
This approach to the research paper methods section handles social sciences the same way as natural sciences.
The approach is founded on objective knowledge, where a good methods section of research proposal example would for instance use questions that seek yes or no answers.
Further, it focuses on the operational definitions of the research study variables to be measured.
The approach uses deductive reasoning that is based on existing theory as its foundation in the formulation of the hypotheses that require testing.
It is more focused on “explanation”.
Structure of the Research Paper Methods Section
The research paper methods section should be well organized to ensure that it fulfills its purpose.
Such organization is usually realized through various subsections.
To enhance this organization, the methods section should start with an introduction.
The introduction of the research paper methods section should:
1. Restate the research problem
2. Stipulate the underlying assumptions
Then the section should go ahead and stipulate the methods for gathering, analyzing, and processing relevant research data.
Note that it is important to provide justification for the choice of methods made particularly if they lie outside the tradition of the field of the study.
Key subsections include:
1. Participants
In the research paper methods section, this subsection describes the participants involved in the research study.
Information to be included about the participants encompass:
1. Who they were (the unique features that distinguish them from the general population)
2. Their number
3. Their selection method
In line with the above observations, example of participants section in research proposal would therefore be as follows:
“200 workers were randomly selected from tech companies in the Midtown Palo Alto.”
Importantly, this subsection should at least provide the participants’ basic demographic characteristics (like age, sex, ethnicity, religion, etc.), the study’s population, and restrictions on the participants’ pool.
For example, if the research study’s participants entailed male nurses from a local private hospital, it should be noted in this subsection as illustrated below.
“The study’s participants included male nurses from 3 hospitals in Kingman rural Arizona.”
The subsection should also give information on how the number of participants assigned the conditions was arrived at and the criteria used in their selection.
On assignment of conditions, the subsection should explain:
1. The participants selection method employed
2. Reasons why the participants took part in the research study
3. Where the study was advertised
4. Whether an incentive was given for participation
2. Materials
Materials as a key part of the research paper methods section requires you to describe the materials, equipment, measures, or stimuli used in the research.
It covers technical equipment, testing instruments, and other materials used in conducting the research.
All the data collection and measurement instruments should covered in this subsection.
For instance, data instruments used in a nursing bedside shift study and psychological assessment tool used in a psychological study should appear in the materials subsection.
The materials methods section of research proposal example would therefore appear as below:
“A structured questionnaire was used to assess nurses’ beliefs on the role of patient involvement in bedside shifts in enhancing patient safety.”
Note that you could include standard equipment like videos and computers without explaining their use in details.
The important thing to understand is that you should provide adequate details for specialized equipment used in research.
For example, data collection instruments created for a specific niche should be illustrated by succinctly highlighting them in the methods section and then including them in the appendix.
3. Design
Design is also a very important part of the research paper methods section.
In this subsection, you should provide details of the design employed in the research study.
The design should specify the research variables and their levels.
You should clearly identify:
1. Independent variables
2. Dependent variables
3. Control variables
4. Any extraneous variables that might affect the results
All relevant details on design elements and factors should be included in this subsection.
For instance, in an example of methods section in scientific paper, the design should clarify whether the experiment used a between-groups or within-groups design.
4. Procedure
The procedure subsection should cover the procedures used in the research study or experiment.
It should explain:
1. What the participants did
2. How data was collected
3. The steps followed in data collection
Accordingly, a methods section of research proposal example for procedure would be as following:
“The researcher will administered questionnaires through mail. The questionnaires explained to the respondents that they were expected to answer all the questions, within a 2 weeks timespan.”
The procedure subsection should be detailed and concise at the same time.
1. What you did
2. How you did it
Tips for Writing the Research Paper Methods Section
You could rely on different tips to make your research paper methods section more effective.
These tips include:
1. Introduce the Methods Section
Introducing the methods section is important in building interest among the readers.
It helps eliminate boredom of reading through the methods section, which is particularly associated with the presence of terminologies and technical language.
Such introduction can be done through two main ways, including:
1. Explanation : This entails using the first paragraph or subsection of the methods section to explain the reasons behind choosing a particular study approach or experiment.
For instance, explanation example of methods section in scientific paper would entail providing details on the inclusion or exclusion criteria in clinical research.
2. Visual presentation : A visual presentation of the methods section like a table, flowchart, or schematic diagram can be used to introduce the section and help readers easily follow the methods section.
2. Use the Appropriate Style
The research paper methods section should be written in a manner that imitates researcher’s verbal description of the research process.
It should be written in a way that the reader can understand the research procedures used, effectively repeat them and obtain similar results.
You should therefore use simple language and provide explanations for technical language used.
As well, the language used in the methods section should be in:
1. Third person
2. Passive voice
Note that sometimes you could use active voice and future tense.
Future tense is often used in this section.
Use of active voice and future tense in the methods section of research proposal example may be as follows:
“We will administer the questionnaires through mail.”
3. Assuming the Reader’s View
Putting yourself in the shoes of the reader is an important tip to consider when writing the research paper methods section.
You need to look at the methods section from the reader’s view to help evaluate whether it is thorough and clear.
Questions to ask yourself include:
1. Does the methods section provide adequate information to facilitate the reproduction of the study?
2. Can information be removed from the methods section without negatively affecting its interpretation?
3. Have all the controls, essential references, and reagents’ sources been mentioned?
Finding answers to the above questions would help you determine whether the methods section is effectively developed or not.
4. Borrow a Leaf from Others
It is advisable to borrow ideas from past studies when writing the research paper methods section.
You should find a number of well-written research paper articles in your field to act as a guide on how to write the methods section.
The articles chosen should be in a similar field as the one being explored and preferably on related or close topics.
Some of the things to observe from these articles include:
1. The structure of the methods section,
2. Language used
3. Nature and amount of information provided
You should use insights gained from these articles as the starting point when writing the methods section.
Dos and Don’ts
There are various things to do and avoid when writing the research paper methods section.
Things to do include :
1. Adhere to the research paper instructions. This involves the way the methods section should be structured and the information to be included.
2. Structure the methods section in a way that it tells the research story. This entails presenting the methods in a logical manner to help the reader effectively follow the development of the study.
3. Use subheadings to structure the methods section. The subheadings divide the section in a way that helps the reader easily follow the section.
4. Follow the order of the results to improve organization of the methods section. This requires you to match the sequence of the methods to the sequence of the results acquired from the methods.
5. Provide relevant details in a diligent manner. This involves giving details on the smallest variations in steps in methods to ensure effective interpretation of results.
6. Specify the research study’s variables. In this, you should include all the independent variables, dependent variables, control variables, and extraneous variables.
7. Provide for statistical analysis approaches used. This involves describing the analysis software used, all the relevant statistical tests, and entailed levels of significance.
8. Mention the ethical approval given by the ethics committee. Ethical approval can be mentioned in the methods section when relevant.
Things to avoid include :
1. Repeating published methods. You should avoid incidences where methods used in other research studies are in details included in the methods section. Instead, you should cite them.
2. Providing to little or too much information. Too little information hampers effective interpretation while too much information makes the methods section indistinct.
3. Failing to provide a context for the methods section. This entails failing to justify the choices made in the methods. To this problem, you should use language that shows intention, purpose, and aims.
4. Using ambiguous terms in designating conditions and parameters. You should instead use specific identifiers that can be clearly understood.
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Organizing Academic Research Papers: 6. The Methodology
- Purpose of Guide
- Design Flaws to Avoid
- Glossary of Research Terms
- Narrowing a Topic Idea
- Broadening a Topic Idea
- Extending the Timeliness of a Topic Idea
- Academic Writing Style
- Choosing a Title
- Making an Outline
- Paragraph Development
- Executive Summary
- Background Information
- The Research Problem/Question
- Theoretical Framework
- Citation Tracking
- Content Alert Services
- Evaluating Sources
- Primary Sources
- Secondary Sources
- Tertiary Sources
- What Is Scholarly vs. Popular?
- Qualitative Methods
- Quantitative Methods
- Using Non-Textual Elements
- Limitations of the Study
- Common Grammar Mistakes
- Avoiding Plagiarism
- Footnotes or Endnotes?
- Further Readings
- Annotated Bibliography
- Dealing with Nervousness
- Using Visual Aids
- Grading Someone Else's Paper
- How to Manage Group Projects
- Multiple Book Review Essay
- Reviewing Collected Essays
- About Informed Consent
- Writing Field Notes
- Writing a Policy Memo
- Writing a Research Proposal
- Acknowledgements
The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.
Importance of a Good Methodology Section
You must explain how you obtained and analyzed your results for the following reasons:
- Readers need to know how the data was obtained because the method you choose affects the results and, by extension, how you likely interpreted those results.
- Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
- In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
- The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
- The research method must be appropriate to the objectives of the study . For example, be sure you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
- The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring . For any problems that did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
- Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.
Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.
Structure and Writing Style
I. Groups of Research Methods
There are two main groups of research methods in the social sciences:
- The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation .
- The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.
II. Content
An effectively written methodology section should:
- Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
- Indicate how the approach fits the overall research design . Your methods should have a clear connection with your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is unsuited to achieving the stated objective of your paper.
- Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom.
- Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors?
- Provide background and rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
- Provide a rationale for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
- Address potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.
NOTE : Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic.
III. Problems to Avoid
Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose. Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].
It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.
Azevedo, L.F. et al. How to Write a Scientific Paper: Writing the Methods Section. Revista Portuguesa de Pneumologia 17 (2011): 232-238; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Writing the Experimental Report: Methods, Results, and Discussion . The Writing Lab and The OWL. Purdue University; Methods and Materials . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.
Writing Tip
Statistical Designs and Tests? Do Not Fear Them!
Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.
Another Writing Tip
Knowing the Relationship Between Theories and Methods
There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.
Introspectively engage in an ongoing dialectic between theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.
Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics. Part 1, Chapter 3. Boise State University; The Theory-Method Relationship . S-Cool Revision. United Kingdom.
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RESEARCH METHODOLOGY SAMPLE FOR SOCIAL RESEARCHES.pdf
RESEARCH METHODOLOGY SAMPLE FOR SOCIAL RESEARCHES by mwalimu martin otundo:freelance researcher +254721246744:[email protected]
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Igwe JaKabondo Otieno
Chief Editor
Paul Mbaluka
hussein majimbo
Peter Musundi
RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DEGREE IN BUSINESS MANAGEMENT (STRATEGIC MANAGEMENT OPTION) OF THE MOUNT KENYA UNIVERSITY
Mahdi Abdirahman
ABSTRACT This study examined challenges facing the SWM in Borama town, Somaliland. A challenge is a task or a situation that needs a lot of skills, energy and determination to overcome. Solid waste management is the collection, transfer and disposal of all non-liquid and non-gaseous solid materials from residential, commercial, institutional, construction and demolishing and street sweeping. Challenges were conceptualized as financial, technical, social and institutional aspects; SWM as safe and reliable collection, transfer and disposal of solid waste. The study was conceived out of the fact that while the poor state of solid waste management in Borama town was not contested, the aspects that have hindered effective SWM waste were not known. This created the need to unravel the aspects that have hindered the management of solid waste in Borama town to enable reconstruction of an efficient and effective means of SWM. This study focus on four objectives: to establish the status of financial recourses, the status of technical aspects, status of social aspects and status of institutional aspects. Guided by the Contingency Theory of management, the study was conducted through a census survey research design. Data was collected from all 63 workers of Horseed Sanitation Company using questionnaire from March – April 2014, analyzed using chi-squire test of goodness of fit method and reported in figures and tables. The study established that financial resources has a significant effect on SWM in Borama town, χ2 (2, N = 63) = 20.857, p = .000, C = .499; that technical aspects have a significant effect on SWM in Borama town, χ2 (3, N = 63) = 48.937, p = .001, C = .661; and that social aspects significantly affect SWM in Borama town χ2 (3, N = 63) = 45.635, p = .000, C = .648. The study also established that institutional aspects have a significant effect on SWM in Borama town, χ2 (3, N = 63) = 38.270, p = .000, C = .615. This study concluded that technical aspects is the major challenge facing the SWM in Borama town, since it accounts for the largest variance (66.1%) of the challenges facing the SWM in the town. The study recommends that the Ministry of Health, Borama municipality and all stakeholders concerned with sanitation allocate enough budgets for the provision of SWM services and development of better waste management through waste reduction, reuse and recycling; and that the government of Somaliland, Borama municipality and Horseed Sanitation Company re-train their staff on the knowledge in waste collection, knowledge of waste transfer and knowledge of disposal. The study also recommends that Borama Municipality and Horseed Sanitation Company provide awareness raising programs to inform the community of the dangers and the consequences of wastes, especially on illegal open dumping and improve the status and conditions of workers; and that Somaliland government and especially Borama municipality come up with clear and enforceable responsible authorities and rules that can strengthen the capacity of the municipality and other partners’ and increase NGOs and CBOs participations. Lastly the researcher recommends the study be done in a larger scale to cover more areas beyond Borama town with more variables and dimensions and with more experimental methodologies.
Martin Otundo Richard
Concepts developed by Martin Otundo (senior researcher in kenya, [email protected], +254721246744, academia.edu-martin otundo) ABSTRACT According to United Nations Development Programme (UNDP), (2010) report, people need water and sanitation to sustain their health and maintain their dignity. The report further states that water beyond the household sustains ecological systems and provides input into the production systems that maintain livelihoods. This means that water permeates all aspects of human development and lack of its access at household level or for production results to peoples’ choices and freedoms curtailed by ill health, poverty and vulnerability. Due to the fundamental role played by water in human life, a number of water projects have been implemented across the globe, targeting various livelihoods in a number of communities. This has seen the same happen in Kenya, more specifically in Magarini sub-county where this research targeted. The aim of this study therefore was to assess the impact of communal projects implementation on the welfare of local communities, while focusing on the Msumarini shallow wells in Magarini Sub county Kenya. The study was guided by four objectives that sought to; examine how employment opportunities created by communal projects implementation impacts on the welfare of the local communities of Magarini sub county, investigate how health related issues resulting from communal projects implementation impacts on the welfare of the local communities of Magarini sub county, examine how social conflicts created by communal projects implementation impacts on the welfare of the local communities of Magarini sub county and establish how environmental pollution caused by the implementation of communal projects impacts on the welfare of the local communities of Magarini sub county, Kenya. A descriptive research design was adopted for the study since the study was a social research in nature. In the study systematic random sampling was used in accordance to the characteristics and the elements that the population possessed from the various 8locations. The target population was about 1680but a population sample of 95was used as calculated by the Yamane (1967) formula. A pilot study was conducted to check the instruments reliability and validity and a correlation figure of 0.5 was considered. Data from the field was collected using a structured questionnaire which was administered personally, via e-mails, enumerators and picked them after they had been filled. The data then was sorted out, coded and analyzed using the SPSS version. Chi-square was used to test the hypothesis. From the hypothesis tested and the figures in the conversion of the data in chapter four, a number of issues emerged. For example the Chi-Square values of 21.57, 36.15, 50.97 and 24.77 were found for impacts on employment creation, health issues, social conflicts and environmental pollution respectively; meaning that in all the cases the alternative hypotheses were adopted. In relation to the findings in chapter four, the researcher recommended a number of issues like more funding for the water projects so as to take care of the unemployment in the area and further areas for research have been suggested.
charles laku
BIZUAYEHU BIRHANU
During the last decade, the Human Resource Management (HRM) field has shifted from a micro focus on individual HRM practices to a debate on how HRM as a more holistic management approach may contribute to the competitive advantage of the organization. The shift from examining single HRM practices to systems of practices entails focusing on the interrelationship between the various elements of the HRM system. This has created a lot of interest on the specific effect of human resources management strategies on performance. However, review of available literature indicates a serious lack of empirical studies designed to investigate whether a good alignment between human resources management and firm strategy has a positive or negative effect on performance of commercial banks in Kenya. This has led to a high desire by bank managers to know the contribution of human resource strategies to performance. This study, therefore, sought to investigate the effect of employee resourcing strategies on the performance of commercial banks in Kenya. The research adopted a mixed method approach which consisted of both qualitative and quantitative approaches. This allowed the two approaches to complement each other for better results. Specifically the study adopted the survey method. The study targeted a population of 46 banks and 2,738 employees who included: the CEOs, HR managers, Operations managers and other employees. The Nairobi head office of each bank was purposively sampled since strategies and policies are made at the headquarters and then cascaded to the branches. To ensure representation of the various subgroups in the population a sample of 349 employees was selected using stratified random sampling. Data was collected using self-administered questionnaires and interviews. Statistical data analysis involved first editing and coding the questionnaire and interview responses;
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Research Methods | Definitions, Types, Examples
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
- Qualitative vs. quantitative : Will your data take the form of words or numbers?
- Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
- Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?
Second, decide how you will analyze the data .
- For quantitative data, you can use statistical analysis methods to test relationships between variables.
- For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.
Table of contents
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Qualitative vs. quantitative data
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary vs. secondary research
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Descriptive vs. experimental data
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis methods
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
- From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
- Using non-probability sampling methods .
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis methods
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
- During an experiment .
- Using probability sampling methods .
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
- If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
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Resampling Methods Of Estimation Research Paper
Sample Resampling Methods Of Estimation Research Paper. Browse other research paper examples and check the list of research paper topics for more inspiration. If you need a religion research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our research paper writing service for professional assistance. We offer high-quality assignments for reasonable rates.
Most scientists occasionally or frequently face problems of data analysis: what data should I collect? Having collected my data, what does it say? Having seen what it says, how far can I trust the conclusions? Statistics is the mathematical science that deals with such questions. Some statistical methods have become so familiar in the scientific literature, especially linear regression, hypothesis testing, standard errors, and confidence intervals, that they seem to date back to biblical times. In fact most of the ‘classical’ methods were developed between 1920 and 1950, by scientists like R. A. Fisher, Jerzy Neyman and Harold Hotelling who were senior colleagues to statisticians still active today.
The 1980s produced a rising curve of new statistical theory and methods, based on the power of electronic computation. Today’s data analyst can afford to expend more computation on a single problem than the world’s yearly total of statistical computation in the 1920s. How can such computational wealth be spent wisely, in a way that genuinely adds to the classical methodology without merely elaborating it? Answering this question has become a dominant theme of modern statistical theory.
1. The Bootstrap
The following fundamental question arises in almost every statistical data analysis: on the basis of a data set z we calculate a statistic s(z) for the purpose of estimating some quantity of interest. For example, z could be the nine cholesterol reduction scores shown in Table 1, and s(z) their mean value z = 28.58, intended as an estimate of the true mean value of the cholesterol reduction scores. (The ‘true mean value’ is the mean we would obtain if we observed a much larger set of scores.) How accurate is s(z)?
This question has a simple answer if s(z) is the mean z of numbers z 1 , z 2 , …, z n . Then the standard error of z, its root mean square error, is estimated by a formula made famous in elementary statistics courses.
For the nine numbers in Table 1, Eqn. (1) gives 10.13. The estimate of the true cholesterol reduction mean usually would be expressed as 28.58 ± 10.13, or perhaps 28.58 10.13 s, where s is some constant like 1.645 or 1.960 relating to areas under a bell-shaped curve. With s = 1.645, interval (2) has approximately 90 percent chance of containing the true mean value.
The bootstrap, (Efron 1979), was introduced primarily as a device for extending Eqn. (1) to estimators other than the mean. For example, suppose s(z) is the ‘25 percent trimmed mean’ z {.25}, defined as the average of the middle 50 percent of the data: we order the observations z 1 , z 2 , …, z n , discard the lower and upper 25 percent of them, and take the mean of the remaining 50 percent. Interpolation is required for cases where 0.25 n is not an integer. For the cholesterol data, z {.25} = 27.81.
There is no neat algebraic formula like Eqn. (1) for the standard error of a trimmed mean, or for that matter almost any estimate other than the mean. That is why the mean is so popular in statistics courses. In lieu of a formula, the bootstrap uses computational power to get a numerical estimate of the standard error. The bootstrap algorithm depends on the notion of a bootstrap sample, which is a sample of size n drawn with replacement from the original data set z=(z 1 , z 2 , …, z n ). The bootstrap sample is denoted z* = (z 1 *, z 2 *, …, z n *). Each z i * is one of the original z values, randomly selected, perhaps z* 1 = z 1 , z* 2 = z 2 , z* 3 = z 3 , z* 4 = z 4 , z* 5 = z 5 , etc. The name bootstrap refers to the use of the original data set to generate ‘new’ data sets z*.
The bootstrap estimate of standard error for z {0.25} is computed as follows: (a) a large number ‘B’ of independent bootstrap samples each of size n, is generated using a random number device; (b) the 25 percent trimmed mean is calculated for each bootstrap sample; (c) the empirical standard deviation of the B bootstrap trimmed means is the bootstrap estimate of standard error for z {0.25}. Figure 1 shows a schematic diagram of the bootstrap algorithm, applied to a general statistic s(z).
Trying different values of B gave these bootstrap estimates of standard error for the 25 percent trimmed mean applied to the cholesterol data
B: 25 50 100 200 400
bootstrap estimate
of standard error: 12.44 9.71 11.50 10.70 10.48
Ideally we would like B to go to infinity. However, it can be shown that randomness in the bootstrap standard error that comes from using a finite value of B is usually negligible for B greater than 100. ‘Negligible’ here means small relative to the randomness caused by variations in the original data set z. Even values of B as small as 25 often give satisfactory results. This can be important if the statistic s(z) is difficult to compute, since the bootstrap algorithm requires about B times as much computation as s(z).
The bootstrap algorithm can be applied to almost any statistical estimation problem. (a) The individual data points z i need not be single numbers; they can be vectors, matrices, or more general quantities like maps or graphs. (b) The statistic s(z) can be anything at all, as long as we can compute s(z*) for every bootstrap data set z*. (c) The data set ‘z’ does not have to be a simple from a single distribution. Other data structures, for example, regression models, time series, or stratified samples, can be accommodated by appropriate changes in the definition of a bootstrap sample. (d) Measures of statistical accuracy other than the standard error, for instance biases, mean absolute value errors, and confidence intervals, can be calculated at the final stage of the algorithm. These points are discussed in Efron and Tibshirani (1993). The more complicated example below illustrates some of them.
There is one statistic s(z) for which we don’t need the computer to calculate the bootstrap standard error, namely the mean z. In this case it can be proved that as B goes to infinity, the bootstrap standard error estimate goes to √ (n-1)/n times Eqn. (1). The factor √ (n – 1)/n, which equals 0.943 for n = 9, could be removed by redefining the last step of the bootstrap algorithm, but there is no general advantage in doing so. For the statistic z, using the bootstrap algorithm is about the same as using Eqn. (1).
At a deeper level it can be shown that the logic that makes Eqn. (1) a reasonable assessment of standard error for z, applies just as well to the bootstrap as an assessment of standard error for a general statistic s(z). In both cases we are assessing the standard error of the statistic of interest by the ‘true’ standard error that would apply if the unknown probability distribution yielding the data exactly equalled the empirical distribution of the data. The efficacy of this simple estimation principle has been verified by a large amount of theoretical work in the statistics literature of the 1990s (see the discussion and references in Efron and Tibshirani 1993).
Why would we consider using a trimmed mean rather than z itself? The theory of robust statistics, developed since 1960, shows that if the data z comes from a long-tailed probability distribution, then the trimmed mean can be substantially more accurate than z, that is, it can have substantially smaller standard error (see, e.g., Huber 1964). The trouble, of course, is that in practice we don’t know a priori whether or not the true probability distribution is long-tailed. The bootstrap can help answer this question.
Figure 2 shows the bootstrap estimates of standard error for 5 different trimmed means z {p}, ‘p’ being the proportion of the data trimmed off each end of the sample before the mean is taken. (So z {0} is z, the usual mean, while z {0.5} is the median.) These were computed using the bootstrap algorithm in Fig. 1, B = 400, except that at step 2 five different statistics were evaluated for each bootstrap sample z*, namely z {0}, z {0.10}, z {0.25} , z {0.40}, and z {0.50} .
According to the bootstrap standard errors in Fig. 2, the ordinary mean has the smallest standard error among the five trimmed means. This seems to indicate that there is no advantage to trimming for this particular data set.
In fact the nine cholesterol reduction scores in Table 1 were a random sample from a much bigger data set: 164 scores, corresponding to the 164 men in the Stanford arm of a large clinical trial designed to test the efficiency of the cholesterol-reducing drug called cholestyramine (Efron and Feldman 1991). With all of this extra data available, we can check the bootstrap standard errors. The solid line in Fig. 2 indicates the true standard errors for the five trimmed means, ‘true’ meaning the standard error of random samples of size nine taken from the population of 164 scores.
We see that the true standard errors confirm the bootstrap conclusion that the ordinary mean is the estimator of choice in this case. The main point here is that the bootstrap estimates use only the data in Table 1, while the true standard errors require extra data that usually isn’t available in a real data analysis problem.
2. Growth Curves
Longitudinal data are often modeled using growth curves. Our example uses educational testing data from students in North Carolina (see Williamson et al. 1991, Rogosa and Saner 1995). The data we use for the example are eight yearly observations on achievement test scores in mathematics Y for 277 females, each followed from grade 1 to grade 8. The straight-line growth curve model for each individual can be written as
where the observable scores Y contain error of measurement indicated by ε pt . Here t denotes time and p denotes person. The constant rate of change for each person is indicated by θ p ; θ p has a distribution over persons with mean µ θ and variance σ θ 2 .
To illustrate bootstrap estimation we pick out two of the questions that are investigated using the growth curve models:
(a) Can individual change be measured reliably? In behavioral and social science research the reliability index for the estimated rate of change, ρ(θ), is the parameter of interest.
(b) Is there a relation between initial standing (math achievement in grade 1) and the rate of improvement? The parameter to be estimated is correlation between θ and initial status (over the population of individuals), written as ρ θξ( ).
Maximum-likelihood estimates of ρ(θ) and ρ θξ( ) can be obtained from standard mixed model analyses. Standard errors and interval estimates for these quantities are not available from widely-used computer packages.
Figure 3 shows histograms for 4000 bootstrap estimates of ρ(θ) and ρ θξ( ). Bootstrap samples were constructed by resampling individuals (where the data for each individual is a vector of the eight observations). The 90 percent confidence intervals were obtained using the so-called ‘percentile method’: the endpoints are the 0.05 and 0.95 percentiles of the bootstrap histograms. (The percentile method is the simplest of a number of methods for confidence interval construction via the bootstrap: a generally better method is the ‘BC a ’; see Efron and Tibshirani 1993, chap. 12–14. The results in Table 2 show good precision for estimation of the reliability, but relatively less precision for the correlation parameter.
3. Related Methods
There are a number of important related methods, with strong similarities to the bootstrap. The jackknife systematically deletes one or more observations from the dataset, and applies the statistic to the remaining data. By doing this for each observation or groups of observations, one can obtain estimates of standard errors and bias that roughly approximate to the bootstrap estimates.
Cross-validation is similar to the jackknife but is used to assess the prediction error of a model. It systematically excludes one or more observations from the dataset, fits the model to the remaining data, and then predicts the data that was left out. Application of the bootstrap for estimating prediction error is not straightforward and requires some special modifications. With these, the bootstrap and cross-validation have similar performance for estimating prediction error. Details and references are given in Efron and Tibshirani (1993) and Efron & Tibshirani (1997).
There is a connection of the bootstrap to Bayesian inference. Roughly speaking, the bootstrap distribution approximates a nonparametric posterior distribution, derived using a noninformative prior. Rubin (1981) gives details.
A difficult theoretical question is: when does the bootstrap work? There is much research on this question (see, e.g., Hall 1992). In ‘regular’ problems where asymptotic normality can be established, first and higher order correctness of the bootstrap (and modifications) have been proven. However, the bootstrap can fail, for example, when it is used to estimate the variance of the maximum or minimum of a sample. More generally, it is difficult to prove theoretically that the bootstrap works in complicated problems: but these are the very settings in which the bootstrap is needed most. Eventually, theory will start to catch up with practice. In the meantime, the bootstrap, like any statistical tool, should be applied with care and in the company of other statistical approaches.
Some popular statistics packages have bootstrap functions, including S-PLUS and Resampling Stats. In most packages that facilitate some customization by the user, it is easy to write one’s own bootstrap function.
4. Conclusions
The bootstrap is a powerful tool for data analysis, with many applications in the social sciences. It is being used more and more, as fast computational tools and convenient software become widely available. Good general references on the bootstrap include Efron and Tibshirani (1993), Davison and Hinkley (1997), Hall (1992), Shao and Tu (1995), and Westfall and Young (1993).
Bibliography:
- Davison A C, Hinkley D V 1997 Bootstrap Methods and their Application. Cambridge University Press, Cambridge, UK
- Efron B 1979 Bootstrap methods: Another look at the jack-knife. Annals of Statistics 7: 1–26
- Efron B, Feldman D 1991 Compliance as an explanatory variable in clinical trials. Journal of the American Statistical Association 86: 9–26
- Efron B, Tibshirani R 1993 An Introduction to the Bootstrap. Chapman and Hall, London
- Efron B, Tibshirani R 1997 Improvements on cross-validation: The 632 bootstrap method. Journal of the American Statistical Association 92: 548–60
- Hall P 1992 The Bootstrap and Edgeworth Expansion. Springer Verlag, Berlin
- Huber P 1964 Robust estimation of a location parameter. Annals of Mathematics and Statistics 53: 73–101
- Rogosa D R, Saner H M 1995 Longitudinal data analysis examples with random coefficient models. Journal of Educational and Behavioral Statistics 20: 149–70
- Rubin D 1981 The Bayesian bootstrap. Annals of Statistics: 130–134
- Shao J, Tu D 1995 The Jack-knife and Bootstrap. SpringerVerlag,
- Westfall P H, Young S S 1993 Resampling-based Multiple Testing: Examples and Methods for palue Adjustment. Wiley
- Williamson G, Appelbaum M, Epanchin A 1991 Longitudinal analyses of academic achievement. Journal of Educational Measurement 1: 61–76
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Example Of Research Questions And Methodology. Research Paper
Type of paper: Research Paper
Topic: Lottery , Researcher , Information , Questionnaire , Target , Community , Population , Students
Published: 09/20/2022
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Is winning the lottery a good thing or a bad thing?
Research questions. This study seeks to answer the following questions: Is winning the lottery a good idea? What becomes of lottery winners? Methods used to answer research questions The research questions elaborated above will majorly be addressed via well-structured questionnaires. The questionnaires will aim to examine the popular belief that lottery winners are normally happy people. A comparative outlook will also be introduced where necessary. This will enable the researcher to compare two control groups: lottery winners vs. participants who have lost in a lottery. These comparisons will essentially be drawn with reference to secondary data sources.
Participants.
All through this research, the researcher will target two groups of avid lottery players within the research’s target population. The first group will comprise of avid lottery players who happen to have ever won in a lottery. The second group will comprise of avid lottery players who have lost in a lottery. Note that all participants in this research will have a single common trait - that they are all enthusiastic lottery players (Hedenus 27). Their only distinction will be that while some are winners, others are not. The researcher will recruit lottery players aged between 18 and 50 years. These participants will be recruited from various social places such as parks, colleges, malls, recreational facilities, buses, trains and multiple urban centers. The researcher will aim to recruit at a total 300 participants. This will enable the researcher gather more comprehensive data from a wider array of participants.
Data Collection Procedures.
The questionnaire The researcher will incorporate appropriate use of designed questionnaires in seeking information from the target population. This will be the primary research instrument. The questionnaire will be constructed in a way that will enable it answer the research questions above. The questions will be simple to allow respondents readily grasp what is required of them. Questions will be structured to capture key respondents data which include their background information, information about their work, income and their views about winning or losing in a lottery. The questionnaire will be distributed via drop-and-collect method in various sites. These sites will include parks, colleges, malls, recreational facilities, buses, trains and multiple urban centers where foot traffic is high. Questionnaires will be administered in the early evening when most participants have completed their daily obligations hence can be located at the designated hangouts.
Data Analysis Procedures.
All data collected from the 300 respondents who would have responded to the questionnaire will be analyzed by the researcher. First, the researcher will separate all the questionnaires into two major categories: Category A to comprise of questionnaire respondents who have ever won a lottery and Category B which will comprise of questionnaire respondents who have only partaken in a lottery. Afterwards, the researcher will separately analyze the various responses gathered from respondents in these two broad categories. The researcher will then compare these responses against the research questions above to enable him establish a firm pattern in the data. This will enable the researcher to concretely study the statistical relationship between these two categories. From this analysis, the researcher will hence be able to determine whether winning in a lottery is good or bad.
Works Cited
Hedenus, Anna. "Finding prosperity as a lottery winner: Presentations of self after acquisition of sudden wealth." Sociology 45.1 (2011): 22-37.
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