Why is it important to choose the appropriate kind of research type in different topics of study

All researchers, at some stage in their project, need to decide what specific method and instrument to employ so that they can collect the data required to answer their research question or hypothesis. The choice, and the key decisions leading to it, are usually written up as part of a research methodology or research design chapter.

Índice Show

Choosing qualitative approaches, methodologies, and methods
Which cluster of quantitative techniques should I use?
Which specific techniques should I use?
Fundamental issues in statistics
Specialist techniques

In practice, students often choose a method and/or instrument they are already familiar with, or which they feel will be ‘easy to use’. In our experience, students favour the use of surveys, specifically questionnaires, and prefer to use online survey websites such as Survey Monkey to conduct the research. When asked the reason they chose this specific method (survey) and instrument (online, written questionnaire) they tend to be unable to provide an answer. If, for example, a student replied they had chosen this method because they wanted to collect quantitative data from 15 to 25 year-olds about the usability of the Amazon website, then method and instrument might be appropriate. However, if they wanted to gather data about the opinions of pensioners towards equity-release products it might be inappropriate. A possible rationale for the unsuitability of the latter approach is that the nature of the data is sensitive, so older people are less like to use or trust online survey instruments

Here are some criteria you should consider when choosing an appropriate method:

The method must be suited to the type of problem you are studying: Quantitative approaches such as surveys with written questionnaires are generally utilised to respond to hypotheses, test theories, determine facts, demonstrate causal relationships between variables and predict outcomes. Qualitative approaches such as interviews with open questions are normally used to comprehend and explore people’s behaviour, actions, views and perceptions.

The veracity of your method should provide ample academic rigour to address your research questions (or hypotheses): Veracity is demonstrated when you explain the way in which you apply the work of prior studies to the design your method. For instance, your choice can be justified if it reproduces the same method relied upon in earlier or similar inquires, or, alternatively, adapting a previously availed of method to fit your inquiry. For example, you may have found numerous qualitative, exploratory studies in your literature review, indicating that this is the ‘normal’ approach you should also take with your research on the topic. Alternatively, you may wish to say that the subject has already been explored qualitatively, and you now want to use a different method (e.g. a quantitative technique to measure the size, scope, time, etc.).

The ability of the chosen method to provide a persuasive response to your research questions (or hypotheses): Persuasiveness occurs when your method is composed of correctly constructed and relevant elements. For example, when you wish to collect quantitative data, you should use forced-choice questions for surveys; but when you wish to collect qualitative questionnaire it may be more appropriate to use open-ended questions in interviews.

The likelihood that the type of data will help you to answer your research question(s), aims and objectives: For example, if my research question is “how many people …” I will probably need to use a quantitative method to arrive at a number; if it is “what do people feel about …” I will probably need to use a qualitative method to obtain their views.

The description of your method which should be sufficient for others to adopt it as a blueprint, and, subsequently, replicate the study: Replication can be carried out directly (doing the research the same way again in the same setting), indirectly (doing the research the same way again in a different setting), or in a revised format (repeating a study deemed to have been flawed in some way, such as with the sample, method, analysis, or interpretation).

All research studies are designed with the primary purpose of addressing research questions, or testing research hypotheses, which produces credible results. Effective design commences with recognising that your study, or fieldwork, is shaped by your worldview, which, in turn, influences the methodologies, strategies and methods you employ for data collection.

Thesis Upgrade’s booklet, Designing Your Study And Fieldwork, is available as a downloadable PDF from our shop. The booklet illustrates the effect your worldview has on how you investigate a particular phenomenon and the approach you subsequently adopt. It emphasises the importance of deciding on an appropriate research methodology, selecting a suitable research strategy and choosing a relevant data collection method.

The most challenging activity in many research projects revolves around deciding which methodologies and methods to utilise. This set of resources will help you

decide which theoretical perspectives, methodologies, and methods you should use
decide which techniques you could use to analyse data
identify other methods that could improve your research

Once you choose a specific methodology or method, you might need to seek more advanced information about these choices. For example, you could read suitable books or complete online training courses. Sometimes, the most effective courses may cost several hundred dollars--such as the courses available from the Australian Consortium for Social and Political Research. You may decide to utilise your funding allocation to attend one or more of these courses.

Choosing qualitative approaches, methodologies, and methods

During many research projects, candidates will collect and analyze qualitative data--often words instead of numbers. If you need to collect or analyze qualitative data, you must then decide which approaches, methodologies, and methods to utilize.

read this document to chose suitable approaches, methodologies, and methods
also skim the potential alternatives in the following table

This table presents more information about specific examples of qualitative--as well as mixed method--methodologies and methods

Topic	When this topic is important	Document to read
Theoretical paradigms
Pragmatism	Researchers often adopt this approach if they want to collect both qualitative data and quantitative data
Decolonising theory	Researchers often, and perhaps should adopt this perspective if they want to conduct research with Indigenous communities
Critical race theory	Researchers often adopt this perspective when they want to redress discrimination towards specific races or ethnicities
Standpoint theory	Researchers often apply this theory to show that research conducted by dominant collectives, such as affluent men, tend to overlook or distort the experiences of women or marginalized communities. This theory is often applied to justify feminist or Indigenist research
Methodologies: Classical
Grounded theory	You want to develop a theory about why people engage in certain social behaviours in a particular context--and want to test and refine this theory iteratively. To learn about a specific variant in detail, read
Narrative approaches	You want to analyse people's stories about their lives or construct stories about people's lives
Interpretative phenomenological analysis and other similar approaches	You want to help interesting people share their lived experience--their perspective or perception of their world
Participatory action research	You want to collaborate with a community of individuals to solve a problem that is of interest to this community
Case studies, as recommended by Robert Yin	You want to explore one or more intriguing cases--such as a fascinating person, event, organization, practice, or intervention--in context, using a variety of methods and techniques
Mixed methods research	You want to collect both qualitative data, such as interviews, and quantitative data, such as numerical information
Methodologies: Ethnographies
Policy ethnography	You want to explore and characterise the experience of individuals, such as community health managers, who need to implement some government policy. You might, for example, want to uncover the obstacles and challenges that impeded the implementation of some policy--and should be addressed in the future.
Duo-ethnography	You and another researcher want to analyse and report your conversations with each other about a shared issue in your life, such as counselling violent people or the development of sexual orientation.
Methodologies around language
Critical discourse analysis	You want to explore how the words, phrases, and images that authors or speakers utilise may sustain or amplify disparities in power and, therefore, promote injustices
Conversation analysis	You want to understand how people use conversations and language to achieve social goals, such as to convince, persuade, or change other individuals
Program evaluations
Utilisation-focussed evaluations	You want to evaluate some program, initiative, or intervention--but you are willing to work collaboratively with stakeholders to increase the likelihood your evaluation will be heeded
Outcome harvesting	You want to first identify favourable outcomes of various programs--and then clarify which programs, initiatives, or practices explain these outcomes. This approach is suitable when many programs or practices may be contributing to various outcomes
Realist evaluation	You want to determine which people benefit from some complex, social intervention--comprising many features and activities--and the circumstances in which this intervention is beneficial
Appreciative inquiry	You want to uncover actions a team, organisation, or community could initiate to improve their conditions--but derive the ideas from positive experiences in the past
Cost effectiveness analysis	You want to establish whether the benefits of some program, initiative, or intervention outweigh the costs. Useful if the benefits cannot be readily monetised
Social return on investment	You want to determine whether the social, environmental, and economic benefits of some program or intervention outweigh the costs
Unorthodox methodologies
Actor network theory	You want to explore how humans, documents, objects, artefacts, and other materials all affect one another to construct, modify, sustain, and dissolve some association, event, or phenomenon
Horizon scanning	You want to develop plans or practices that accommodate possible problems or opportunities that might unfold in the future
Process evaluation	You want to evaluate how an intervention was implemented--rather than merely whether some intervention was effective. For example, you might want to assess why certain protocols were not observed, the effects of these deviations, and so forth.
Systematic intervention construction	You want to integrate all the practical implications that have been recommended about a topic--such as how to reduce weight or alleviate depression--to construct a unified program or intervention
Data collection: Variations on traditional approaches
Dyadic interviews	You want to interview two participants at a time, to exploit the benefits, but avoid the complications, of focus groups
Episodic interviews	You want to encourage participants to recall specific events, moments, or experiences that are relevant to your research topic--as a means to explore this topic in more depth
Observational research	You want to observe some event or circumstance as systematically and ethically as possible
Qualitative social network analysis	You want to explore how and why information, resources, friendships, and other attributes spread across people, organisations, or communities—but want to apply a qualitative approach to explore these relationships in detail
Data collection by participants
Systematic self-observation	You want to teach participants to collect data about themselves in specific circumstances, such as when they tell lies
Participatory visual methods	You would like participants to use cameras to record their lives--or use photos, videos, drawings, and other artefacts to elicit memories during interviews
Web-based diary studies	You would like participants to maintain a diary, primarily to record, and then share, their experiences while they unfold
Data collection from the web
Research with Twitter	You would like to use information from Twitter to answer your research questions
Web scraping	You would like to extract content from many websites as efficiently as possible--content that you can then subject to a range of analyses later
Data collection from discussions or stories
Story completion methods	You want to ask people to write stories--as a means to uncover the motivations, perceptions, explanations, and attitudes of individuals towards sensitive topics
Yarning	You want to learn about Indigenous experiences, perspectives, and ideas from conversations with Indigenous people; this document also outlines some relevant paradigms, such as slow science
The nominal group technique	You want to arrange members from some community to identify and rank possible solutions to some problem they share
Data analysis and presentation
Thematic analysis	You want to extract several key themes, concepts, or insights to understand some phenomenon better; one of the most common techniques to analyse qualitative data. This document also introduces template analysis--a variant of thematic analysis in which researchers often develop a set of possible themes before they commence the analysis
Framework analysis	You want to uncover themes and patterns from your data--but you want to utilise or integrate previous taxonomies, framework, or knowledge as well. An alternative to template analysis, but often used in the health sector
Tensional analysis	You want to uncover, explore, and describe the tensions or conflicts that pervade a particular setting or location, such as the tensions that research candidates or teachers experience in modern life
Situational analyses	You want to develop a theory or account of some phenomenon--similar to grounded theory--but want to construct maps or diagrams to appreciate some of the complications, contradictions, and instabilities
Ethnodrama	You want to convert the data you derive from interviews--data about some community or setting--into a theatrical performance, like a play
Data analysis with software
Content analysis and concept maps with Leximancer	You want to uncover common themes from various texts, such as emails or books, using an algorithm
Text analysis using R	You would like to numerically examine and analyse the texts you downloaded from the web or other sources
Comparing different texts	You want to ascertain the differences between two sets of texts, such as social media before COVID-19 and social media after COVID-19
Classifying texts	You want to use machine learning to classify texts

Which cluster of quantitative techniques should I use?

During many research projects, candidates will collect and analyze quantitative data--data that involves numbers or counting. If you need to collect or to analyze quantitive data, you first need to decide how to design your research and which cluster or category of techniques you will utilize to analyse the data. The following information could help you answer these questions.

Materials to read	Content of these materials
	Defines the main research designs
	Offers advice on which cluster of techniques you should use to analyse your data
SPSS video on Youtube	Teaches basic statistics and SPSS. If your statistical expertise is limited, this video will greatly help you understand the other material on statistics and data analysis--even if you do not plan to use SPSS in the future. You can also use this workbook to help you learn the basics:
	Introduces R--perhaps the most powerful free tool you can use to analyse and to manage your data

Which specific techniques should I use?

Once you have decided which cluster of techniques is suitable, you then need to choose a more specific technique. This table may help you achieve this goal.

Topic	When this topic is important	Document to read
Basic analyses
Overview of between-subject analyses	You want to compare groups on one or more measures
Overview of within-subject or mixed-model analyses	You want to examine whether some measure changes over two or more times or to compare matched groups
Overview of correlation or regression analyses	You want to explore the association between numerical variables
Non-parametric statistics	Often useful if the outcome measures are ranks, ordinal, categorical, or violate assumptions, such as normality
Linear or multiple regression analysis	You want to explore the association between one numerical outcome and multiple variables. If you want to impress the examiner or reviewer and determine the importance of each variable, read this document too
Comparing groups or classifying cases
Logistic regression	You want to explore whether two groups or conditions differ on a set of measures or characteristics; you can extend this technique to more than two groups as well. If you want to impress the examiner or reviewer and determine the importance of each measure or characteristics, read this document too
ROC curves	A technique that assesses the accuracy of measures or tests that are designed to predict one of two outcomes, such as whether or not someone should be diagnosed with some disease
Latent class analysis	You want to uncover subsets or clusters of participants--participants who are similar on various categorical variables
Advances in regression
Moderated regression analysis	You want to explore how some other variable affects or moderates the association between one numerical outcome and other variables. For a file to help you construct, the graphs, click
Ridge, lasso, or elastic net regression	You want to improve your linear regression analysis--and, in particular, increase the accuracy of predictions. This technique will impress many examiners or reviewers
Fractional polynomial regression	You want to explore the association between some numerical outcome and various predictors--but at least one of the associations deviates from a straight line and instead is curved or complex
Poisson, negative binomial, and zero-inflation models	You want to explore the association between one outcome and multiple variables--but the outcome measure is a count, such as the number of times each participant initiates some act, such as visits a doctor. In these instances, linear regression is often not applicable
Generalised linear equations	You want to explore the association between one outcome and multiple variables--but the outcome measures are counts, ranks, ordinal, categorical, or violate assumptions, such as normality. Thus, linear regression is not applicable
Multi-level analysis	Useful if your participants, animals, specimens, or other units are derived from several broad clusters that you are not interested in comparing—or should be used in lieu of within-subject or mixed-model analyses when some of the data are missing
Generalized estimating equations	Useful if your participants, animals, specimens, or other units are derived from several broad clusters that you are not interested in comparing--or your design is longitudinal--but the outcome measures are not necessarily numerical or linearly related to your predictors
Generalized additive models	Useful if the association between some predictor, such as time, and some outcome, such as weather, did not follow a regular pattern but shifts quite erratically
Structural equation modelling
Exploratory factor analysis	Identifies sets of items or questions that are correlated with each other—commonly used to evaluate or develop scales, measures, or characteristics
Mediation models	You want to examine the relationship between a sequence of variables--often to explore why two variables are related
Path analysis	You want to examine more than one cluster of relationships simultaneously. In contrast, linear regression explores only one pathway that connects the independent variables to an dependent variable
Structural equation modelling	Similar to linear regression or path analysis, but applicable when the key variables are composites of multiple questions, items, or indicators. This technique is versatile
Growth curve modelling	Explores how variables change over time--as well as the conditions or circumstances that affect these changes over time
Advances in statistics
Basic time series analysis and Google Trends	Useful if you want to examine how some characteristic, such as the popularity of some topic, changes over time. This document also introduces Google Trends--a webpage that you can use to explore the popularity of various topics over time. This document refers to the following file
Vector autoregression	Useful if you want to examine how two variables affect each other over time--such as unemployment and consumer confidence
Survival analyses, such as Cox regression	Useful if you want to explore which predictors or circumstances affect when some event is likely to unfold--such as death, completion of a task, or withdrawal from some setting
Social network analysis	Useful if you want to explore how friendships, information, resources, associations, and other attributes spread across networks of people, teams, organisations, or communities.
Item response theory	A technique that assesses the suitability of assessments that are designed to measure ability or capability--especially if the level of difficulty varies across the questions
Bayesian statistics: An introduction	Often utilized when you want to integrate past expectations or information with the data you have collected
Machine learning
K nearest neighbour and machine learning	Introduces the notion of machine learning as well as outlines one technique, called k nearest neighbour--a technique often used to classify and predict outcomes--such as which book someone will like based on their previous purchases
Support vector machines	A variant of machine learning is often used to classify people, animals, or objects into one of several categories; useful when the number of attributes and cases is moderate
Neural networks	A variant of machine learning that is useful if you want to predict some outcome from a series of predictors
Decision trees	Another variant of machine learning is also useful if you want to predict some outcome from a series of predictors; unlike neural networks, the outcomes are easier to interpret
Random forests	A more accurate and sophisticated variant of machine learning than decision trees--also useful if you want to predict some outcome from a series of predictors; read the document on decision trees first
AdaBoost	Like random forests, useful if you want to predict some categorical outcome from a series of predictors; read the document on decision trees first. Knowledge about AdaBoost will also help you learn about other machine learning methods
Gradient boosting	Like AdaBoost, but useful if you want to predict either categorical outcomes or numerical outcomes from a series of predictors; read the document on decision trees first. Knowledge about gradient boosting will also help you learn about other machine learning methods
Naive Bayes	A variant of machine learning that is useful if you want to predict some categorical outcome from a series of predictors--and is effective if the predictors are uncorrelated with each other. Often used to categorise texts

Fundamental issues in statistics

When analyzing data and conducting statistical tests, common questions arise, such as how many participants should you recruit. To answer these questions, you should first learn about power and effect sizes.

Document to read	Content of these documents
	Demonstrates how to calculate effect sizes--a statistic that many journals now recommend
	Offers some advice on how to determine the appropriate sample size--the number of participants, animals, organizations, fields, specimens, and so forth you need to complete your studies successfully. Read this document only after learning about effect sizes in the previous document
	Reveals how to increase the likelihood of significant results

Specialist techniques

Some specialized methods and approaches can be utilised in a variety of circumstances.

Documents to consider	Content of these documents
	Introduces multiple criteria decision making. This technique can be used if you want to choose one of several options that differ on multiple criteria--such as choose a suitable teaching method to apply or house to purchase. This technique can be used to decide which methods to utilise or as a method itself.
	Introduces sensitivity analysis. This approach is useful whenever you need to apply some formula or equation to predict some outcome—and then want to demonstrate the accuracy or certainty of this outcome.
	Introduces fuzzy cognitive maps. This technique can be used if you want to predict how some decision, such as a policy in which candidates must work at home, affects various outcomes. But, this technique is especially valuable when all the variables or considerations are likely to affect each other
	Introduces Q methodologies. This technique can be used to analyse the opinions of people on some topic.
	Introduces the Delphi method--an approach in which you seek the opinions of experts online several times in a systematic fashion
	Introduces lead user research--an approach in which you seek the insights of people who have modified and improved a specific product, service, or practice, ultimately to unearth possible innovations
Accommodating complexities in your design
	Introduces propensity score matching--used to improve the accuracy of quasi-experimental designs in which participants are not randomly assigned to conditions
	Introduces step-wedge designs--in which you gradually introduce some intervention to more groups or people over time.
	Introduces an approach to utilize when comparing intervention to a treatment when the sample size is small. When you apply this approach, you compare each score in the intervention condition with the mean of the control condition.
Techniques related to COVID
	Presents a range of measures and tools that could be useful if you want to conduct social science research that is related, even if obliquely, to COVID-19