Understanding Social Research Methods and Perspectives in Sociology

Nick Osbaldiston

The key goals of this chapter are to:

  • understand the principles of research methodology in sociology
  • explain the differences between positivism, interpretivism and constructivism
  • understand the basics of quantitative and qualitative research
  • explain the differences between quantitative and qualitative research
  • examine the different approaches to methodology that exist in sociology
  • consider the critiques of social science research via Indigenous worldviews.


As a discipline, like any other social science, sociology undertakes research to explore and understand the phenomenon it studies. Traditionally, sociology uses either quantitative or qualitative research methods or a mixture of both in research projects. Central to understanding this are some fundamental differences in how sociologists, and philosophers, have understood the world around them, how we can best understand that world, and what methods we can use to get the best data. Unlike the natural sciences, sociologists cannot take their research subjects into the laboratory and conduct experiments on them (thankfully!). Rather, social research requires entering a social world full of complexity and utilises the best tools available to understand how people act, interpret, and engage within that. In recent times, however, sociology has expanded its approach to social research, engaging in diverse ways of knowing, including Indigenous perspectives in Australia and New Zealand. These matters will be discussed and considered in detail throughout this chapter.

Foundations of Methodologies

A useful place to start our discussion of the different methods employed by sociologists is to examine the different perspectives that underpin these. While there are many perspectives including that of Indigenous ways of knowing that align with research methods, we will briefly focus here on roughly three areas, positivism, interpretivism, and constructivism.  Each of these areas leads to different approaches to how we undertake research, and how we understand the notion of ‘truth’.

Positivism: Sociology as Science

During the period of enlightenment in Europe, traditional ways of knowing the world were challenged by the rise of science and other forms of logic. Worldviews based on supernatural, superstition or vague abstract thought, which in the past dominated how Europeans saw the world around them, broke down, replaced instead by a modern scientific approach to understanding life. This shift was labelled by French philosopher (and forefather of sociology), Auguste Comte (1798-1857), as ‘positivism’ though the roots go right back to Greek philosopher Plato (Todd, 1993). Positivism here should not be understood as a general optimistic outlook! Rather, it is a ‘matter of fact’ approach that studies phenomenon through observable data.

Figure: Auguste Comte (c.1851) by Johan Heindrich Hoffmeister is in Public Domain

Comte, the inventor of the term, argued that there were three phases of history that led to the development of a scientific understanding of the world (Bourdeau & Pickering, 2018). Firstly, humans, especially Europeans, understood their world through a theological or religious lens – attributing life to the divine or supernatural. Secondly, and moving into the different intellectual discussions that occurred even within theology, questions of metaphysics developed – that were somewhat disconnected from religion, but still considered the world through abstract and vague interpretations and knowledge. The last moment in human history for Comte led to the development of scientific understanding where understanding life took on a matter-of-fact approach. In other words, we no longer relied on supernatural or metaphysical knowledge. Rather hard observable facts about how the world works and operates dominated our understanding and pursuit of truth (Pickering, 2011).

At a general level, positivists like Comte (Pickering, 2011) held a strong belief in the power of evidence, and would not entertain an understanding of the natural and social worlds beyond facts. Our world and how we understood truth had to be measurable and knowable through empiricism which requires undertaking scientific reasoning through data. His approach had a major influence at the time on the burgeoning social sciences, including Emile Durkheim’s approach to sociology. For Durkheim (1895/2014), in his Rules of Sociological Method, sociology ought to be like a natural science, observing only what he deemed as the ‘social facts’ that exist distinct from individuals and hold sway over them. Social facts here for Durkheim (1895/2014) can be understood as customs, rites, rituals, norms, beliefs, and values that are collectively developed and agreed upon, which exert a power on individuals to conform. He argued initially for the use of statistics to unpack social facts, as these help to understand the general rules which impact social behaviours. This approach he took up in his famous investigation into Suicide in 1897. Durkheim’s approach to sociology had a major impact on various others, including Talcott Parsons who led the development of sociology in The United States of America (see the identity, self and culture chapter).

For the most part, positivism advocates for the use of statistics, as the most appropriate scientific method, in order to understand society and rejects any attempt to establish ‘truth’ via other methods. As you will see later, this includes demographic, statistical and survey data that can be studied mathematically, to explore general social trends which impact us all as individuals in society. We loosely describe these approaches to research quantitative methodology. In positivism, there is a fundamental belief in an independent truth that can be acquired, studied and turned into knowledge, via statistical measurement.

In recent times though, there has been a movement against pure positivism across the social sciences in what we might call ‘post-positivism’. Broadly speaking, post-positivists tend to argue that the world we study is not disconnected from our own impressions, world-views and values as researchers. Truth should still be found, but we cannot ignore the impact researchers have in naming, framing, describing and even publishing what that ‘truth’ is. The problem of a purely ‘objective’ truth is that we are always involved in the process of bringing that truth to light. Our social, cultural and even historical contexts matter, and the development of knowledge on what is real is mediated through human interaction. For this to exist, we must recognise according to post-positivists, that we have biases, that can shape the way information is presented. We also have limitations of knowledge, so that even if researchers are careful, precise and have well-developed methods, there might always be ‘unknowns’ that impact reality. Post-positivists remain committed to scientific methods of understanding truth but are cautious about their results due to these unknown factors along with potential biases and other issues. This means we can never declare in our research that there is a universal truth (Panhwar et al., 2017). Reality is messy, and as such, we need to try and get as close to the truth as we can. Post-positivists therefore will talk of correlation and not ‘causation’ in statistics, but also push for data triangulation which involves using all available resources including qualitative research, to obtain a broad explanation, as much as possible, of the thing being studied.

Figure: Research meeting by Headway is licensed by Unsplash

Interpretivism: Sociology as Understanding

Several criticisms have been levelled at positivism over the years, including from German sociologist Max Weber (1864-1920) and philosopher Wilhelm Dilthey (1833-1911). For both, the disciplinary fields of the natural sciences were starting to dominate the understanding of reality or social life (Hammersley, 2012).  Max Weber in particular constructed a new approach to sociology based on the German term verstehen which incorporates understanding of the context, intentions and perceptions of the individual when analysing social behaviour (Tucker, 1965). In other words, instead of trying to understand social behaviour as a ‘matter of fact’, Weber argued for an understanding of social life as dependent on the context, and the individual’s perceptions, rather than seeking generalised social facts as Durkheim would.

This led to the cultivation of a new trend known broadly as interpretivism in the social sciences. Broadly, interpretivism entails a wide appreciation of our social lives beyond mere natural laws and facts. Rather, we live in complex social and cultural worlds, where a range of factors including culture, history, social relations, values, and personalities, impact on how we see and view the world around us. Social scientists, to really understand how people behave, try and incorporate as much of this as possible into their analysis. This is not achieved, for people like Weber, in the sorts of positivist approaches that theorists like Durkheim advocated for (Harrington, 2004). Rather, the social sciences ought to examine how individuals make meaning out of life, by interpreting their behaviour as closely as they can. For disciplines like anthropology then, this entails spending significant time with communities of interest, documenting behaviour, examining, and asking questions of people, and building a stronger understanding of cultural norms, ideas, values, rituals and everyday behaviour. For sociologists, this approach usually results in researchers speaking to participants, and providing space for them to explain their perceptions and explanations on the phenomenon being studied.

Important for interpretivists is the ability to switch off (as best as one can) any preconceived ideas about society when entering the research field. Rather than dismissing or even criticising the behaviour of other people, researchers need to document and treat all behaviour as meaningful to those who are being studied (Hammersely, 2012). This is known broadly as being reflexive, which entails setting aside your own pre-existing morals and values and having empathy for those you are researching with. For instance, you might have strong political views about a certain issue, and when researching find people who have alternative or opposing views to your own. As a social scientist in this case, interpretivism argues that one ought to suspend judgement and focus instead on building an understanding of why people believe what they do, and why they may indeed voice their opinion differently. This is easier said than done!

Interpretivism heavily impacted the development of sociology starting with Max Weber’s interpretive sociology, leading into the symbolic interactionist traditions of the Chicago school scholars of Harold Garfinkle and Erving Goffman, through to the feminist researchers and critical theorists. Importantly, it has led to the development of qualitative research in sociology, which, unlike statistics, focuses on exploring the individual’s lives via interviews, ethnography, biography and other spoken or written data.

Figure: Women interviewing by CoWomen is licensed by Unsplash

Constructivism: Sociology as Sceptical

Somewhat like interpretivism, constructivists will reject the positivist way of understanding truth and the reliance on the scientific method. However, constructivism has its roots in a sceptical approach to knowledge which treats social life as an emerging process wherein even knowledge, ideas, values, concepts, and norms is a process of continuing relations between actors. Unlike interpretivism, constructivists question whether we can really ever have an understanding of other people and argue that we can never really truly be objective in the development of knowledge.

Social constructivism is difficult to really understand at first. It involves being sceptical about our knowledge of the social world – arguing that things are the result of human beings actively and reactively developing their understanding of everything in life (Fox, 2008). For instance, love for a social constructivist is an emotion whose meaning is developed via a range of actors over time. This includes not just everyday individuals but past romantic writers and present-day romantic movies and so on which are not fixed. In other words, in the future, new framings of love will emerge as our understanding/knowledge of love shifts with new ideas. What we perceive as the definition or understanding of a particular thing, is contextual, and will change over time.

This theory is the product of several sociologists and philosophers including Peter Berger and Thomas Luckmann who in 1967 wrote a book entitled The Social Construction of Reality. In this work, Berger and Luckman (1967) argue that our reality developed via a process they call habitualisation. This involves actions that are frequently repeated, which will eventually become a norm over time. However, this is not universal but will change in the future as societies change and actions alongside them. For now though, actions that humans undertake eventually become a type of ‘common sense’ which appears as reality, but which ultimately becomes an independent entity of sorts that can be governed by institutions such as the state. For instance, certain actions that we have considered abnormal in the past, such as homosexuality, have in time become an accepted norm. Whereas in the past, the aversion to homosexual practices was reinforced by disciplines like psychiatry and the state.

For philosophers like Michel Foucault (1926-1984), constructivist thinking is important when we start to unpack norms that we consider ‘common sense’. For him, historical processes, language and importantly, expertise, creates ideas about what is normal behaviour in society. For instance, what is considered throughout modernity as ‘madness’ (what we might deem mental unwellness today) is the direct result of both a history of dealing with abnormal people and the growing power of psychiatry that owned ‘knowledge’ of what it meant to be normal. As a consequence for Foucault (1990), knowledge is power and determines what can be declared as abnormal behaviour. This is important as these ideas distill into society who come to govern themselves according to what is scientifically known as normal or abnormal. But constructivists are sceptical of the idea of normal/abnormal as these are usually contingent on certain representations of reality that have been agreed upon and taken up by society in general.

Consequently, constructivists are interested in unpacking what reality is by examining the fluid nature of meaning throughout time, place and context, arguing that these things are context-specific (Hammersely, 2012;. Nothing is ‘real’ per se, but rather the result of different actors agreeing and disagreeing to certain definitions of thing being studied. Important, social scientists are themselves a significant actor in this process. Through research, certain behaviour is defined and labelled via concepts and thus researchers provide a reality for the thing being studied. Thus, social scientists themselves contribute to the social construction of reality. However, for the most part, constructivists will lay claim to the idea that all realities are constructed through the conglomeration of social, cultural, technical, political, scientific and other knowledge that has a direct impact on how we as individuals envisage our reality (and renegotiate with these at an individual level). Thus social scientists in this domain study how these processes come about, while also acknowledging the role they play in developing social life.

Watch this short presentation on what constructivism is in the philosophy of research for further information [3:15].

Indigenous Worldviews

Social research today has a complicated relationship with people who are marginalised relative to the dominant groups in society. This is because research has been used throughout history to bolster power, and to justify practices that we now recognise as harmful to cultures and communities. In the context of Aotearoa New Zealand and Australia, this is especially the case for Māori, Aboriginal and Torres Strait Islander peoples. These groups have been subject to considerable amounts of social research since the beginning of European colonisation, and yet still experience considerable disadvantage and inequality relative to the rest of the population (Rigney, 1999).

However, as more First Nations people earned academic qualifications and began working in formal social research settings, a few things happened. One was a response to the problem outlined above – that a lot of unhelpful, and sometimes harmful, research was done on and about First Nations people, without meaningful input from them. Scholars such as Graham (2008) suggest that Aboriginal worldviews share a common approach to land and community that differs considerably from Western worldviews. This is a fundamental difference that can’t be adequately captured by outsider researchers. Another was an articulation of long legacies of research amongst First Nations cultures directly. Long before colonisation, First Nations people studied everything from the environment and animals around them, to the stars in the night sky, to healing and medicine, to people and social interactions. First Nations researchers in academic settings drew on those traditions and worldviews to underpin their own approaches to research.

First Nations worldviews, and their impacts on research, have been well laid out by a number of scholars. These include Linda Tuhiwai Smith (Ngāti Awa and Ngāti Porou, Māori), whose book Decolonizing Methodologies (first published in 1999) both critiqued the development of the scientific method for its racist practices and exploitation of First Nations peoples and knowledges, and also laid out an argument for how research can be used to decolonise settler-colonial institutions. Lester-Irabinna Rigney (Narungga, Kaurna and Ngarrindjeri) is another influential thinker in terms of research methodologies. Rigney (1999, p.109-110) defined an approach to research that can “contribute to self-determination and liberation struggles” on First Nations’ own terms. He terms this an ‘Indigenist’ methodology, which is based on three principles which overlap with one another:

  • an emancipatory imperative of resistance, or research that aids survival, healing, and self-determination
  • political integrity or research conducted by First Nations people themselves, who are responsible to their communities
  • a prioritisation of Indigenous voices in research outputs.

First Nations researchers utilise a variety of research methods, but they ask different research questions, interact with their research field, analyse their data, and construct research outputs differently. There are often more culturally appropriate versions of particular research methods that can be applied. For example, instead of structured or semi-structured interviews, a researcher might adopt a Dadirri approach to research conversations with First Nations participants. Dadirri is about place and Country, and also about deeply listening (West, et al, 2012; Ungunmerr-Baumann, et al, 2022). A quantitative sociologist must follow the principles of good data collection outlined above, but will seek to subvert the dominant approach to statistics that homogenises First Nations people, does not adequately consider the contexts for findings, and often takes a blame-worthy approach (Walter, 2018).

While researchers who come from non-Indigenous backgrounds can never fully adopt an Indigenous worldview, they can follow key principles to adopt a decolonising approach as much as is possible. These include a commitment to self-determination, undertaking research that responds to community priorities, and considering Indigenous Data Sovereignty, which is explained briefly in the video below [3:32].

Sociological Methods

In sociology, multiple methods are utilised in the design of research and subsequently analysis of data. To understand societal patterns, behaviours, attitudes and opinions, there is perhaps an endless list of approaches we can take to get as much information as we can. However, for the most part, sociology is divided into three main camps – which are a direct result of the above perspectives and debates on what is truth and how it can be found in our discipline’s history. These are quantitative, qualitative social research methods, and a combination of the two in what is known as ‘mixed methods’ approaches.

The two main approaches (quantitative and qualitative) are underpinned by something broadly known as theory. Theory is a way of making sense of the social world that we live in, via observation, by developing ideas, concepts and even ideologies that explain what we find as researchers. Importantly, theory allows us to make comparisons between different cultures, societies and histories. In the case of the latter, comparing how different things are today to how they were say 100 years ago, is pivotal to the ongoing development of sociology.

To develop theory though, we need firstly to obtain observations (or data). There are usually two strategies in the production of theory in sociology – these are generally known as theory building and theory testing. The first is more specifically known as inductive reasoning where the researcher begins with some understanding, description or knowledge of the phenomenon being studied, and then enters the research field to obtain data. Inductive research involves gathering data first, and then as time progresses, turns to data analysis techniques to make sense of what is observed. Through this process, theory comes together as we attempt to make sense of the results of the study (Blaikie and Priest, 2019; De Vaus, 2013). Put another way, inductive reasoning means building theory from the ground up!

The other approach to this is what we might call theory testing, or deductive reasoning. In this instance, we utilise theory to predict or hypothesise what the results of our research will be. This approach recognises past research in particular, by examining the theories or concepts that emerge out of other studies, and then developing predictions based on what others have discovered. Another way of describing this process is that of constructing and then testing hypotheses. Once we gather our data, we test to see if the theory fits with the results of our research. If the results confirm the theory, we can say that the theory is correct and build on this further using future research. If the results do not align with the theory, we can conclude that the theory is either wrong, does not work in the context of the study (i.e. the population we are studying or the place where the research is conducted), or potentially that our data collection exercise was flawed.

There are other approaches to reasoning now that exist that go beyond theory building/testing recognising that these approaches are too rigid. For instance, Blaikie and Priest (2019) describe retroductive and abductive styles where theory is not simply a process of either building from the group up or testing from the top down – but an integrative process as one develops and then proceeds to gather data (see also Meyer and Lunnay, 2013). From this perspective, the reality of research is never as clearly divided as inductive/deductive strategies indicate. Rather, at times researchers participate in the development of their theories. However, in this text, we want to follow the basics first! Below you will find an overview (unfortunately by no means exhaustive!) of the different methods of research in sociology with some examples from the antipodes.

Quantitative Research Methods

Unpacking the different methodological styles of research means understanding the different types of data we can use. For quantitative research methods, data is usually statistical, aligning with the principles of positivism and post-positivism for the most part (see above). Statistics can be found everywhere in our contemporary life. In fact, we produce statistics daily ourselves every time we log onto the internet and search for something, click on a link, like a video or post on social media or even when we walk if we own a smartwatch! Statistics in this sense is also known as Big Data, which represents a challenge to sociology (see chapter on digital sociology).

Quantitative sociology tends to use statistics that come from two areas, population data and survey data. In the case of the former, we all are measured and counted in various population data sets across our nation-states including via the instrument known as Census. Censuses collect information from us all, usually once every five years, on important variables to sociology such as sex, marriage status, family types, income, language, nationality, migration movement, occupation, and chronic health conditions. Data from these censuses are provided back as a public service by the state which the public can access whenever they please. Sociologists use this data to examine sociological issues from poverty through to migration. This data is invaluable as it is one of the few sources that holds information on all of the population of the country.

🛠️ Sociological Tool Kit: Explore Australia and New Zealand via statistics

Census is undertaken every five years in both Australia and New Zealand with the data taken publicly available for all. Have a look at either the Australian Census Data provided by the Australian Bureau of Statistics, or the New Zealand/Aotearoa Data provided by Stats NZ Tatauranga Aotearoa.

See if you can find the answers to the following questions

  • What is the population of the country?
  • What is the median age of the country? Has that changed since last census?
  • What is the median personal income of those living in the country?

The second type of data that is often used in quantitative research is that of survey data. As Census is only run every five years, we cannot rely on this instrument alone. Furthermore, national surveys like Census do not necessarily answer questions that we might have in our respective research areas. For instance, Census might inform us that families are having fewer children now compared to the baby boomer generation, but this does not answer the question as to why. Subsequently, survey research is helpful in that it can (1) allow us an opportunity to design questions on issues that are relevant to the research we are undertaking, and (2) provide us with the opportunity to ascertain further information such as attitude, across the population.

Unlike census data though, researchers do not have the time, resources or the funds to deliver surveys to all of the population of a country! We also do not have the capacity to force people to do surveys if they do not want to. As such, surveys, and statistics more generally, depend on one vital scientific understanding, probability sampling or theory. Probability can be best described as the ability to “say with a specific degree of confidence, how likely the patterns in a sample are to reflect those in the wider population” (De Vaus, 2013, p.66). In other words, probability suggests that we do not have hand surveys out to everyone in a population. Rather, we can hand surveys out to some in the population and make inferences about how we all think on that basis.

Probability is reliant on what is known as the bell curve. This is something you probably have heard of before. It is based on the idea that most of us when measured on different things (such as weight, blood pressure, and IQ) will be quite similar to one another. We tend to group around what are known as measures of central tendency – which are mean (average), median (middle) and mode (most common). Take for instance the average height for an adult male in New Zealand which is 178cm. Average (or mean) is calculated easily by adding all the heights of adult men, and then dividing that by the number. Mathematically then, this would mean that most of New Zealand’s male population would be around 178cm tall. However, some men will be far taller than that, some will be far shorter!

To obtain a good understanding of the population, you need only take a sample of the population. Think about it this way. Let’s say you have a group of 100 students who are in your class and you want to know how many chocolate frogs they eat in a year. If we surveyed all 100 of them, we might find that the average (or what we call mean) is 55 (which isn’t that many let’s be honest). However, if we grabbed randomly, 30 students, and surveyed them, we might find that the mean for them is 53. So we are about 2 frogs off the real population mean. The difference is what we call sample error. We could keep sampling each student until we got to the actual average, but in probability, we do not need to do this. Simply put, we do not need to talk to everyone in the country! If we use good sampling techniques, we can obtain a good representation generally (remembering that most people are not unlike each other in the bell curve) of the population we are studying. How do we know, however, if our group is like the population if we do not know what the population mean is? This is where we use something called confidence intervals.

Watch the next two videos explaining the Bell Curve [1:04] and Confidence Intervals [5:34]:

In short, we will not be certain about whether our sample is truly representative of the population – but we use confidence intervals to suggest that we are fairly sure – usually 95% – or in other words, there is a 5 percent chance we made a mistake in our sampling process. Thus, social science statistics is never 100%.

In research, the most important thing is the variable. This is the thing that you are measuring and can be as simple as age, gender, location and so on or as complicated as trying to measure happiness, altruism, or motivation. We can measure one of these variables in a sample, which is called univariate analysis. As demonstrated in our example of chocolate frogs above, we found that the average our group ate in a year was 55. That is interesting information and might be good for a report to the local chocolate manufacturer! However, we wanted to know if there were differences in our sample of students.

This is where we introduce bivariate analysis – which basically means taking one variable (an independent variable) and seeing if it causes a difference to another variable (dependent variable). For instance, we might want to see if international students eat more or less chocolate frogs than domestic students. We examine our data again and find that domestic students eat on average 33 chocolate frogs a year whereas international students eat on average 65 a year. What we have here is a statistical difference between two cohorts – and there is some indication that the independent variable (student type) is having an impact on how many frogs one eats in a year (dependent variable). We can begin to infer that there is something going on here that demands explanation (maybe the local shop where international students live near markets to them heavily chocolate frogs!). However, we need to do some serious mathematical statistical testing to show how confident we are that it is indeed this independent variable that matters most (don’t worry we won’t teach that here!).

However, what if we were interested in differences between domestic and international students, and within that whether age had a role to play in determining chocolate frog consumption? This is something called multivariate analysis, which involves multiple independent variables, and even dependent variables. In our example, we might examine the cohorts and find that as international and domestic students get older, they consume far fewer chocolate frogs. Thus, we can begin to infer that, age plays a role as well in how many frogs people eat. Our results would suggest that younger international students eat more chocolate frogs than anyone else in the sample!

🔍 Look Closer: Survey research on religion following the Christchurch Earthquake of 2011

On February 22nd, 2011, Christchurch suffered a significant earthquake that killed 185 people and changed the landscape and townscape of the city even to this day. Sibley and Bulbulia (2012) were interested to see if people had turned more to religion during this period following what is known as the religious comfort theory. They conducted a survey to collect information from a nationwide survey instrument and analysed data from 2,305 women and 1,440 men. The research found the following:

  • Religion did become more appealing to those who had suffered during and following the earthquake – though they do raise caution in these findings as conversion to faith is complicated.
  • Those who were faithful did not experience any significant ‘buffering’ in subjective suffering or health over those who were not in a faith. The findings suggest that religion perhaps does not provide the level of comfort above those who are not religious.

This study provides a good example of survey research, and the ability to take a significant issue such as natural disasters, and conduct widespread research across society.

Qualitative Research Methods

Following the patterns of interpretivism and constructivism (above), qualitative research methods start with the premise that we cannot understand society through scientific methods. Rather, to explore social behaviour, we need to get down to individuals and collect information/data on how they perceive, experience, interpret and understand life. Furthermore, remembering what constructivism argues, we also need to understand how people ‘construct’ their worlds through their values, ideas, and actions.

Qualitative research tends to follow a pattern that separates it from quantitative research. Firstly, qualitative data gathering exercises are often inductive, in that there is little theory testing and more development of theory as one goes through the research process. This requires some flexibility in the design of the research, and an ongoing assessment of what data is being gathered, as the researcher meets with and works with participants (Hammersely, 2013). Furthermore, methods tend to be far more unstructured, unlike the statistical work of quantitative research. As Hammersely (2013, p.12) points out, “there is little pressure to engage in formal counting, ranking, or measurement” as most of the data is based on observations in the natural world and verbal/non-verbal communication from the participants in the project.

Secondly, in qualitative research, the foundation is to analyse and interpret human behaviour by getting as close to people as we can, and in natural settings.  Quantitative research tends to do the opposite by either putting people into experimental conditions (such as in a lab) or having them take questionnaires with little room for the participant to elaborate their responses. As such, a criticism of qualitative research is this need for the researcher to be close to their participants, creating potential for bias, and for the research to have less objectivity than quantitative work. In qualitative research, this is not a major issue, however. All research has the potential to be influenced by the researcher. However, to overcome this, researchers try to exercise reflexivity to understand how their own values, ideas, and even theoretical positions, might influence the data that they see. To be short, reflexivity means identifying your own worldviews and trying to reduce the influence of these on your data analysis and reporting.

Lastly, research in qualitative work is not designed to be representative in the same way that quantitative research is and does not rely on probability sampling techniques. For researchers in this space, there is no unitary truth to be found as we all have very different backgrounds, ideas, values, socialisation and so on which means the amount of variables that could impact a dependent variable is endless. It is rather, better to get as close to the behaviour or people we are studying as possible to obtain quality data that can be interpreted later. As such, qualitative researchers tend not to worry too much about how many participants they have, nor that the data is truly representative of the population. Rather, the qualitative tradition focuses instead on interpretation of behaviour, perceptions, and ideas of those we research with.

Unlike quantitative research methods, the data obtained in qualitative research are usually text-based or words (though they can be other things – see below). As such, there are multiple styles of research that exist in the qualitative tradition. Below are some of the major approaches used by sociologists (and social scientists) in their work. Although as we will show towards the end, the list is potentially endless with new innovations in qualitative research happening consistently.


Ethnography is one of the oldest forms of qualitative research emerging out of the discipline of anthropology from the 1900s onwards. In general, ethnography is a practice that aims to obtain a “detailed, in-depth description of everyday life and practice” (Hoey, 2014, p.1). It involves the researcher entering into the field (where the community that they want to research lives) and engaging in what is known as participant observation to develop an understanding of culture especially. As Hoey (2014, p.2) suggests, “to develop an understanding of what it is like to live in a setting, the researcher must become a participant in the life of the setting while also maintaining the stance of an observer”. This entails a level of reflexivity, in ensuring that every day, one is ensuring that their own worldviews and values are not impeding the process of understanding the community you are researching with.

Fieldwork for ethnographers involves a myriad of things including participating with the community in active everyday life, asking questions of people to understand further actions or perceptions of different behaviours and/or life, and taking numerous ‘field notes’ along the way. Writing is an important aspect of ethnographic work, as these notes become data later when doing analysis. Every day, ethnographers take an account of the things that they have learned, observed, or have been told and look for patterns of behaviour to give eventually what Clifford Geertz (1973) calls a “thick description” in reporting later. Your task as an ethnographer is to gather as much information as you can on what you are observing, what it means for others, how people understand others, how social interactions are organised, when people do different things and what sorts of relationships people have. This means gathering data on verbal and non-verbal communication. For instance, we might do an observation in a classroom and note how students act while a lecturer is giving a lesson – noting how they’re sitting, interacting with their bodies, and verbally engaging with the class.

Hoey (2014, p.7) argues that writing never ends for an ethnographer and that “fieldnote writing is an interactive, iterative process” meaning that you go back and forth trying to understand what you have observed, and then looking for gaps that need filling in your data gathering. However, once you are finished, analysis requires you to have an “intimate relationship with your notes” so that you have a strong understanding of what you have found, and that you can if required, “make notes on your notes” (Hoey, 2014, p.8).

Ethnography as a research method can be incredibly important to understanding not simply cultures overseas, but within our own communities as well (see box out). However, ethnography can be time-consuming and often results are not forthcoming immediately. It also requires patience as a researcher, and trust from the community you are researching with. Without the latter, people may struggle to open up to you, and provide you with information. Furthermore, if you are researching with communities where you do not speak the language, the capacity to learn and understand is somewhat hindered. Nevertheless, ethnography is the oldest form of qualitative research and consistently demonstrates its value to a wider audience.

🔍 Look Closer

Example of Ethnography: Arlie Hochschild Strangers in their Own Land

Arlie Hochschild, a heavily influential sociologist in the United States of America, conducted a long ethnography with those in Louisiana Bayou country, to understand their views and opinions in relation to American politics. Her research paved the way for a greater understanding of those labelled in negative ways by those with progressive political worldviews. Watch this video interview [16:54] with her to see how participant observation allowed her to interpret and thereby understand those in these places.


One of the most popular forms of qualitative research, especially for sociologists, is that of interviews. Unlike ethnography, interviews are quicker forms of data gathering that require the researcher to meet with the participant and ask a series of questions to elicit understanding. Importantly for sociologists, interviewing is a moment of interaction, where the researcher can meet, discuss and explore their research topics with others. Social interaction, as we know from the class and status chapter, is a very important area for sociologists!

Like ethnography, the point of interviews is to investigate the research topic within natural settings by eliciting understanding from participants. There are three different types of interviews that occur generally. The first is the structured interview which involves a set schedule of questions, not deviating from them, in a one-on-one situation (or more as the case may be – for instance if you’re questioning partners or colleagues). The advantages of the structured interviews are as follows;

  • allows for comparison of answers to the same questions with different people in the sample
  • allows for the researcher to focus their interviews on the specific issues that they want to find information on
  • provides a more structured format to analyse later and is far less prone to issues of subjective interpretation
  • is a far more formal process that can be used in professional settings – such as interviews with workplaces
  • allows for a more survey-like approach where questions are closed and easier to analyse even using statistics later
  • provides an opportunity to understand what is missing from the research at the conclusion.

Conversely, structured interviews that follow a set list of questions limit how much the interviewer can deviate and explore interesting areas/topics that the interviewee may mention. In other words, using structured interviews means you leave little room for surprises in your research. It also means that you limit how much an interviewee can explain things and follow their own thoughts into different areas (Denzin and Lincoln, 2008).

Figure: Life in Durres by Juri Gianfrancesco is licensed by Unsplash

The more common approach in sociology is to follow a semi-structured approach in the design and then implementation of interviews. Like structured interviews, the semi-structured approach requires an interview schedule with a list of questions. However, the questions are always open-ended, unlike a survey, allowing the participant to explore the topic, and provide more information. The questions also serve as prompts to elicit further discussion if needed. If the interviewee brings up something in a response to a question, the interviewer can press further asking them to elaborate further, or follow the thread of the conversation to other topics that were not expected. In short, this style of research is about providing the researcher more freedom to introduce new lines of inquiry into the research and may well prompt them to follow up on these in later interviews. The advantages of the semi-structured interview then, are as follows:

  • still allows the interviewee to follow specific questions that need to be answered for the research, that can be compared across interviewees
  • provides an opportunity for the interviewer to follow topics that emerge in the interview and stray from the interview schedule if needed
  • allows for the researcher to refine their research topic further as new ideas, thoughts and issues arise during the interviews
  • provides the opportunity for the interviewee to explore their own thoughts and connect the topic to other areas that might be important to the research
  • is less formal and can be used in multiple settings
  • provides thicker data as the interviewee can talk with more freedom with open-ended questions.

Of course, one of the issues associated with interviews in this manner is that the data produced is complicated and often conversational. This data can take significantly longer to analyse as the researcher has to sift through pages of transcripts trying to pick up on commonalities in the research. Furthermore, due to the nature of the data, semi-structured interviews are prone to questions of subjective bias. We might be more likely to impose our own worldviews onto the research data. Additionally, this sort of data might mean we find conclusions that align with theoretical inclinations in the form of confirmation bias. To overcome this, most qualitative researchers advise keeping notes on decisions made during data analysis and/or follow guidelines strictly on how to analyse data (Blaikie and Priest, 2019).

The final approach to interviews is that of the fully unstructured interview. Much like ethnography, the researcher here sets out to conduct interviews with freedom, following up with participants on a specific topic and being guided by discussion later. The interviewer here operates much like an ethnographer, attempting to understand culture, social interaction or the setting further in an exploratory fashion. Often, unstructured interviewing goes hand-in-hand with the ethnographic methods of participant observation. People go about their day-to-day lives and researchers ask them questions about what they are doing. Furthermore, researchers can also move with participants in their daily activities, asking questions on the meaning of different things, or trying to get interviewees to elaborate on repeated actions (such as rituals). In human geography in recent years, there has been a push for a type of unstructured interview that enables both the interviewer and interviewee to move through places/spaces in a walking interview (Evans and Jones, 2011). This is especially useful when the researcher wants to know how people view and experience different landscapes, settings, or spaces. It also provides an opportunity for the interviewee to be reminded of different past events as they walk through places, enabling the researcher to elicit meaning of place.

The advantage of the unstructured interview is therefore as follows:

  • allows the researcher complete freedom to obtain as much meaning as possible from the research topic with interviewees.
  • provides more chances to immerse oneself into the culture of the participant.
  • creates conditions where the conversation between the researcher and participant is more natural – potentially making the interviewee more comfortable.
  • additionally, the nature of the interview may provide a relationship of trust to develop, meaning the participant might open up further about difficult topics.
  • the style of interview can lead to stronger and more nuanced understanding, especially as they tend to be longer than other forms.
  • allows the interviewer to refine their research topic further as time progresses.

The natural style of unstructured interviews results in disadvantages not unlike semi-structured interviews. The most significant of these is that the data is often long and harder to organise. Researchers in this space will need to analyse a substantial amount of data, and in some cases will do so as the interviews proceed, rather than waiting until the end. Furthermore, these types of interviews take a long time in comparison to surveys or structured interviews. Additionally, the data that emerges is not easily compared as each unstructured interview may have different topics in comparison to others.

🔍 Look Closer

Example of Interviews: Deborah Lupton and John Tulloch and Risk Epistemologies of Australians

What do you think of risk? Do you think we try and avoid risks at all costs or even insure as much as we can against it? This is the question Deborah Lupton and John Tulloch asked in 2002 by conducting research in Australia with several Australians. Conducting interviews with them, they were able to challenge some of the dominant thinking of sociologists at the time like Ulrich Beck who argued that we have entered an age where people try hard to avoid risks at all costs. Rather, Lupton and Tulloch (2002) found:

  • different perceptions of risk exist depending on a range of factors including age, gender and sexual identity. We do not all experience risk the same – especially young people.
  • Many people take risks as a form of lifestyle. For instance, activities and sports like mountain biking, skydiving and surfing embrace risks as part of the experience. We also take risks daily with other things such as starting a new romantic relationship or investing in the stock market. All these things could end badly, but we embrace them nonetheless.

The interviews conducted by these researchers help us to understand that the risk theories of sociology at the time may need some reconsidering in different contexts.

We focused here on the role of interviews with one or two people. However, in some cases, sociologists and social scientists like to interview groups of people all at once. This style of interview is known as focus groups. You may have seen a focus group (or been part of one) when companies bring people together to elicit their opinions about a topic or even product. Focus groups within sociological research however allow us to bring a group of people together, and allow them to interact with one another on topics of importance to the research. As researchers, our task is to facilitate this discussion and provide the opportunity for all members of the group to interact, engage and even disagree with each other. Importantly, this style of research allows us to understand, especially in organisational settings, important issues such as power dynamics. For instance, we might find that one or two people within an organisation tend to dominate conversation, and/or disagree with comments made by other colleagues. Focus groups might also allow for groups to come together to evaluate their individual positions and provoke understanding amongst themselves. In addition to this, focus groups provide an opportunity to obtain significant amounts of data (in terms of people talked to) in a short period of time. However, the focus group tends to be difficult to organise at times, and can also cost money as researchers may need to arrange a venue. Furthermore, individuals within the focus groups, especially those who are introverted, may find it difficult to have a voice in large groups. Finally, researchers have little control in their moderation of focus group discussions, and as such they can lead to limited information/data that is useful for their research.

Alternative forms of qualitative research also exist using interview techniques as a guide. Photo elicitation is one such approach where researchers utilise visual imagery to guide interviews along the way (Harper, 2011). Interviewees may also provide images (such as photographs or videos) to evoke feelings, and memories or talk about certain topics. In addition to this, photo voice is another style involving imagery where participants are enabled to take photos or videos of their community, culture or setting in everyday life, and discuss the meaning of the images with the interviewer (Wang and Burris, 1997). This is especially important for those doing research to empower communities through a style of research called participant action research.

Figure: Close up of photograph being taken by Bailey Mahon is licensed by Unsplash

Technology is also useful in interviews. This includes for instance the use of mapping software where interviewees are able to make use of maps to show different places and pull together their life history for the researcher showing where they might have lived, where different important events of their lives occurred and even where they might want to go in the future (Buckle, 2020). Other forms include creating paintings (Balmer, 2021) which might be especially useful when researching with children, using sound to elicit understanding of place (Duffy, Waitt and Harada, 2016), and using diaries from participants in collaboration with interviews (Thille, Chartrand and Brown, 2022). In short, qualitative research and interviews are far more flexible than statistical analysis, and scholarship in this space is always innovating new methods to obtain deeper understanding.


Mixed Methods – a Pragmatic Approach to Research

You might be thinking by now that the division between qualitative and quantitative research based on the philosophical ideas we presented earlier in the chapter feels a bit too constraining. Maybe, like others, you find both forms of research appealing. In this case, there is good news! One of the styles of research that has garnered interest in recent times is that of mixed-methods approaches. In short, mixed methods provides an opportunity for the researcher to utilise whatever style of research is useful to answer the question and gather as much data as possible to gain a better perspective. This style of research is based on a pragmatic philosophy or worldview that Creswell (2014, p.10; Creswell and Creswell, 2018) describes in the following,

Pragmatism as a worldview arises out of actions, situations, and consequences rather than antecedent conditions[…] There is a concern with applications – what works – and solutions to problems. Instead of focusing on methods, researchers emphasize the research problem and use all approaches available to understand the problem.

In short, from this perspective, there is no ‘right way’ to conduct your research. Each method has advantages and disadvantages, and in the end, your research problem needs to be answered with the best methods on offer. Knowledge as we have seen above, is diverse and by using multiple methods in our research, we can obtain the best possible answers to our research problems. From this angle, we need to strip away the philosophical questions of ‘truth’ and focus instead on what the problem is we need to solve.

The mixed methods approach needs to be defined properly here before outlining some of the styles. Firstly, this pragmatic research method involves both gathering and analysis of quantitative and qualitative research. However, it might also involve the gathering of multiple forms of quantitative or qualitative data. Nevertheless, important to mixed methods is that whatever is done, is not separate from the other. In other words, we collect qualitative research to add to the quantitative data we have, or vice-versa (see below). As such, the methodologies of qualitative/quantitative research need to be followed appropriately and in keeping with the current research expectations.

Secondly, this style of research must be set out appropriately and methodically in a timeline. Mixed-methods approaches are not an anything-goes approach. Nor do we select different methods within the project for the sake of data gathering. Each method has a role to play in explaining or developing knowledge to answer a research problem. Lastly, this approach allows the researcher to cut across different research problems in a practical manner. For instance, statistics might assist the researcher in providing answers to organisational bodies, while qualitative data might assist in explaining that within community settings. The data produced can be aligned with the needs of different stakeholders.

There are multiple types of approaches to mixed methods that can be utilised (Creswell, 2014). Here we want to explore three. Firstly and one of the more popular, as Creswell (2014, p.219) outlines, is the parallel mixed methods design where the collection of qualitative and quantitative data occurs and is then compared with each other to ascertain differences or similarities of responses. For instance, we might conduct a survey with a large sample of people (let’s say 500) and then interview a smaller group (let’s say 20) and then compare the data we have. By doing so, we can elaborate further also on the data we get from statistics, and vice versa. This approach provides us with detailed insights at the individual level, while also giving large-scale data with a broader sample that can be used to both generalise to the population and provide nuance at local levels.

The next approach two approaches involve using one method to refine another method. Explanatory sequential mixed method design for instance involves a two-phase process where initial statistical work is done first and then followed up with qualitative research (Creswell, 2014). Important to this approach is the quantitative component. Gathering this data and then analysing it, provides the foundation for what types of questions we need to ask in qualitative research. For instance, let’s say we want to research understanding student love and attachment to sociology. We start by doing surveys with 400 students across the university. When we analyse the data we find that students who are most attached to sociology are those within the humanities and social sciences programs (hardly unsurprising!). Following this, we devise research through interviews to ascertain why students in these programs are, and also why others are not. You can hopefully see here that those with stronger skills in quantitative research would prefer this approach, as the grounding for the project remains in quantitative skills.

Conversely, exploratory sequential mixed methods are the reverse of the previous process. Firstly, we explore a research topic through qualitative research, perhaps using inductive analysis to build hypotheses. Following the analysis of this data, we then test potential variables at a broader level using quantitative measures (most likely survey research). Let’s say for instance that in the previous example, we start by exploring why students love sociology. After 20 or so interviews and analysis, we find that several interviewees express attachment to the discipline due to specific lecturers in the university. We hypothesise that students who have had these lecturers will be more attached to sociology than others. We then devise a broader survey instrument with 400 students across the university and statistically test our hypothesis. Those who are stronger at qualitative research will find this approach more suitable as it builds upon interview data, constructing a theory or hypothesis from within (Creswell, 2014).

These three styles are not the only way to do mixed-methods approaches in sociological research, but they are representative of two types of approaches. Firstly, to complement the data from both styles of research, and secondly, to take one form of method, and expand on that using another form. There are several other approaches such as embedded mixed methods where one style of research is embedded within a larger body of research, transformative mixed methods where all data is used to create change, and multiphase mixed methods where longitudinal information on both qualitative and quantitative data is collected side by side (Creswell, 2014).

There are significant benefits to this approach as outlined above. However, limitations to mixed-methods approaches are centred on the assumption that all data is useful, which can be critiqued by the different theories/philosophies we explored earlier. Furthermore, these approaches require skills in both qualitative and quantitative research and this might create difficulties if the researcher is not skilled in both areas. It is also possibly time intensive, requiring a lot of work to gather the data, and then analyse it all. Overall, though, this approach is well-developed and again, innovation within mixed-methods research is frequent.

Other Styles of Research – the Digital World

There are several forms of research that we have not covered here in this chapter. These include document analysis, socio-historical analysis, autoethnography, visual ethnography, experiments (which we do not do a lot of in sociology), case studies, social networks, and longitudinal analysis. One of the burgeoning areas of social research today is the incorporation of the internet and/or social media. Our everyday lives are now lived both in the offline and online worlds. As such, researchers such as Christine Hine (2020) and Robert Kozinets (2015) contend that the Internet needs to be considered a serious site for investigation in this contemporary age.

Figure: Three people in front of computers by Brooke Cagle is licensed by Unsplash

On the one hand, we establish several virtual communities in our online spaces which we engage with daily, including that of social media but also social or community groups. Kozinets (2015) argues that these online communities deserve attention as these groups, and online interactions, are meaningful to us. Consider a virtual gaming community of people who do not meet in real life at all, but perhaps collectively come together of an evening to play together. Their interactions and experiences represent a community of sorts, only lived in the online space. However, as Milton and Petray (2020) show in their research, sometimes these communities demonstrate some of the sociological problems that exist in our society. For instance, in their research into online crime forums, they find a clear division between those who consider themselves legitimate citizens and those they believe are not, and often this is based on age and/or race. This type of ‘us’ vs. ‘them’ mentality exists across many social media forums and perhaps exacerbates already established (although maybe unspoken in everyday life) biases towards other minority groups (see digital sociology chapter).

However, unlike Kozinets (2015), Christine Hine (2020) argues that there should not be a separation between ‘online’ and ‘offline’ communities when we conduct our research. Rather, Hine (2020) challenges us to think about life as lived concurrently in both online and offline spaces. She pushes for what is known as multi-sighted ethnography, which seeks to overcome the boundaries geographically in how we study. In short, the field sites that we journey to, and interview or participate with people in, have to embrace the complexity of life. We do not simply live, work, and play in one specific place. Furthermore, for Hine (2020), this includes the online spaces where we meet, talk, socialise, plan and so on in our everyday lives. Her work intends to get researchers thinking about how the life is embodied and experienced every day in both real and virtual worlds. Consider for instance if we were seeking to research the study patterns of students taking this subject. If we conducted an ethnography where we observed them in the library, we would only capture so much information. However, if we embrace the fluidity of modern life, we might find online social forums where students meet together to share tips and hints, as well as organise study groups face to face. Hine (2020) encourages us to realise that the internet is here, and it is embedded in our everyday lives seamlessly, and in our research, we need to incorporate it.

In Summary

This chapter introduces you to the foundations of social research methods, while also preparing you for advanced studies in both qualitative and quantitative research into the future. Main points to take away here are as follows:

  • Sociology has a long history of research methods stemming back from the classical period of Durkheim and Comte.
  • Positivism and post-positivism are based on the assumptions that the best way to attain data is through quantitative research – usually statistics and based on the scientific method.
  • Interpretivism and constructivism on the other hand argue that life is far more complicated to be understood statistically, and as such propose alternative approaches to obtain the best data which is normally qualitative research.
  • Pragmatism, however, argues that the best approach to answering a research problem is to use whatever data sources are available and not get swamped by philosophical differences in method.
  • Quantitative research entails a range of statistical measures and depends largely on the idea of the normal distribution (bell curve).
  • Qualitative research is far wider in scope and includes everything from interviews through to digital ethnography.
  • Indigenous world-views however criticise the approaches in social research as delegitimising the validity of other knowledge.


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