Transcription
Hi class. For today's lecture, we will be focusing on quantitative and qualitative research methods. Here, we are really getting into the bread and butter of research methods that psychology has historically and also currently does focus on in the social scientific community, particularly health and psychology.
There has been an active and ongoing debate over the relative merits of adopting qualitative or quantitative methodologies as primary approaches. In part, this debate amped up in the 1970s when focusing on how we should best study human behavior, especially when we want to be able to say things about large groups of the human populations and also about individuals as well. And through Pickering referred to this debate as "the sociology of science have acknowledged." Especially as psychology as a field and psychologists as its champions were working hard to garner recognition of psychology as a truly concretely scientific field.
Now, this effort very much favored quantitative methodologies, as they could be more easily generalized to the broader population. And since qualitative methods didn't often have the benefit of generalizability beyond a single or a couple cases. Now, qualitative methods, by contrast, focused on smaller sample sizes and favored individualistic, more narrative, and less numerically and statistically heavy researchers. Quantitative methods do in psychology. Qualitative methods, such as the case study approach, which we talked about earlier on, were the method of choice for early thinkers and practitioners such as Freud and Jung. But since the scientific revolutions of psychological thought, this changed a bit.
Now, due to the limitations of qualitative methods, this helped to add fire to the argument that early psychological study was indeed not scientific. And that, at best, the results of qualitative analyses of early case studies could only apply to those specific cases and really couldn't be generalized to anybody else. So, in analyses of psychological research studies from the 1920s to 1950s, some have observed that personal experiences were discussed in articles "as freely as statistical data."
Now, since that time, there's been an increasing acceptance of a mixture of both qualitative and quantitative methods in scientific psychology, finding the benefits of each. This is especially the case in the United Kingdom. There have also been significant efforts to make qualitative research more scientific and find ways to integrate qualitative methodologies into the larger scientific process. In parallel with the development of medicine, utilizing qualitative case study approaches as a foundation to later quantitative research, psychology as a science emphasizes, as we discussed, the scientific method. So, early scientific researchers, such as Wilhelm Wundt and William James, at the turn of the century, looked at individual experiences as a scientific foundation for further understanding.
Today, our ability to analyze huge swathes of quantitative data makes our abilities far greater than those of early researchers and makes quantitative research nearly as doable as qualitative had been in the past. In the past, huge scientific studies like the ones that we do today were limited, and quantitative research methods were far less favorable when compared to qualitative research methods, mainly because of the tremendous time and resources that were involved in conducting such large-scale studies. Qualitative research methods were much more doable. They were less costly and less time-consuming, albeit them generalizable.
Today, we have the great benefit of ease of use for quantitative methods due to advanced computing methods and data analytics software like SPSS. However, as we will find and moving through this week's lecture materials, quantitative methods are not necessarily better than qualitative, and vice-versa. You can't say the qualitative is necessarily better than quantitative. In fact, there are circumstances where one may be favorable to the other based on the unique advantages and disadvantages of the other and the type of research questions that you might be asking. In certain circumstances, one might be more appropriate than the other, even if it's more or less generalizable. So, we're going to be talking about the benefits versus the negatives of each and which contexts they are most applicable in or best to use in.
So, when we try to tease apart what the difference between quantitative and qualitative research is, we have to have some working definitions. And quantitative research can be defined as being concerned with quantifying or measuring and counting as a particular approach to empirical knowledge, believing that by measuring accurately enough, we can make claims about the object at study. So, due to the stringency and objectivity of this form of research, quantitative research is often conducted in controlled settings, such as labs, to make sure that to make sure that the data is as objective and unaffected by external conditions as possible, unaffected by those confounding variables we discussed in earlier lectures. This also helps with the replicability of the study. By conducting a study more than once and receiving the same or similar responses, one can be pretty sure that the results are accurate and indeed generalizable to the larger population. Quantitative research tends to be predictive in nature and is used to test research hypotheses rather than descriptions of processes. Quantitative research tends to use a larger number of participants, using experimental methods or very structured psychometric questionnaires that follow what are referred to as Likert-type scales, which we'll discuss a little bit later on in this lecture.
By contrast, qualitative research is concerned with the quality or qualities of an experience or phenomena. Qualitative research rejects the notion of there being a simple relationship between our perception of the world and the world itself, instead arguing that each individual places different meaning on different events or experiences and that these are constantly changing. Qualitative research generally gathers texts based on data through exercises with a small number of participants, usually semi-structured or unstructured interviews.
So, when going into a little bit more detail about what exactly quantitative research is, quantitative research is essentially about collecting numerical data to explain a particular phenomena. Particular questions seem immediately suited to being answered using quality or quantitative methods. For example, how many males get a first-class degree at university at a university compared to females? What percentage of teachers and school leaders belong to ethnic minority groups? Has student achievement in English improved in our school district over time? These are all questions we can look at quantitatively, as the data we need to collect are already available to us in numerical form.
Does this not severely limit the usefulness of quantitative research, though? There are many phenomena we might want to look at, but which don't seem to produce any quantitative data. In fact, relatively few phenomena in education or even in psychology actually occur in the form of naturally quantitative data. Luckily, we're far less limited than it might appear. Many data do not appear naturally in quantitative form can be collected in a quantitative way. We do this by designing research instruments and specifically at converting phenomena that don't naturally exist in quantitative form into quantitative data, which we can then analyze statistically. Examples of this are attitudes and beliefs. We might want to collect data on students' attitudes to their school and their teachers. These attitudes obviously do not naturally exist in quantitative form, yet we can develop a questionnaire or a survey that asks students to rate a number of statements, for example, "I think school is boring," as either strongly agree, agree, disagree, or disagree strongly, and give answers a number ranging from one for strongly disagree to four for agree strongly. Now, we have quantitative data on pupil attitudes or student attitudes to school. In the same way, we can collect data on a wide number of phenomena and make them quantitative through data collection instruments, such as questionnaires, surveys, or tests. The number of phenomena we can study in this way is almost unlimited, making quantitative research quite flexible.
This is not to say that all phenomena are best studied by quantitative methods, as we'll see. While quantitative methods have some notable advantages, they also have disadvantages, which means that some phenomena are likely better studied by using different or qualitative methods. The last part of the definition of quantity research refers to the use of mathematically based methods, in particular statistics, to analyze the data. This is what people usually think about when they think about qualitative or quantitative research and is often seen as the most important part of quantitative studies. This is a bit of a misconception as well. Using the right data analysis tools obviously matter a great deal. Using the right research design and data collection instruments is actually more crucial. The use of statistics to analyze the data is, however, the element that puts a lot of people off in doing quantitative research, as the mathematics underlying the methods seems complicated and frightening to many. As we'll discuss, most researchers do not really have to be particularly expert in mathematics, at least in terms of understanding the complex mathematical formulas, as computer software allows us to do these analyses quickly and relatively easily.
So, when we talk about these various instruments that are used in data collection and numerically quantifying a more quantitative experience, something that doesn't naturally present itself in quantitative form, this is an example of what is known as a Likert-type scale, which are used to numerically quantify a research participant's response. Each response is assigned a number, which can then be entered into the statistical analysis software, such as SPSS or Stata, which are prominent forms of analyses software used within the social sciences, particularly within psychology and sociology, to utilize mathematical principles to generate understandable results based on a smaller sample of people, which is taken from the larger population.
And here we see an example from my own research. It might be a little bit blurry in this screen recording, but this survey, the segment here, is asking individuals how they feel just before listening to heavy metal music. And so, there are various emotions that we can see that represented. So, here the statement says, "I usually feel happy just before listening to heavy metal or emo music." Number one would be strongly disagree. On the other side of the spectrum would be number four, strongly agree. And the participant would circle the data point, the number that corresponds to their level of agreement. In this way, we can utilize, we can take a subjective experience and we can turn it into a numerical representation.
So, we talked about this sample versus population issue, and when we think about this, what are we really talking about? Well, essentially, we're saying that since we can't run a statistical analysis, a quantitative or, for that matter, qualitative study on an entire population, this would be a feat that would be nearly impossible. We must collect data from the population that is large enough to use the inferential statistical methods based on rules of probability to make a generalization to what is occurring in the population. So, in the case of heavy metal musical preference and dysphoric emotional states, as discussed in the last slide, we're essentially stating that because we cannot effectively sample all heavy metal listeners in the world and give them our survey, instead, we want to collect a representative sample from the larger population and generalize the results derived from that sample to that target population.
So, if we take a pragmatic approach to research methods, the main question that we need to answer is, what kind of questions are best answered by using quantitative as opposed to qualitative methods? Well, there are four main types of research questions that quantity research is particularly suited to finding an answer to. The first type of research question is that which is demanding a quantitative answer or a numerical answer. Examples are, how many students choose to study education? How many students choose to study psychology or sociology? Or how many math teachers do we need, and how many happen to have we got in our school district? That we need to use quantitative research to answer this kind of question is obvious. Qualitative or non-numerical methods will obviously not provide us with the numerical answer that we want.
The second question is that numerical change can likewise accurately be studied, but only by using quantitative methods. And so, another question of another question of this nature would be, are the numbers of students in our university or college rising or falling? Is achievement going up or down? For this, we'll need to do a quantitative study to find out.
Third, as well as wanting to find out about the state of something, we often want to explain a phenomena. What factors predict the recruitment of psychology teachers? What factors are related to changes in student achievement over time? As we'll discuss a little bit later, this kind of question can also be successfully studied by quantitative methods, and many statistical techniques have been developed that allow us to predict scores on one factor or variable from scores on one or more other factors or variables.
The final activity for which quality quantitative research is especially suited is the testing of hypotheses. We may want to explain something, for example, whether there's a relationship between students' achievement and their self-esteem and social background. We could look at the theory and come up with the hypothesis that lower social class background leads to lower self-esteem based on lack of resources, which would in turn be related to lower achievement. And using quantitative research, we can try to test this theory. Problems 1 and 2 are called descriptive; we're merely trying to describe a situation. But problems 3 and 4 are inferential; we're trying to explain something rather than just describe it.
So, when we think about the advantages of quantitative research, the first thing we'll acknowledge is that it is the dominant approach in psychological research. It's concise, its accurate, and can be strictly controlled to ensure that the results are replicable and that causation can at least be inferred, if not established. Quantitative data also has predictive power in that research can be generalized to different settings. It can also be a lot faster and a lot easier to analyze quantitative data rather than qualitative data.
When we look to the disadvantages of quantitative research, the first major issue is that quantitative data does not directly recognize the individuality of human experience and can be guilty of grouping people into set categories because it's easier to analyze that way. It can also, as such, be accused of oversimplifying human nature. This form of research does not recognize the subjective nature of all social research. If we set out to support a hypothesis, as quantitative research often does, we aren't being entirely objective, now are we?
Well, while quantitative, while a quantitative research is effective in many domains, there are other types of questions that are well-suited to quantitative methods. The first situation where quantitative research will likely fail is when we want to explore a problem in depth. Quantitative research is good at providing information in breadth from a large number of units, but when we want to explore a problem or concept in depth, quantitative methods can be too shallow to really get under the skin of a phenomena. We'll need to go for ethnographic methods, interviews, in-depth case studies, or other qualitative techniques.
Second, we saw that quantitative research is well suited for the testing of theories and hypotheses. What quantitative methods cannot do very well is develop hypotheses and theories. The hypotheses to be tested may come from a review of the literature or theory, but can also be developed by using exploratory qualitative research.
Third, if the issues to be studied are particularly complex, an in-depth qualitative study, case study for example, is more likely to pick up on this than a quantitative study. This is partly because there is a limit to how many variables can be looked at in any one quantitative study, and partly because in quantitative research, the researcher defines the variables to be studied him or herself, while in qualitative research, unexpected variables may emerge.
Finally, while quantitative methods are best for looking at cause-and-effect, causality as it's known, qualitative methods are more suited to looking at the meaning of particular events or circumstances.
So, when we look at this across the board, many might say, why not just use qualitative methods? They seem to be more in-depth, they seem to be more detailed, and they seem to grasp the individual experience on a much deeper level. Well, while all of those factors are true, qualitative research is not without its disadvantages. But let's get a little bit more of a detailed description of qualitative research before we go into its disadvantages and advantages.
Qualitative research methods have much to offer when we need to explore people's feelings or ask participants to reflect on their experiences. As was noted previously, some of the earliest psychological thinkers of the late 19th century and early 20th century may be regarded as proto-qualitative researchers. Examples include the founding father of psychoanalysis, Sigmund Freud, who worked in Vienna in the late 19th to mid-20th century and recorded and published numerous case studies and then engaged in analysis, postulation, and theorizing on the basis of his observation observations and pioneering and pioneering theory generation that developed from it. Then there was Swiss developmental psychologist Jean Piaget, who meticulously observed and recorded his children's development engagement with their social world. They were succeeded by many other authors from the 1940s onward who adopted qualitative methods and may be regarded as contributors to the development of qualitative methodologies through their emphasis on the importance of the idiographic and the use of case studies. This locates the roots of qualitative thinking in the long-standing debate between the empiricists and the rationalistic schools of thought, and also in social constructionism. Proponents of qualitative research argue that such methodology sees people as individuals attempting to gather their subjective experience of an event. This can provide a unique insider view of the research question through the qualitative approach, which is less structured than a quantitative approach. Unexpected results and insights can also occur.
When we consider the disadvantages of qualitative research, however, we're forced to note that due to the individual and subjective nature of qualitative data, it's often inappropriate or even impossible to make predictions for the wider population, and as a result, its utility is very much limited and in some cases outweighs its benefits. It can also be lengthy to analyze, and due to the open-ended approach used in qualitative research, it can be very difficult, if not impossible, to test hypotheses. However, today, a growing number of psychologists are reexamining and reexploring qualitative methods for psychological research, challenging the more traditional scientific experimental approaches characterized by quantitative methodologies. There is a move towards the consideration of what these other methods can offer to psychology, as they have very much been embraced in sociology as well. What we're now seeing is a renewed interest in qualitative method methods, which has led many researchers to become interested in how qualitative methods in psychology can stand alongside and complement quantitative methods. This is important, since both qualitative and quantitative methods have value to the researcher, and each can complement the other, albeit with a different focus.
Rigorous research methodologies from necessary foundation in evidence-based research is also important. Until recently, such a statement has been read as referring solely to quantitative methodologies, such as in double-blind or randomized controlled trials of medication encountered often in healthcare research. Qualitative methods were designed for specific purposes and were originally not intended to take researchers to the heart of patients' lived experiences. The experimental and quantitative research methods, such as the randomized control trial, focus on matters involved in the development of clinical drug trials and assessing treatment outcomes, survival rates, improvements in healthcare, and clinical governance, and in auditing as well. Qualitative paradigms, on the other hand, offer the researcher an opportunity to develop an idiographic understanding of participants' experiences and what it means to them within their social reality to be in a particular situation. Qualitative research has a role in facilitating our understanding of some of the complexity of biopsychosocial phenomenon and thus offers exciting possibilities for psychology in the future. Qualitative research is developing, therefore, new ways of thinking and revisions the more established methods, constantly being introduced and debated by researchers across the world. These methods include content and thematic analysis, grounded theory in psychology, discursive psychology and discourse analysis, and narrative psychology, as well as the detailed and in-depth case studies of an of individuals that we discussed in earlier lectures.
So, picking these various types of qualitative research apart in a little bit more detail, what is content analysis? Well, content analysis or thematic analysis is particularly useful for conceptual or thematic analysis or relational analysis. It can quantify the occurrences of concepts selected for examination. And content analysis with Ematic analysis has become a rather catch-all term, but this approach is useful when the researcher wishes to summarize and categorize themes encountered in data collection. These can include summaries of people's comments from questionnaires, documents such as diaries, historical journals, video and film footage, or other material, but list here is not exhaustive. The approach is also useful in guiding the development of an interview schedule. However, this method provides summaries of frequency of the content. The method may therefore be considered to be too limited where an in-depth approach is required. Interview data needs methods of analysis capable of providing the researcher with greater insight into participants' views, the psychological or phenomenological background into participants' stories, and their narrated experiences and feelings.
Other qualitative methods are explored for utility of purpose, and our next next slide, one such method, originally developed from sociological research, and it's known as grounded theory. Grounded theory is frequently considered to offer researchers a suitable qualitative method for in-depth exploratory investigations. It's a rigorous approach which provides the researcher with a set of systematic strategies. While this method shares some features with phenomenology, grounded theory assumes that the analysis will generate one overarching and encompassing theory from that analysis. Grounded theory in its original sociological version was designed to investigate social processes from the bottom-up, or the emergence of theory from the data. Grounded Theory methods developed from the collaboration of sociologists Glaser and Strauss during the 1960s and 1970s. It's a set of strategies that has been of immense use in sociological research as an aid to developing wider social theory, hence its name. As Willig, to his 2008 article observes, grounded theory can be an attractive method for psychologists who have trained in quantitative methods, since the building blocks identified using the grounded Theory approach aim to generate generate cat from the data collected, thus moving from data to theory. Its originator, Glaser and Strauss, considered the separation of theory from research as being a rather arbitrary division. They set out devising an approach whereby the data collection stage may be blurred or merge with the development of theory in an attempt to break down the more rigid boundaries between the usual data collection and data analysis stages. Grounded Theory approaches data by blurring these different stages and levels of abstraction. A grounded Theory analysis may proceed by checking and refining the data analysis by collecting more data until what's known as data saturation can be achieved. However, for many psychological investigations, it may be obvious at an early stage that due to the complexity of people's lived experiences, participants' narratives about their lives, feelings, and/or emotions may not always be best served by adopting a grounded theory as a method. Grounded theory was originally developed for researching from a sociological perspective, and while there is some commonality between sociology and social psychology, the use of grounded theory to analyze data might not always provide a sufficiently robust and flexible way of capturing psychological nuances and complexities contained in the participants' narratives about lived experiences. Grounded theory ISM methodology was therefore adopted and adapted by some qualitative psychologists. Willig concludes that grounded Theory can be "reserved for the study of social psychological processes" as a descriptive method. A further challenge when considering using grounded theory is the challenge provided by the different types of grounded theory that had been developed within the field, such as the debate between the two main schools of grounded theory that go a bit beyond the breadth of this course.
The next type of qualitative analysis of note is discourse analysis, and as the name suggests, discourse analysis is primarily concerned with the nuances of conversation. The term discourse can cover anything related to our use of language, whether a single utterance or a moment of speech, such as a speech fragment, through to a conversation between two people or the delivery of a political speech. It may refer to how language may be systematically ordered, as in language rules or different conventions, such as medical jargon or legal terminology. The turn to language in research, the researching society, and in the discursive psychology field has been inspired by theories emerging from other disciplines and consideration of speech used as both communication and performance. As well as Willick observes, descript their discourse analysis is more than a methodology, since social scientists have become interested in how we use language in communication and how we socially construct our environment and lived experience by the use of language. It's become more of a critique of how we describe the world and the nuances of the discourse and language we use. Discursive psychology highlights how knowledge is socially constructed and reported, for example, in "existing institutional practices that may be considered unjust." Where some psychologists may wish to explore conversation by exploring the finer nuances of conversation, such as length and pause, the terms of speech people use, or other variations of course, then discourse analysis can be a useful method. So, really understanding language in its utility in our social world is where this type of qualitative analysis would be effective.
So, what then do we do if we want to look at both breadth and depth? Because when we talk about an understanding of the broad and generalizable, we speak two quantitative methods, but when we discuss the depth of individual experience, we often look to qualitative methods. Well, when we want to look at causality and meaning, of breadth and depth, in those cases, it's often best to use what are so-called mixed methods designs, in which we use both quantitative, for example, a questionnaire, and qualitative, for example, a number of case studies, in our methodologies. Mixed methods research is a flexible approach where research design is determined by what we want to find out, rather than any predetermined epistemological position. In mixed methods research, qualitative or quantitative components can predominate, or both can have equal status. However, there are some issues from a statistical perspective with mixed methods designs. While it would seem to be enticing to use mixed methods designs, there are issues of more opportunities for error to be introduced into the research paradigm. As such, the issues of reliability and validity are often pronounced in mixed methods designs. However, to get a more complete picture, many researchers of both, of within the social sciences, often choose to utilize such an approach.
Alright, that's it for our lecture for this week. Please watch the remaining videos online and comment on the discussion posts. Take care.