http://www2.warwick.ac.uk/fac/soc/sociology/staff/academicstaff/chughes/hughesc_index/teachingresearchprocess/quantitativequalitative/quantitativequalitative/

Quantitative and Qualitative Approaches

DEPARTMENT OF SOCIOLOGY

QUALITATIVE AND QUANTITATIVE APPROACHESTO SOCIAL RESEARCH

Christina Hughes

C.L.Hughes@warwick.ac.uk

There has been widespread debate in recent years within many of the social sciences regarding the relative merits

of quantitative and qualitative strategies for research. The positions taken by individual researchers vary

considerably, from those who see the two strategies as entirely separate and based on alternative views of the

world, to those who are happy to mix these strategies within their research projects. For example, Bryman (1988)

argued for a `best of both worlds' approach and suggested that qualitative and quantitative approaches should be

combined. Hughes (1997), nevertheless, warns that such technicist solutions underestimate the politics of

legitimacy that are associated with choice of methods. In particular, quantitative approaches have been seen as

more scientific and `objective'.

In exploring issues of qualitative and quantitative research, this material builds directly on the epistemological

foundations presented in the package `What is Research?' For example, in exploring the distinctions between

qualitative and quantitative forms of research we need to consider the different ontological and epistemological

questions we considered when discussing positivism, interpretivism and critical paradigms. Thus, on first

consideration, the use of questionnaires as a research technique might be seen as a quantitative strategy,

whereas interviews and observations might be thought of as qualitative techniques. Similarly, it is often assumed

that quantitative approaches draw on positivist ontologies whereas qualitative approaches are more associated

with interpretive and critical paradigms. A further assumption is that some critical approaches to research, such as

feminism, only use qualitative approaches (see Graham, 1984; Jayrantine, 1993 to prove this assumption wrong!).

And so in practice, of course, it is often more complicated than that! Thus, interviews may be structured and

analysed in a quantitative manner, as when numeric data is collected or when non-numeric answers are

categorized and coded in numeric form. Similarly, surveys may allow for open-ended responses and lead to the in-

depth study of individual cases. In addition, quantitative and qualitative approaches are strongly associated with

objectivity (quantitative) and subjectivity (qualitative). These were issues that we considered in terms of the role of

the researcher within the research process earlier in the course. Finally, the choice of approach is linked to the

research objectives.

The main aim of this package is to introduce you to, and facilitate your understanding of, the key debates

concerning qualitative and quantitative approaches. The learning outcomes are:

· To outline the qualitative and quantitative paradigms;

· To illustrate the distinctiveness of each paradigm;

· To illustrate issues of similarity between each paradigms;

· To outline the ways in which qualitative and quantitative methods can be combined;

· To apply this learning to individual research projects.

AN INTRODUCTION TO THE QUALITATIVE AND QUANTITATIVE DIVIDE

Read the quotations below. Draw up a list of the characteristics of qualitative and quantitative research.

As long ago as 1957, Cronbach drew attention to the existence of two quite separate `disciplines of scientific

psychology'. One sort of psychologist attempts to test general principles about human and animal behaviour, and

is concerned with documenting average performance; the other sort of psychologist in interested in describing and

interpreting individual differences, in particular with respect to various dimensions of intellectual ability, personality

and psychopathology. The first sort of psychologist does experiments, typically on small samples obtained for

reasons of convenience. The other sort of psychologist does larger-scale questionnaire surveys or interview

studies, attempts to procure representative samples, and tends to use standard, pre-validated measures. When

analysing results, the first sort of psychologist tends to compute t-tests and analyses of variance. The second sort

tends to use correlation, regression, and factor-analytic techniques. (Everitt and Hay, 1992: 3-4)

Quantitative research consists of those studies in which the data concerned can be analysed in terms of

numbers ... Research can also be qualitative, that is, it can describe events, persons and so forth scientifically

without the use of numerical data ... Quantitative research is based more directly on its original plans and its

results are more readily analysed and interpreted. Qualitative research is more open and responsive to its subject.

Both types of research are valid and useful. They are not mutually exclusive. It is possible for a single investigation

to use both methods. (Best and Khan, 1989: 89-90)

Qualitative research is harder, more stressful and more time-consuming than other types. If you want to get your

MEd dissertation or whatever finished quickly and easily do a straightforward questionnaire study. Qualitative

research is only suitable for people who care about it, take it seriously, and are prepared for commitment

(Delamont, 1992: viii)

Quantitative research is, as the term suggests, concerned with the collection and analysis of data in numeric form.

It tends to emphasize relatively large-scale and representative sets of data, and is often, falsely in our view,

presented or perceived as being about the gathering of `facts'. Qualitative research, on the other hand, is

concerned with collecting and analysing information in as many forms, chiefly non-numeric, as possible. It tends to

focus on exploring, in as much detail as possible, smaller numbers of instances or examples which are seen as

being interesting or illuminating, and aims to achieve `depth' rather than `breadth'. (Blaxter, Hughes and Tight,

1996: 61)

Research is a systematic investigation to find answers to a problem. Research in professional social science

areas, like research in other subjects, has generally followed the traditional objective scientific method. Since the

1960s, however, a strong move towards a more qualitative, naturalistic and subjective approach has left social

science research divided between two competing methods: the scientific empirical tradition, and the naturalistic

phenomenological mode. In the scientific method, quantitative research methods are employed in an attempt to

establish general laws or principles. Such a scientific approach is often termed nomothetic and assumes social

reality is objective and external to the individual. The naturalistic approach to research emphasises the importance

of the subjective experience of individuals, with a focus on qualitative analysis. Social reality is regarded as a

creation of individual consciousness, with meaning and the evaluation of events seen as a personal and subjective

construction. Such a focus on the individual case rather than general law-making is termed an ideographic

approach. (Burns, 2000: 3)

Quantitative research is empirical research where the data are in the form of numbers.

Qualitative research is empirical research where the data are not in the form of numbers. (Punch, 1998: 4)

QUANTITATIVE RESEARCH

KEY CHARACTERISTICS

· CONTROL: This is the most important element because it enables the scientist to identify the causes of his or

her observations. Experiments are conducted in an attempt to answer certain questions. They represent attempts

to identify why something happens, what causes some event, or under what conditions an event does occur.

Control is necessary in order to provide unambiguous answers to such questions. To answer questions in

education and social science we have to eliminate the simultaneous influence of many variables to isolate the

cause of an effect. Controlled inquiry is absolutely essential to this because without it the cause of an effect could

not be isolated.

· OPERATIONAL DEFINITION: This means that terms must be defined by the steps or operations used to

measure them. Such a procedure is necessary to eliminate any confusion in meaning and communication.

Consider the statement `Anxiety causes students to score poorly in tests'. One might ask, `What is meant by

anxiety?' Stating that anxiety refers to being tense or some other such term only adds to the confusion. However,

stating that anxiety refers to a score over a criterion level on an anxiety scale enables others to realise what you

mean by anxiety. Stating an operational definition forces one to identify the empirical referents, or terms. In this

manner, ambiguity is minimised. Again, introversion may be defined as a score on a particular personality scale,

hunger as so many hours since last fed, and social class as defined by occupation.

· REPLICATION: To be replicable, the data obtained in an experiment must be reliable; that is, the same result

must be found if the study is repeated. If observations are not repeatable, our descriptions and explanations are

thought to be unreliable.

· HYPOTHESIS TESTING: The systematic creation of a hypothesis and subjecting it to an empirical test.

(Adapted from Burns, 2000: 6-7)

QUANTITATIVE APPROACHES

STRENGTHS AND LIMITATIONS

STRENGTHS

· Precision - through quantitative and reliable measurement

· Control - through sampling and design

· Ability to produce causality statements, through the use of controlled experiments

· Statistical techniques allow for sophisticated analyses

· Replicable

LIMITATIONS

· Because of the complexity of human experience it is difficult to rule out or control all the variables;

· Because of human agency people do not all respond in the same ways as inert matter in the physical sciences;

· Its mechanistic ethos tends to exclude notions of freedom, choice and moral responsibility;

· Quantification can become an end in itself.

· It fails to take account of people's unique ability to interpret their experiences, construct their own meanings and

act on these.

· It leads to the assumption that facts are true and the same for all people all of the time.

· Quantitative research often produces banal and trivial findings of little consequence due to the restriction on and

the controlling of variables.

· It is not totally objective because the researcher is subjectively involved in the very choice of a problem as worthy

of investigation and in the interpretation of the results.

Questions to consider

· Why are only testable ideas of worth in science?

· Scientific study is empirical and objective. What is meant by this statement?

(Adapted from Burns, 2000: 9-10)

QUALITATIVE RESEARCH

KEY CHARACTERISTICS

· Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher

immerses her/himself in the setting.

· The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.

· Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in

words and other actions. Therefore, qualitative research is an interactive process in which the persons studied

teach the researcher about their lives.

· Qualitative researchers attend to the experience as a whole, not as separate variables. The aim of qualitative

research is to understand experience as unified.

· Qualitative methods are appropriate to the above statements. There is no one general method.

· For many qualitative researchers, the process entails appraisal about what was studied.

Ely et al add the following from Sherman and Webb (1988) to their definition:

Qualitative implies a direct concern with experience as it is `lived' or `felt' or `undergone' ... Qualitative research,

then, has the aim of understanding experience as nearly as possible as its participants feel it or live it.

QUALITATIVE APPROACHES

STRENGTHS AND LIMITATIONS

LIMITATIONS

· The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative

data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity.

· Contexts, situations, events, conditions and interactions cannot be replicated to any extent nor can

generalisations be made to a wider context than the one studied with any confidence.

· The time required for data collection, analysis and interpretation is lengthy.

· Researcher's presence has a profound effect on the subjects of study.

· Issues of anonymity and confidentiality present problems when selecting findings.

· The viewpoints of both researcher and participants have to be identified and elucidated because of issues of

bias.

STRENGTHS

· Because of close researcher involvement, the researcher gains an insider's view of the field. This allows the

researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more

positivistic enquiries.

· Qualitative descriptions can play the important role of suggesting possible relationships, causes, effects and

dynamic processes.

· Because statistics are not used, but rather qualitative research uses a more descriptive, narrative style, this

research might be of particular benefit to the practitioner as she or he could turn to qualitative reports in order to

examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

· Qualitative research adds flesh and blood to social analysis.

Questions to consider

· What is meant by `deep' when referring to qualitative data?

· How limiting is the problem of non-replication?

(Adapted from Burns, 2000: 13-14)

QUALITATIVE AND QUANTITATIVE APPROACHES TO

SOCIAL RESEARCH

THE SIMILARITIES

· Whilst quantitative research may be mostly used for testing theory it can also be used for exploring an area and

generating hypotheses and theory.

· Similarly qualitative research can be used for testing hypotheses and theories even though it is mostly used for

theory generation.

· Qualitative data often includes quantification (eg statements such as more than, less than, most as well as

specific numbers).

· Quantitative (ie questionnaire) approaches can collect qualitative data through open ended questions.

· The underlying philosophical positions are not necessarily so distinct as the stereotypes suggest.

QUALITATIVE AND QUANTITATIVE APPROACHESTO SOCIAL RESEARCH

THE COMBINED APPROACH

ELEVEN WAYS TO COMBINE QUALITATIVE AND QUANTITATIVE RESEARCH

1. Logic of triangulation. The findings from one type of study can be checked against the findings deriving from

the other type. For example the results of a qualitative investigation might be checked against a quantitative study.

2. Qualitative research facilitates quantitative research. Qualitative research may: help to provide background

information on context and subjects; act as a source of hypotheses; aid scale construction.

3. Quantitative research facilitates qualitative research. Usually this means quantitative research helping with

the choice of subjects for a qualitative investigation.

4. Quantitative and qualitative research are combined in order to provide a general picture. Quantitative

research may be employed to plug the gaps in a qualitative study which arise because, for example the

researcher cannot be in more than one place at any one time. Or if not all issues are amenable solely to a

quantitative or a qualitative investigation.

5. Structure and process. Quantitative research is especially efficient at getting at the structural features of social

life while qualitative studies are usually stronger on process aspects.

6. Researchers' and subjects' perspectives. Quantitative research is usually driven by the researcher's

concerns, whereas qualitative research takes the subject's perspective.

7. Problem of generality. The addition of some quantitative evidence may help generalizability.

8. Qualitative research may facilitate the interpretation of relationships between variables. Quantitative

research readily allows the researcher to establish relationships among variables, but is often weak when it comes

to exploring the reasons for those relationships. A qualitative study can be used to explain th efactors underlying

the broad relationships.

9. Relationship between macro and micro levels. Employing both quantitative and qualitative research may

provide a means of bridging the macro-micro gulf. Qualitative research can tap large-scale structural features of

social life while qualitative research tends to address small-scale behavioural aspects.

10. Stage in the research process. Use at different stages of a longitudinal study.

11. Hybrids. Use of qualitative research is a quasi-experimental quantitative study.

(Adapted from Punch, 1998: 247)

QUANTITATIVE AND QUALITATIVE APPROACHES:

WHICH TO CHOOSE?

SIX FACTORS TO TAKE INTO ACCOUNT

1. Research Questions: What exactly are you trying to find out? Focus on the `exactly' as this can lead you either

into the quantitative or qualitative direction.

2. Are we interested in making standardized and systematic comparisons or do we really want to study

this phenomenon or situation in detail?

3. The Literature: How have other researchers dealt with this topic? To what extent do you wish to align your own

research with standard approaches to the topic?

4. Practical Considerations: Issues of time, money, availability of samples and data, familiarity with the subject

under study, access to situations, gaining co-operation.

5. Knowledge payoff: Will we learn more about this topic using quantitative or qualitative approaches? Which

approach will produce more useful knowledge? Which will do more good?

6. Style: Some people prefer one to the other. This may involve paradigm and philosophical issues or different

images about what a good piece of research looks like.

The question `quantitative or qualitative?' is commonly asked, especially by beginning researchers. Often, they are

putting the `methods cart' before the `content horse'. The best advice in those cases is to step back from

questions of method, and give further consideration to the purposes and research questions, bearing in mind that

the way questions are asked influences what needs to be done to answer them. But when that has been done,

and the question still remains, the above factors help in making the decision.

Of course, a reasonable decision in any study might be to combine the two approaches.

(Adapted from Punch, 1998: 244-245)

QUALITATIVE AND QUANTITATIVE APPROACHES

Bibliography

Bernard, H (1994) Research Methods in Anthropology: Qualitative and Quantitative Approaches, London,

Sage

Best, J and Khan, J (1989) Research in Education, Englewood Cliffs (NJ), Prentice Hall

Blaxter, L, Hughes, C and Tight, M (1996) How to Research, Buckingham, Open University Press

Bryman, A (1988) Quantity and Quality in Social Research, London, Routledge

Burns, R (2000) Introduction to Research Methods, London, Sage

Cassell, C and Symon, G (Eds) (1994) Qualitative Methods in Organizational Research: A Practical Guide,

London, Sage

Creswell, J (1994) Research Design: Qualitative and Quantitative Approaches, Thousand Oaks, (Calif), Sage

Delamont, S (1992) Fieldwork in Educational Settings: Methods, Pitfalls and Perspectives, London, Falmer

Ely, M et al (1991) Doing Qualitative Research: Circles within Circles, London, Falmer

Everitt, B and Hay, D (1992) Talking about Statistics: A psychologist's Guide to Data Analysis, London,

Edward Arnold

Finch, J (1986) Research and Policy: The Uses of Qualitative Methods in Social and Educational Research,

London, Falmer

Graham, H (1984) Surveying through stories, in C Bell and H Rosen (Eds) Social Researching: Politics,

Problems, Practice, London, Routledge and Kegan Paul, pp 104-124

Halfpenny, P (1979) The analysis of qualitative data, Sociological Review, 27, pp 799-825

Hammersley, M (1989) The Dilemma of Qualitative Method, London, Routledge

Henwood, K and Pidgeon, N (1993) Qualitative research and psychology, in M Hammersley (Ed) Social

Research: Philosophy, Politics and Practice, London, Sage

Hughes, C (1997) Mystifying through coalescence: The underlying politics of methodological choices, in K

Watson, C Modgil and S Modgil (Eds) Educational Dilemmas: Debate and Diversity, Quality in Education,

London, Cassell, pp 413-420

Mason, J (1994) Linking qualitative and quantitative data analysis, in A Bryman and R Burgess (Eds) Analysing

Qualitative Data, London, Routledge, pp 89-110

Neuman, W (1994) Social Research Methods: Qualitative and Quantitative Approaches, Boston, Allyn and

Bacon

Stanley, L (Ed) (1990) Feminist Praxis, London, Routledge

Punch, K (1998) Introduction to Social Research: Quantitative and Qualitative Approaches, London, Sage

http://www2.warwick.ac.uk/fac/soc/sociology/staff/academicstaff/chughes/hughesc_index/teachingresearchprocess/quantitativemethods

Developing Literacy in Quantitative Methods

DEVELOPING LITERACY IN QUANTITATIVE RESEARCH METHODSDr Christina HughesUniversity of WarwickC.L.Hughes@warwick.ac.uk

These materials have two inter-related aims. The primary aim is to develop students' literacy in the use and

reading of research that uses quantitative data. The second is to enhance students' confidence in their

understandings of such approaches. To achieve these aims the package will introduce students to a number of

basic statistical techniques that are used in social research. In addition the materials will explore some common

concepts that underpin quantitative social research.

The specific objectives are:

· To develop understandings of the relationship between different types of quantitative data and their implications

for descriptive and inferential statistical techniques;

· To develop understandings of the statistical techniques of: measures of central tendency, measures of

dispersion;

· To explore the meanings of correlation and causality in relation to quantitative social research;

· To explore uses, and misuses, of official statistics.

Quantitative techniques are most commonly associated with survey and experimental research designs. As the

name suggests, quantitative research is concerned with the collection and analysis of data in numeric form. It

tends to emphasize relatively large-scale and representative sets of data, and is often (problematically) presented

or perceived as being about the gathering of `facts'. Because of strong associations that are made between

statistics as social facts and dominant ideas of science as objective and detached, quantitative strategies are often

viewed as more valid.

Many small-scale research studies that use questionnaires as a form of data collection will not need to go beyond

the use of descriptive statistics and the exploration of the interrelationships between pairs of variables. It will be

adequate to say that so many respondents (either the number or the proportion of the total) answered given

questions in a certain way; and that the answers given to particular questions appear to be related. Such an

analysis will make wide use of proportions and percentages, and of the various measures of central tendency

(averages) and of dispersion (ranges).

You may, however, wish or need to go beyond this level of analysis, and make use of inferential statistics or

multivariate methods of analysis. There are dozens of inferential statistics available: three commonly used

examples are Chi-square; Kolmogorov-Smirnov and Student's t-test. The functions of these statistics vary but they

are typically used to compare the measurements you have collected from your sample for a particular variable

with another sample or a population in order that a judgement may be made on how similar or dissimilar they are.

It is important to note that all of these inferential statistics make certain assumptions about both the nature of your

data and how it was collected. This means that you have to be clear whether your data is, for example, nominal,

ordinal, interval or ratio. If these assumptions do not hold these measures should not be used.

Multivariate methods of analysis may be used to explore the interrelationships among three or more variables

simultaneously. Commonly used examples include multiple regression, cluster analysis and factor analysis. While

you do not need to have an extensive mathematical knowledge to apply these techniques, as they are all available

as part of computer software packages, you should at least have an understanding of their principles and

purposes.

One key point to be aware of when carrying out quantitative analysis is the question of causality. One of the

purposes of analysis is to seek explanation and understanding. We would like to be able to say that something is

so because of something else. However, just because two variables of which you have measurements appear to

be related, this does not mean that they are. Statistical associations between two variables may be a matter of

chance, or due to the effect of some third variable. In order to demonstrate causality, you also have to find, or at

least suggest, a mechanism linking the variables together.

[Extracted from Blaxter, Hughes and Tight, 1996]

Bibliography

This bibliography includes texts that are useful for students new to quantitative techniques and those that

are useful for the more advanced. The asterisk (*) indicates those that are introductory. The key

publishers of methodology texts are Sage, Routledge and Open University Press. If you wish to extend

your reading or keep up to date with developments you should put your name on these publishers'

catalogue mailing lists. There are also a number of journals that are primarily concerned with

developments in methodology. These include: The International Journal of Social Research Methodology

and Social Research Online ( http://www.socresonline.org.uk). In addition, secondary sources produced by

the Office for National Statistics for the Government Statistical Service can be obtained from The Office

for National Statistics, 1 Drummond Gate, London, SW1V 2QQ or through the STATBASE on-line

directory.

Black, T (1999) Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research

Design, Measurement and Statistics, London, Sage

Blaxter, L, Hughes, C and Tight, M (1996) How to Research, Buckingham, Open University Press*

Bowling, A (1997) Research Methods in Health: Investigating Health and Health Services, Buckingham, Open

University Press*

Bryman, A and Cramer, D (1990) Quantitative Data Analysis for Social Scientists, London, Routledge

Calder, J (1996) Statistical Techniques, in R Sapsford and V Jupp (Eds) Data Collection and Analysis, London,

Sage, pp 225-261

Cramer, D (1994) Introducing Statistics for Social Research: Step-by-step calculations and computer

techniques using SPSS, London, Routledge

Denscombe, M (1998) The Good Research Guide: For small scale social research projects, Buckingham,

Open University Press*

De Vaus, D (1991) Surveys in Social Research, Sydney, NSW, Allen and Unwin

Hek, G, Judd, M and Moule, P (1996) Making Sense of Research: An Introduction for Nurses, London,

Cassell*

Hinton, P (1995) Statistics Explained: A guide for social science students, London, Routledge*

Leary, M (1991) Introduction to Behavioural Research Methods, Belmont, Calif, Wadsworth Publishing

Levitas, R and Guy, W (1996) Interpreting Official Statistics, London, Routledge

Persell, C and Maisel, R (1995) How Sampling Works, Newbury Park, Calif, Pine Forge

Pilcher, D (1990) Data Analysis for the Helping Professions: A Practical Guide, Newbury Park, Calif, Sage

Sapsford, R (1996) Extracting and Presenting Statistics, in R Sapsford and V Jupp (Eds) Data Collection and

Analysis, London, Sage, pp 184-224

Solomon, R and Winch, C (1994) Calculating and Computing for Social Science and Arts Students,

Buckingham, Open University Press*

Stanley, L (Ed) (1990) Feminist Praxis, London, Routledge

Townsend, P (1996) The Struggle for Independent Statistics on Poverty, in R Levitas and W Guy (Eds)

Interpreting Official Statistics, London, Routledge, pp 26-44

Traub, R (1994) Reliability for the Social Sciences: Theory and Application, Thousand Oaks, Calif, Sage

Wright, D (1997) Understanding Statistics: An introduction for the social sciences, London, Sage*

TYPES OF QUANTITATIVE DATA

Nominal data

Nominal data come from counting things and placing them in a category. They are the lowest level of quantitative

data in the sense that they allow little by way of statistical manipulation compared with the other types. Typically

there is a head count of members of a particular category, such as female/male or African Caribbean/South Asian.

These categories are based simply on names; there is no underlying order to the names.

Used for the following descriptive statistics: proportions, percentages, ratios.

Ordinal data

Like nominal data, ordinal data are based on counts of things assigned to specific categories but in this case the

categories stand in some clear, ordered, ranked relationship. The categories are `in order'. This means that the

data in each category can be compared with the data in the other categories as being higher or lower than, more

or less than, etc. those in other categories. The most obvious examples of ordinal data come from the use of

questionnaires in which respondents are asked to respond to a five-point Likert scale. It is worth stressing that

rank order is all that can be inferred. With ordinal data we do not know the cause of the order or by how much they

differ.

Used for the following descriptive statistics: proportions, percentages, ratios.

Interval data

Interval data are like ordinal data but the categories are ranked on a scale. This means that the `distance' between

the categories is a known factor and can be pulled into the analysis. The researcher can not only deal with the

data in terms of `more than' or `less than' but also say how much more or how much less. The ranking of the

categories is proportionate and this allows for direct contrast and comparison. Calendar years are one example.

This allows the researcher to use addition and subtraction (but not multiplication and division) to contrast the

difference between various periods.

Used for the following descriptive statistics: measures of central tendency (mode, median, mean)

Ratio data

Ratio data are like interval data except that the categories exist on a scale which has a `true zero' or an absolute

reference point. When the categories concern things like incomes, distances and weights they give rise to ratio

data because the scales have a zero point. Calendar years, in the previous example, do not exist on such a scale

because the year 0 does not denote the beginning of all time and history. The important thing about the scale

having a true zero is that the researcher can compare and contrast the data for each category in terms of ratios,

using multiplication and division, rather than being restricted to the use of addition and subtraction as is the case

with interval data. Ratio data are the highest level of data in terms of how amenable they are to mathematical

manipulation.

Used for the following descriptive statistics: measures of central tendency (mode, median, mean)

[adapted from Blaxter, Hughes and Tight, 1996 and Denscombe, 1998]

TYPES OF QUANTITATIVE DATA

EXAMPLES

Are the following nominal, ordinal, ratio or interval data?

· The income levels of social workers;

· The examination scores of members of this course;

· The sex of your research participants;

· The birth position of members of a family;

· Exam grades received at school;

· Number of exam passes;

· The temperatures of different geographical zones;

· The size of families in the UK;

· IQ scores;

Illustrative Issue

A Likert scale is written to convey equidistant points along an axis:

*-----------------*---------------- *---------------- *---------------- *

Very Fairly Important Not very Not at all

Important Important Important Important

Are the meanings ascribed by research respondents similarly equidistant?

Is such data interval or nominal?

TYPES OF QUANTITATIVE DATA

A CAUTIONARY COMMENT

Very important 1

Fairly important 2

Not very important 3

Not at all important 4

The problem is that the `real' distance between the ratings numbered 3 and 4 for a respondent may be much

greater than the distance they perceive between the items numbered 1 and 2. The `real' distances between each

of the ratings may also vary from person to person. In theory, therefore, such data should be treated as ordinal

data. Most researchers take a pragmatic approach, however, and continue with the practice of treating ratings and

psychological tests as interval data.

One way of dealing with data that are difficult to `type' correctly is through the use of models. Scientists use

models of weather systems to study the relationships between different factors in order to understand better what

the contributory factors are. In the same way, statisticians produce statistical models based on their current

understanding of the problem. When they do not quite work as expected, they modify some of their assumptions.

If the assumption of an interval scale does not work, then further analyses can be carried out on the assumption of

an ordinal scale. Over the years, reviews of the statistical evidence suggested that the assumption of equality of

equal intervals within rating scales is justified. But where such assumptions are made, there is always the

possibility of misinterpretation of the data. The important point is to be clear always that there are different types

of data, and that this will affect the type of analyses that can be used on them. (Calder, 1996: 229)

MEASURES OF CENTRAL TENDENCY

OR MID-POINTS AND AVERAGES

There are three types of average and these are collectively called `measures of central tendency'. These are the

mean, the median and the mode.

The mean (or arithmetic average)

This is the most common meaning of `average'. It includes the total spread and finds the mid-point. To calculate

the mean:

1. Add together the total of all the values for the category

2. Divide this total by the number of cases

· The mean cannot be used with nominal data. For example, you cannot `average' names, sexes, nationalities and

occupations.

· The mean is affected by extreme values, or outliers. Because the mean includes all values the average can be

pulled toward the value of the outlier or toward the more extreme values.

· The mean can lead to strange descriptions, such as 2.4 person households.

Example: Calculate the mean from the following:

1 4 7 11 12 17 17 47

The median or mid-point

The median is the mid-point of the range. To calculate:

1. Place the values in ascending/descending rank order

2. Find the mid-point number

3. With even numbers of values the mid-point is half-way between the two middle values

· The median can be used with ordinal data as well as interval and ratio data.

· The median is not affected by extreme values or outliers.

· The median works well with a low number of values.

· The main disadvantage is that you can do no further calculations with the median.

Example: Calculate the median from the following:

1 4 7 11 12 17 17 47

The Mode

The mode is the value that is most common. To calculate:

1. Arrange the data in ascending/descending order;

2. Identify the value that occurs more frequently than any other.

· The mode can be used with nominal, ordinal, interval and ratio data. It has the widest possible scope therefore.

· It is unaffected by outliers or extreme values.

· It does not allow any further mathematical calculations.

· There may not be any `most common' values or there may be more than one.

Example: Calculate the mode of the following:

1 1 4 4 7 11 12 17 17 17 47

MEASURES OF DISPERSION

Given some of the problems in the accuracy of conveying meaning with measures of central tendency, measures

of dispersion are an important adjunct in any description of the data. Measures of dispersion are used to indicate

how widely the data is spread and how evenly the data is spread. In other words, how far from the central point is

the data dispersed?

There are three main measures of dispersion: the range, fractiles and standard deviation.

The range

This is the simplest, and a very effective, way of describing the spread of the data. To calculate the range:

· Substract the minimum value in the distribution from the maximum value.

Although effective, the range can still be affected by the value of any outliers. In consequence it can give a

misleading impression of the spread of the data. This is why is it important to include a note of the highest and

lowest score in your written presentation of data.

Example: Calculate the range from the following:

3 4 7 11 12 17 17 47

Fractiles

To take account of the spread of values across the whole range, fractiles (eg quartiles/quarters, deciles/tenths,

percentiles/hundredths) are used. These divide the range into smaller, equidistant ranges. Fractiles are used with

median values. To calculate:

1. Subdivide the range into equal parts (eg quartiles, deciles, percentiles)

2. Find the median (mid point) value;

3. Working from the median point divide your data into the relevant fractiles.

Fractiles can eliminate the high and low values that affect measures of central tendency. For example, by focusing

on the cases that fall between the second and third quartile reasearchers know that they are dealing with the half

of the values that fall in the middle. In addition it allows the comparison of values between fractiles. For example,

the top ten percent of earners can be compared with the bottom.

Example: The following is income data of social workers. Divide the data into quartiles. Find the median that

occurs in each quartile. Find the median that occurs between the second and third quartile. How would you

present this data? What would you say about the validity of these data?

Income per annum (thousands):

15 16 17 21 22 27 27 47

Standard Deviation (SD)

The standard deviation is used with the arithmetic mean. The standard deviation uses all the values in the range

to calculate the spread of the data. It is a measure of the distance of the scores from your mean. The larger the

standard deviation the more spread out the range is. To calculate:

1. Find the mean

2. Subtract the mean from all your values

3. Square all the results (to turn your minuses into pluses)

4. Add all these `squared numbers' together

5. Divide this by the number of your values minus one

6. Find the square root of this

· The standard deviation can be used for further statistical analysis

· Because of this standard deviation is an immensely important aspect of social research

· The standard deviation can only be used with interval and ratio data. It is meaningless when used with nominal

and even ordinal data.

Exercise: Find the standard deviation of the following:

1 4 7 11 12 17 17 47

CORRELATION

Correlation

How closely are two variables connected? This question is answered in statistical terms with correlation. For

example, do the students who spend the most time studying achieve the highest marks? Do those who spend

least time studying get the lowest marks? These question are asking us to compare two variables: study time and

examination performance. We are asking to what extent is there a relationship between these two variables.

If the answer was that that those who spend most time studying do achieve the highest marks we would say that

there is a positive correlation between the two variables. In other words we would be saying that as the score

increases on one variable it also increases on the other variable. In addition, if those who study least achieve the

lowest marks, we would also say that there is a positive correlation between the two variables.

However, if we found that the more students spent studying the lower their marks, this would be described as a

negative correlation. There is, for example, a negative correlation between the variables of smoking and health.

The more a person smokes the less healthy that person is likely to be. If there is no relation between two variables

then we would say that the variables are uncorrelated. For example, if the hypothesis was that wearing jeans

improved exam scores and the results suggested that some students who wore jeans had high scores and some

who wore jeans had low scores, some students who did not wear jeans had high scores and some who did not

wear jeans had low scores the results are likely to show no correlation.

To calculate correlation one plots the scores on a scatter diagram. This requires you to plot the scores of the two

variables along the axes of a graph and mark the results. If a straight line can be drawn there is a correlation. The

direction of the lines indicates whether this is a positive (up) correlation or a negative (down) correlation.

The two most commonly used correlation statistics are Spearman's rank correlation coefficient that works for

ordinal data and Pearsons's product moment correlation coefficient that works for interval and ratio data.

When reading statistical research you are likely to find the following signs:

· +1 this equals a perfect positive correlation (as one variable goes up so does the other)

· 0 this means there is no relationship between the variables

· -1 this equals a perfect negative correlation (as one variable goes up the other goes down)

· In practice any correlation coefficient between 0.3 (weak) and 0.7 (strong) suggests a reasonable correlation.

Example: Do the following data indicate a correlation?

Student Study Time Examination Mark

1 40 58

2 43 73

3 18 56

4 10 47

5 25 58

6 33 54

7 27 45

8 17 32

9 30 68

10 47 69

(from Hinton, 1995)

CORRELATION AND CAUSATION

CORRELATION DOES NOT MEAN CAUSATION

If two things go together it is easy to assume that they are causally related in some way. Is this the case? Even if

the thickness of a caterpillar's coat correlates closely with the severity of the winter weather, can we conclude that

caterpillars cause bad weather?

Three criteria are required to achieve causality in statistical research:

· Covariation

· Directionality

· Elimination of extraneous variables

Covariation

To conclude that two variables are causally related they need to covary or correlate. If one variable causes the

other then changes in the values of one variable should be associated with changes in the values of the other.

This is, of course, the definition of correlation.

Directionality

To infer that two variables are causally related we much show that the presumed cause precedes the presumed

effect in time. However in most correlational research both variables are measured at the same time. There is

therefore no way to determine the direction of causality. Has X causes Y or Y caused X?

Elimination of Extraneous variables

The third criterion for inferring causality is that all extraneous factors that might influence the relationship between

the two variables are eliminated. Correlational research never satisfies this requirement completed. Two variables

may be correlated not because they are causally related to one another but because they are both related to a

third variables. For example, does loneliness cause depression? Maybe but a third variable - the quality of a

person's social network - may reduce both loneliness and depression.

Example: Does smoking cause cancer?

There is a wealth of research that suggests a strong correlation between smoking and cancer. Does smoking

cause cancer?

[adapted from Leary, 1991]

USING OFFICIAL DATA SETS

There are a number of important, and useful, data sets collected by government and which can be used for

secondary analyses. These include:

· Census of Employment

· Census of Population

· Labour Force Survey

· General Household Survey

· Family Expenditure Survey

The annual publication Social Trends is a useful source for those who are seeking some simple statistics. Social

Trends compiles its analyses from these data sets. In addition, the ESRC keeps data archives of both quantitative

and qualitative research that can be consulted.

Care should be taken in the use of statistics however. For example, in a discussion of poverty statistics, Townsend

notes how successive governments in the UK have chosen to avoid using the term `poverty'. As he further notes

(1996: 26):

Statistics don't fall out of the skies. Like words - of which they are of course an extension - they are constructed by

human beings influenced by culture and the predispositions and governing ideas of the organisations and groups

within which people work. Statistical methodologies are not timeless creations. They are the current expression of

society's attempts to interpret, represent and analyse information about economic and social (and other)

conditions. As the years pass they change - not just because there may be technical advances but because

professional, cultural, political and technical conventions change in terms of retreat as well as advance ... [Thus]

Every student of social science ... needs to be grounded in how information about social conditions is acquired.

Statistics form a substantial part of such information. Acquiring information is much more than looking up

handbooks of statistics. We have to become self-conscious about the process of selection.

Levitas and Guy (1996) contextualise these concerns in terms of the following:

There are developments which may make official data more easily accessible to academic experts [on-line

access]. They do not make data more easily available to the public in the interests of informed political debate.

Moreover, the (relative) ease of conducting secondary analysis carries the danger of forgetting that the concepts

used in any research derive from the questions and interests of its original intentions. The extent to which

secondary analysis can bend data sets to the service of sometimes quite different agendas is necessarily limited.

(p 3)

...The debates ... show that the insistence on the neutrality and objectivity of facts still dominates discussion of

official statistics and their production. The presentation of statistics in particular ways for political ends, and the

abolition of inconvenient measures, continue. It is understandable that professional statisticians should try to

counter this by appeals to objectivity. But it is also abundantly clear that the definitions used in official statistics still

produce measures which embody the interests of the state rather than of citizens. It is therefore only with the

utmost care that such data can be interpreted for democratic purposes. (p 6)

The edited text by Levitas and Guy (1996) outlines the kinds of data sets that are available. It also contains

discussions of the use, and misuse, of government statistics in the following areas: poverty, unemployment, social

class, health, safety at work, working women, ethnicity, disability and crime. Another useful text is that of Stanley L

(Ed) (1990) Feminist Praxis, London, Routledge. Amongst the range of issues discussed, this contains

discussions on the ways in which statistics collected on the homeless are `compromised' by the processes of

turning raw data into statistical information. A chapter by Liz Stanley (A Referral Was Made) discusses the politics

of objectivity influences the presentation of a social service's case.

USING OFFICIAL DATA SETS

EXERCISE

1. How would you interpret the following statement?

"Statistics on patterns of household disposable income are provided in Households below Average Income

reports ... The best response to low household income is to sustain economic recovery and to assist those in

greatest need" (Reported in Townsend, 1996: 27-28)

2. How would you interpret the following conversation?

Ms Corston: Is the Prime Minister aware that Social Trends 1994, a Government publication, reveals that as a

direct consequence of Tory Government policy since 1979 the average disposable income of the richest 20 per

cent of households has increased by £6,000 a year while the 20 per cent of households at the bottom of the

income scale have had their average disposable income cut by £3,000 a year? Does that reveal the hypocrisy of

the Prime Minister's professed commitment to creating a nation at ease with itself?

The Prime Minister: The hon. Lady [Ms Corston] was being selective in what she said - [Interruption]. She was

selective from the report. The net disposable income of people at all ranges of income has increased and the

proportion of total tax paid by those on top incomes has increased, not been reduced. (Reported in Townsend,

1996: 40)

http://www2.warwick.ac.uk/fac/soc/sociology/staff/academicstaff/chughes/hughesc_index/teachingresearchprocess/qualitativemethods

An Introduction to Qualitative Methods

DEPARTMENT OF SOCIOLOGY

AN INTRODUCTION TO QUALITATIVE RESEARCH

PREPARED BY

CHRISTINA HUGHES

UNIVERSITY OF WARWICK

WHAT IS QUALITATIVE RESEARCH?

AN INTRODUCTION

Qualitative research is empirical research where the data are not in the form of numbers. (Punch, 1998: 4)

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter.

This means that qualitative researchers study things in their natural settings, attempting to make sense of, or

interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use

and collection of a variety of empirical materials - case study, personal experience, introspective, life story,

interview, observational, historical, interactional, and visual texts - that describe routine and problematic moments

and meanings in individuals' lives. Accordingly, qualitative researchers deploy a wide range of interconnected

methods, hoping always to get a better fix on the subject matter at hand. (Denzin and Lincoln, 1994: 2)

Qualitative research is ... grounded in a philosophical position which is broadly `interpretivist' in the sense that it is

concerned with how the social world is interpreted, understood, experienced or produced ... based on methods of

data generation which are flexible and sensitive to the social context in which data are produced (rather than

rigidly standardized or structured, or removed from `real life' or `natural' social context, as in some forms of

experimental method) ... based on methods of analysis and explanation building which involve understandings of

complexity, detail and context. Qualitative research aims to produce rounded understandings on the basis of rich,

contextual and detailed data. There is more emphasis on `holistic' forms of analysis and explanation in this sense,

than on charting surface patterns, trends and correlations. Qualitative research usually does use some form of

quantification, but statistical forms of analysis are not seen as central. (Mason, 1996: 4)

These quotations convey something of the nature of qualitative research. They indicate that qualitative research is

concerned with the study of people in their natural settings. Qualitative researchers use a variety of tools and

techniques in order to develop deep understandings of how people perceive their social realities and in

consequence, how they act within the social world. They seek to make connections between events, perceptions

and actions so that their analyses are holistic and contextual. Beyond these broad assumptions, qualitative

researchers are very careful to stress the multiplicity and variety of qualitative approaches. For example, Mason

(1996) comments that she does not feel comfortable with going beyond the above general features. This is

because there are many different answers to key questions of qualitative methodology. Similarly, Denzin and

Lincoln (1994) highlight the multiple nature of qualitative approaches. They also illustrate how these have changed

over time. Denzin and Lincoln (1994: 1) indicate that `qualitative research operates in a complex historical field

that crosscuts five historical moments ... These five moments simultaneously operate in the present'. Denzin and

Lincoln describe these five moments as:

· The traditional (1900-195): associated with the positivist paradigm where qualitative research aims to reflect the

principles of (natural) scientific enquiry;

· The modernist or golden age (1950-1970): where we see the appearance of post-positivist arguments. This is

also part of:

· The blurred genres (1970-1986): where a variety of new interpretive, qualitative perspectives come into the

foreground: hermeneutics, structuralism, semiotics, phenomenology, cultural studies and feminism. The

humanities also became a central resource for critical and interpretive theory. The blurred genres phase gave rise

to:

· The crisis of representation (1986-1990): where researchers struggled with how to locate themselves and their

subjects in reflexive texts;

· The postmodern or present (1990-): a new sensibility that doubts all previous paradigms.

The key points I would make in respect of this are:

· As Ely et al (1991) point out the field of qualitative research is shot through with a host of labels and a host of

proponents of those labels. Different terms are used in roughly synonymous ways (naturalistic inquiry,

ethnographic methodologies, qualitative research, interpretive research). This causes confusion and for the new

(and also not so new!) student it reinforces a sense that it is they who is at fault as they have failed to get to grips

with what exactly qualitative research is. Take heart: Ely et al note that Tesch (1990) compiled a list of 46 terms

that social scientists have used to name their versions of qualitative research. No wonder we are all confused!

· As Denzin and Lincoln (1994) point out the nature of the field of qualitative research changes over time. Different

perspectives and concerns arise at different points.

· As Denzin and Lincoln (1994) also point out old concerns do not go away. The `five moments' are consecutive

but also simultaneous.

· Theories of ontology (what is the form and nature of social reality and, thus, what can be known) and

epistemology (what is the nature of the relationship between the knower and would-be knower and what can be

known) are central to understanding the forms of knowledge that are produced through qualitative approaches.

I would add the following imperatives, drawn from Mason (1996)

· Qualitative research should be conducted systematically and rigorously. This means that, like all social

researchers, qualitative researchers have to use key principles of research design such as linking the research

questions to the methodological approaches, considering issues of analysis and data collection as integrated and

being clear about the purposes of the research.

· Qualitative research should be conducted in a flexible and contextual way. This means that qualitative

researchers make decisions on the basis of their research design and in terms of the changing contexts and

situations in which the research takes place. One of the strengths of qualitative approaches is that this flexibility

can enhance the research leaded to unanticipated, but significant, issues.

· Qualitative research should be conducted through critical, self-reflexive enquiry. This means that the researcher

should be constantly asking questions about her or his role in the research process.

· Qualitative research should produce social explanations to intellectual puzzles. This means being explicit about

the logics that have produced these explanations (eg. sampling, selection of events for analysis, researcher's role

and so on).

· Qualitative research is not a unified body of philosophy and practice. For example, qualitative research should

not be viewed as completely distinctive from, or uncomplementary to, quantitative approaches. The `divide'

between quantitative and qualitative research is to some extent false. Qualitative research does quantify (look for

phrases such as more than, less than). Quantitative research can collect more qualitative data through open

ended questions. All researchers should think carefully about how the choices of method and the potential

combinations of approach that are appropriate and possible.

· Qualitative research should be conducted as ethical practice.

Whilst the field of qualitative research is complex and riven with internal debates, nonetheless it is important to

have a general sense of the key features of qualitative research. To this end I conclude with the following

produced by Ely et al (1991: 4):

QUALITATIVE RESEARCH

KEY CHARACTERISTICS

· Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher

immerses her/himself in the setting.

· The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.

· Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in

words and other actions. Therefore, qualitative research is an interactive process in which the persons studied

teach the researcher about their lives.

· Qualitative researchers attend to the experience as a whole, not as separate variables. The aim of qualitative

research is to understand experience as unified.

· Qualitative methods are appropriate to the above statements. There is no one general method.

· For many qualitative researchers, the process entails appraisal about what was studied.

Ely et al add the following from Sherman and Webb (1988) to their definition:

Qualitative implies a direct concern with experience as it is `lived' or `felt' or `undergone' ... Qualitative research,

then, has the aim of understanding experience as nearly as possible as its participants feel it or live it.

Further Work

For those of you interested in following up debates and issues within qualitative research in more detail the classic

texts are Denzin and Lincoln, 1994 and Denzin and Lincoln, 1998 (a shorter paperback version of the 1994

edition). You might also consult the International Journal of Qualitative Research in Education both for

examples of this form of research and for methodological discussions.

IS QUALITATIVE RESEARCH A CREDIBLE METHODOLOGY?

WHICH BEST DESCRIBES QUALITATIVE AND QUANTITATIVE RESEARCH?

SOFT HARD

SUBJECTIVE OBJECTIVE

SMALL SCALE LARGE SCALE

IDIOGRAPHIC GENERALIZABLE

JOURNALISM SCIENCE

OPINION TRUTH

Ideas that qualitative research is a `second' best approach rest in large part because of the predominance of the

`science' model of social research. The central values of such an approach are objectivity and generalizability.

Within quantitative approaches, objectivity is maintained in various technical ways. For example, through the

distance between the researcher and the researched that is created through the administration of a formal

questionnaire; through the possibilities of replication of the research; and through the use of external checks on

the methods used. The generalizability of quantitative research is again seen to be possible through technical

solutions. The development of sophisticated statistical and sampling techniques are key to this.

In contrast, qualitative approaches emphasise the importance of getting close to the researched. This is because

one of the purposes of qualitative approaches is to try to depict the participant's view of social reality. Thus,

techniques such as participant observation and unstructured or informal interviews are commonly used. Because

of the time and costs involved in such work, qualitative designs do not generally draw samples from large-scale

data sets. In addition, because of the central role played by the researcher in the generation of data, it is not

possible to replicate qualitative studies.

Ideas of `second' best also rest on the stereotypes that arise when quantitative and qualitative approaches are

compared in this way. In practice, researchers use a variety of methods or techniques of data collection under the

umbrella terms of `qualitative' and `quantitative' to enhance the generalizability of the account (Bryman, 1988). In

addition, as Hammersley (1989) indicates there is much greater variety of theories of social reality within and

between the labels `quantitative' and `qualitative' than we might at first imagine.

In some ways the concerns that arise about a qualitative/quantitative divide can be resolved by giving greater

attention to how these approaches can be combined. For Bryman (1998: 126) this would produce `more complete

accounts of social reality'. However, it is perhaps important to remember that the values conveyed by descriptions

such as soft/hard, idiosyncratic/generalizable, art/science, small scale/large scale have political import. There is a

politics in the choice and use of methods, particularly if you hope that your research will impact on policy or create

change in some way. Jayaratne (1993) encourages feminists to use both methods because she believes this is

more likely to achieve feminist goals. Thus: `My approach to this issue [of choice of method] is political: that is, I

believe the appropriate use of both quantitative and qualitative methods in the social sciences can help the

feminist community in achieving its goals more effectively than the use of either qualitative or quantitative methods

alone' (p 109, emphasis in original).

In a similar vein, Stanley's (1990) account of a Social Services' referral indicates the politics embedded in the

power of `objectivity' in the production of social research knowledge. Stanley delivered a paper that gave an

account of an elderly couple's experiences of Social Services' intervention. This account deliberately omitted the

fact that the couple were her parents. As she states `I felt that presenting `the case study' as my own still recent

experience of caring would disqualify both me and it from `research' and `papers' in the eyes of those present' (p

121).

The key points I would wish to emphasise are:

· There are many stereotypes about qualitative and quantitative approaches.

· Qualitative and quantitative approaches are not as distinctive as the idea of a `divide' suggests.

· These stereotypes lead to comparisons that are political in import.

In addition, it is important to be aware of the various strengths and limitations of any methodological approach. I

set out below a summary that I have adapted, and added to, from Burns (2000-13-14) in respect of qualitative

research:

Limitations of Qualitative Approaches

· The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative

data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity.

· Contexts, situations, events, conditions and interactions cannot be replicated to any extent nor can

generalisations be made to a wider context than the one studied with any confidence.

· The time required for data collection, analysis and interpretation is lengthy.

· The researcher's presence has a profound effect on the subjects of study.

· Issues of anonymity and confidentiality present problems when selecting findings.

· The viewpoints of both researcher and participants have to be identified and elucidated because of issues of

bias.

Strengths

· Because of close researcher involvement, the researcher gains an insider's view of the field. This allows the

researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more

positivistic enquiries.

· Qualitative descriptions can play the important role of suggesting possible relationships, causes, effects and

dynamic processes.

· Because statistics are not used, but rather qualitative research uses a more descriptive, narrative style, this

research might be of particular benefit to the practitioner as she or he could turn to qualitative reports in order to

examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

· Qualitative research adds flesh and blood to social analysis.

HOW DO YOU DO QUALITATIVE RESEARCH?

THREE POINTS TO NOTE

The researcher has several methods for collecting empirical materials, ranging from the interview to direct

observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal

experience. The researcher may also use a variety of different methods of reading and analyzing interviews or

cultural texts, including content, narrative, and semiotic strategies. Faced with large amounts of qualitative

materials, the investigator seeks ways of managing and interpreting these documents, and here data management

methods and computer-assisted models of analysis may be of use. (Denzin and Lincoln, 1994: 14).

... unstructured interviewing and focus group techniques are qualitative methods of data collection (Bowling, 1997:

311)

Qualitative researchers study spoken and written representations and records of human experience, using

multiple methods and multiple sources of data. Several types of data collection might well be used in the one

qualitative project. ... the main ways of collecting qualitative data [are] the interview, observation, participant

observation and documents. Whatever the design and whatever the perspective, the data in most qualitative

research come down to these main types.

(Punch, 1988: 174)

Qualitative research mainly works with two sorts of data. Verbal data are collected in semi-structured interviews

or as narratives. ... Visual data result from applying the various observational methods, ranging from participant

and non-participant observation to ethnography and analysing photos and films ... verbal and visual data are

transformed into texts by documenting them and by transcription. (Flick, 1998: 11-12)

As these quotations illustrate, there are many ways to `do' qualitative research. You might undertake an interview

study, an observation or collect and analyse documents. You might spend months in the field living with those

whom you research. You might undertake a series of unstructured, interviews with a large group of people. You

might focus on a single person. The research may be spread over many years or it may be episodic. You might be

researching an unfamiliar culture or you might be researching your own organisation. You might combine

methods, using statistics to convey generalizability about the case you have selected. You might use a computer

for analysis or you might use your living room floor to spread the transcripts out ready for cut and pasting. You

might work alone or in a team.

I would like to emphasise that the `doing' of qualitative research comprises of three key aspects. The first two

relate to the distinctions that are made between the terms method and methodology. The term method is used

when we are referring to the tools or techniques of data collection such as questionnaires, interviews and

observations. The term methodology has a more philosophical meaning and usually refers to the approach or

paradigm that underpins the research. Punch (1998) suggests that method includes research design, data

collection and data analysis. He notes that the noun methodology is more problematic. He states that `Technically,

it [methodology] refers to the study of method(s), the overall analysis of how research proceeds ... It is often used

more loosely, as in the phrase ` the research methodology of this study'. In such a case, `research method(s)

would be a more accurate term' (p29).

The key point about making a distinction between method and methodology is to emphasise that the tools that are

used to collect data cannot be separated from the frameworks of analysis, and their underpinning theories, that

are used to make sense of this data. We can 'do' qualitative research in a positivistic, an interpretive (the most

common), a postmodern and a critical way (also very common).

Given that how we interpret the data cannot be separated from how it is collected, qualitative researchers also

stress a third features of this approach. This is that to be a qualitative researcher requires one to be highly

reflexive. Alvesson and Skoldberg (2000:5) describe this in terms of drawing:

...attention to the complex relationship between processes of knowledge production and the various contexts of

such processes as well as the involvement of the knowledge producer. This involves operating on at least two

levels in research work and paying much attention to how one thinks about thinking ... [this means] that serious

attention is paid to the way different kinds of linquistic, social, political and theoretical elements are woven together

in the process of knowledge development, during which empirical material is constructed, interpreted and written.

Empirical research in a reflective mode starts from a sceptical approach to what appear at a superficial glance as

unproblematic replicas of the way reality functions, while at the same time maintaining the belief that the study of

suitable (well thought-out) excerpts from this reality can provide an important basis for a generation of knowledge

that opens up rather than closes, and furnishes opportunities for understanding rather than establishes `truths'.

The key point here is that the reflexive social researcher has to recognise their own place and role in generating

the knowledge that is in the research report. For example, Scheurich (1997) suggests that the researcher is an

imperialist who can marshall the data according to her will. What does this mean, then, for the validity of the

research?

WHICH HINTS AND TIPS WORK?

Hint: Think of choosing your research topic in terms of the Goldilocks strategy. You want to select a topic which is

not too big, and not too small, but just right (and one which will not break). (p 25)

Hint: If you find very conflicting arguments in your reading around, you may well have identified an issue or

debate which would be worth exploring in your research project (p 95)

Hint: Being critical does not mean rubbishing or rejecting someone else's work. As a researcher and thinker you

should be able simultaneously to entertain two or more contradictory ideas at one time. (p 105)

Hint: If you carry out a search of the literature using a computer database, and this results in hundreds of

references, do not download them all. Narrow your search further, perhaps by limiting it to works published after a

certain date, or by adding to or changing your key words. (p 113)

Hint: If you feel traumatized or terrorized by the process of analysing the data you have collected, you might like

to think of it as analogous to cooking. What and how you cook depends on your taste, skills and the resources you

have available. You may like your food simple and freshly prepared, or carefully blended over a long period, or

fast and processed. You may mix the ingredients together using a recipe, or based on previous experience, or you

may buy a packet already prepared. You may use a range of tools in your cooking, from a simple knife or spoon to

an expensive foodprocessor. You may be preparing food just for yourself or for a banquet. See if you can find

further parallels as you cook your data! (p 174)

Hint: Some of the tasks involved in analysing data are very basic and repetitive. Save these for when you are

unable to do, or do not feel like doing, anything more demanding. (p 183)

Hint: Try explaining it to a non-specialist again. (p 198)

(All from: Blaxter, Hughes and Tight, 1996)

When teaching a class recently, one student admitted that her focus group interview went badly wrong. She had

been researching `Girls' Nights Out' and had invited two groups of friends to her house. They did not get on and

sat either end of the room throughout the entire evening. One of the members of the class (very gently) told her

that, because this was a common problem, it was normal practice in focus group research to ensure that the

people invited did not know each other beforehand. If it was thought desirable to bring people together who did

know each other then it was important to ensure that they were one rather than two or more groups.

This `tip', of course, would have been much more useful beforehand but this story is not so much concerned about

when, and how, we acquire such advice. It is more concerned to emphasise that it is too easy to dismiss the

usefulness of such knowledge!

The hints set out above have arisen from my own research and teaching experiences. They are aimed at students

who are undertaking research for the first time. I hope that the following exercise will generate some more hints

and tips that are useful in your research.

1. What would you advise?

· You are researching reasons for non-participation in adult learning classes. Some of people you are interviewing

are very uncomfortable about talking to a researcher. What tools or techniques would you use to facilitate the

interview?

· You want to undertake a participant observation study in a local community. How/where do you begin?

· The person you are interviewing expresses some extreme racist views. What do you say in response?

· You are facilitating a focus group discussion. How do you deal with the following:

· (a) two members of the group begin an argument; (b) one member of the group dominates the conversation; (c)

several members of the group get up to help themselves to refreshments and begin a conversation in the corner of

the room; (d) the group runs out of steam after fifteen minutes and seems to have exhausted their thoughts on the

topic; (e) one member of the group never speaks.

· How do I manage all this data?

· This is the first time you have undertaken field research and you are very nervous and anxious. What will help?

· You have been undertaking some long term participant observation in a local community centre. You have

become very passionate about the issues that they are facing. Does this matter?

2. What problems have you encountered in your research that you would welcome some advice about?

3. What is your worst experience of research going wrong?

THE RESEARCH DIARY

Observational Notes

These record events experienced principally through watching and listening. They contain as little interpretation

as possible and are as reliable as the observer can construct them.

Methodological Notes

These record aspects of reflection on the methodology. For example they might include a critique of one's own

tactics. They will note the time, place and technique used.

Theoretical Notes

These are your attempts to derive meaning from your data.

Analytical Notes

These are where you attempt to bring together several aspects of your analysis within a broader, more abstract

statement.

UNDERTAKING AN OBSERVATION

· Are the times at which you carry out your observations relevant?

· Do you need to devise an observational schedule or determine pre-coded categories?

· How are you going to organize your data recording?

· Is it important to you to try and record `everything' or will you be much more selective?

· Are your age, sex, ethnicity, dress or other characteristics likely to affect your observations?

· How artificial is the setting? How visible are you as the observer? Does this matter?

· Is observation enough or will you need to participate, and/or use other means of data collection?

· Are there any situations to which you cannot get access but where observation may be important? How can you

get `backstage'?

· If you are going to participate more directly in the events you will be observing, how are you going to balance the

demands of participation and observation?

CAN RESEARCH BE EMPOWERING?

The terms `power' and `empowerment' crop up a great deal in research related to social justice. This is not

surprising. Improvements in justice are related to power: who has it, how it is exercised and where it manifests

itself. It sounds as if researchers for social justice would find a lot to agree about here. But this is not the case. ...

the very differences invite greater reflexivity and clarity about what researchers think they are doing, and whether

it is worthwhile. (Griffiths, 1998: 117)

Using creative genres of writing ... can help mobilize social action or evoke participatory experiences through

imagination, performance art, and storytelling. (Ellis and Bochner, 1996: 30)

Readers who identify with an oppressed group may achieve a unique outcome through reading about rhetorical

figures who are metaphors for themselves. Self-re-cognition may result in an imaginative naming of one's

conditions. (Barone, 1995: 69)

We re-present stories told by subjugated Others, stories that would otherwise be discarded. And we get a hearing.

(Fine, 1998: 150).

We might suggest that calls for empowerment, giving voice and dialogue are fairly commonplace these days. We

find the idea of empowerment in literatures as far apart as those concerned with organisational competitiveness

and those concerned with the eradication of poverty. The extent of usage of such terms, and the impact of what

some call the postmodern `malaise', has meant that we are more likely today to find that there are 'calls for serious

skepticism of and critical attention to those contemporary education narratives that claim to be emancipatory'

(Lather, 1992: 129). All liberatory talk has oppressive potentials.

Gore (1992: 56) usefully delineates the concept of empowerment into three components. Thus, empowerment

suggests (1) an agent of empowerment, (2) a notion of power as property, and (3) some kind of vision or desirable

end sate). This Foucauldian approach suggests that to empower someone, then, means that you are authorized

by yourself or others to give something. Yet what does authority mean. As Weiler (1995: 33) points out for many

feminists the practices of authority can be paradoxical:

... the issue of institutional authority raises the contradictions of trying to achieve a democratic and collective ideal

in a hierarchical institution, but it also raises the question of the meaning of authority for feminist teachers, whose

right to speak or to hold power is itself under attack in a patriarchal, (and racist, homophobic, classist, and so on)

society.

At times, feminist women may indeed seek to claim their authority `over' precisely because it is already questioned

through gendered organizational and social practices. Empowerment also suggests that power is some kind of

property that can be given away, handed over, borrowed or shared. Simplified discussions of empowerment

encourage perceptions of uni-directional models that do not credit others with power nor allow for the contradictory

messiness through which the everyday enactments of power emerge.

Finally, what are people being `empowered' for? Empowerment suggests a desirable end state but what might that

be? And do we all agree with whatever that is?

These issues are central for those working to `empower' and to work for social justice. As Griffiths (op cit) notes

there is no single response to these issues. Yet Griffiths (1998: 95-96 and 102) offers the following ten principles

that underpin working for social justice in qualitative educational research. These are:

1. Improvement: A main reason for doing the research is to get improvement in social justice in and from

education. Results of research include knowledge (but not only propositional knowledge or information) and

improvements)

2. Knowledge and learning: A main reason for doing the research is to get knowledge and to learn from it. This

is inclusive of various kinds of knowledge. It implies the best possible of whatever kind is aimed at.

3. Radical change of any of the beliefs and values is possible: Improvements in knowledge are always

uncertain, so researchers must be prepared to change their minds radically, and to challenge others during and

after doing the research. Research results and processes may surprise and discomfort any or all of the members

of the researcher community, and also other educational researchers.

4. Collaboration and consultation with the immediate research community. Researchers need to work

collaboratively with people as part of the community carrying out the research. It is difficult to establish hard and

fast boundaries to this research community. Creating, establishing and working with such a diverse research

community requires that all sectors respect and work with each other in conditions of trust and safety, in the

interests of improving education. It is acknowledged that the processes of consultation and change are going to

result in conflict and people feeling exposed when putting their views on the line. Waiving trust and safety can be

morally justified, but only in extreme conditions.

5. Openness to a wider community: Researchers need to be open to the viewpoints of all concerned with the

research. This means not only those in (4) above, but also users of the research and anyone else to whom it is

relevant, eg pupils, teachers, support staff, parents, LEA advisors, the neighbourhood, policy-makers and

pressure groups. Strategies are needed to listen to quiet, less powerful, voices.

6. Openness to political groupings and perspectives: Researchers need to seek out and be open to the

viewpoints of socio-political groups. There are alliances to be made between groups of people on the basis of, for

instance, class, race, gender, sexuality. They cross-cut alliances between, for instance, teachers, advisors,

children and parents. All these groups need acknowledgement, support and understanding. This is a source of the

reflexivity mentioned in (7) and (8).

7. Reflexivity about own position and interests. Reflexivity is needed about the researchers' own socio-political

positions and interests. Argument, anger and risk are all part of the process. Some of the feelings of risk come

about because such consultation requires researcher(s) to be open to reflexivity about their own position and

interests.

8. Reflexivity about own understanding and values. Reflexivity is needed about the researchers' own

understanding and values. It is important that the researchers acknowledge their allegiance to beliefs, values and

traditions. Their most dearly held knowledge and values may be based in these principles of research for social

justice, but these too are revisable.

9. Perfection in research is not to be found: There is no hope of doing perfect research. Utopia does not exist.

All research programmes have to be constructed on the run, and against a background of social and educational

change. Time constraints and compromises are inevitable. By the time the best possible design, methods and

forms of dissemination are found, the situation will most probably have changed. It follows that all research must

be subject to critique. Good research still needs to improve. This may mean that there are areas in which a

research programme is excellent, but some other things may be out of its control altogether.

10. Taking responsibility as part of the wider educational research community. Researchers must recognize

their responsibilities related to being part of the community of educational researchers. Good research also

requires researchers to be open to the community of educational and other researchers, in a process of reflexivity

related to dangerous knowledge and power. Advances always come as a patchwork or ragbag. There can never

be a tidy overarching rationale or masterplan for improving fairness. Danger has to be acknowledged. Vigilance is

needed. On the other hand, advances need to be recognised and celebrated.

Griffiths warns that it is important to note that this is not a `pick-and-mix' list. All principles are interlocking.

Does your work embrace these principles or do you think they are valid enough to adopt?

Further Work

Caution does need exercising with the use of concepts such as empowerment and power. Most common

assumptions are based on top down, Us and Them, models where power is something that an elite or a boss has.

This is not to deny that some groups/individuals do have more power than others but the tendency is to focus in

this one direction. Foucauldian analyses of power highlight how power is not simply repressive but productive, that

power is everywhere and that alongside power we will find resistance. The Griffiths (op cit) text gives a fuller

exploration of this and provides associated examples from research in education.

AN INTRODUCTION TO QUALITATIVE RESEARCH

BIBLIOGRAPHY

Alvesson, M and Skoldberg, K (2000) Reflexive Methodology: New Vistas for Qualitative Research, London,

Sage

Arbnor, I and Bjerke, B (1997) Methodology for Creating Business Knowledge, London, Sage

Atkinson, P (1990) The Ethnographic Imagination: Textual Constructions of Reality, London, Routledge

Barone, T (1995) Persuasive writings, vigilant readings and reconstructed characters: the paradox of trust in

educational storysharing, in J Hatch and R Wisnieski (Eds) Life History and Narrative, London, Falmer, pp 63-74

Bartunek, J and Louis, M (1996) Insider/Outsider Team Research, London, Sage

Belenky, M, Clinchy, B, Goldberger, N and Tarule, J (1986) Women's Ways of Knowing: The Development of

Self, Voice and Mind, New York, Basic Books

Bell, J (1996) Doing Your Research Project, Buckingham, Open University Press

Birch, M (1998) Re/constructing Research Narratives: Self and Sociological Identity in Alternative Settings, in J

Ribbens and R Edwards (Eds) Feminist Dilemmas in Qualitative Research: Public Knowledge and Private

Lives, London, Sage, pp 171-185

Blaxter, L, Hughes, C and Tight, M (1996) How to Research, Buckingham, Open University Press

Bowling, A (1997) Research Methods in Health: Investigating Health and Health Services, Buckingham, Open

University Press

Bryman, A (1988) Quantity and Quality in Social Research, London, Routledge

Burns, R (2000) Introduction to Research Methods, London, Sage

Butler, J (1990) Feminism and the Subversion of Identity, New York, Routledge

Coffey, A (1999) The Ethnographic Self, London, Sage

Coffey, A and Atkinson, P (1996) Making Sense of Qualitative Data Analysis: Complementary Strategies,

Thousand Oaks (Calif), Sage

Crotty, M (1998) The Foundations of Social Research: Meaning and Perspective in the Research Process ,

London, Sage

Denscombe, M (1998) The Good Research Guide: for small scale social research projects, Buckingham,

Open University Press

Denzin, N and Lincoln, Y (Eds) (1994) Handbook of Qualitative Research, Thousand Oaks (Calif), Sage

Denzin, N and Lincoln, Y (Eds) (1998) The Landscape of Qualitative Research: Theories and Issues,

Thousand Oaks (Calif), Sage

Ellis, C and Bochner, A (Eds) (1996) Composing Ethnography: Alternative Forms of Qualitative Writing,

Walnut Creek, Altmira Press

Ely, M with Anzul, M, Friedman, T, Garner, D and Steinmetz, A (1991) Doing Qualitative Research: Circles

within Circles, London, Falmer

Fine, M (1998) Working the Hyphens: Reinventing Self and Other in Qualitative Research, in N Denzin and Y

Lincoln (Eds) The Landscape of Qualitative Research, Thousand Oaks (Calif), Sage, pp 130-155

Flick, U (1998) An Introduction to Qualitative Research, London, Sage

Francis, B, Robson, J and Read, B (2000) Inscriptions of Gender: Gendered Patterns in Undergraduate

Writing and First Degree Award, Paper presented at Feminist Pedagogy Conference, University of Liverpool

Gore, J (1992) What We Can Do For You! What Can `We' Do For `You'? Struggling over Empowerment in Critical

and Feminist Pedagogy, in C Luke and J Gore (Eds) Feminisms and Critical Pedagogy, New York, Routledge,

pp 54-73

Griffiths, M (1998) Educational Research for Social Justice: Getting off the Fence, Buckingham, Open

University Press

Guba, E and Lincoln, Y (1994) Competing Paradigms in Qualitative Research, in N Denzin and Y Lincoln (Eds)

Handbook of Qualitative Research, Thousand Oaks (Calif), Sage, pp 105-117

Hammersley, M (1989) The Dilemma of the Qualitative Method, London, Routledge

Harding, S (1991) Whose Science? Whose Knowledge? Ithaca (HY) Cornell University Press

Harrison, R (1992) Employee Development, London, IPD

Hatch, J and Wisnieski, R (Eds) Life History and Narrative, London, Falmer

Hughes, J (1990) The Philosophy of Social Research, London, Longman

Hughes, C (1992) A Stranger in the House: Researching the Stepfamily, in R Burgess (Ed) Learning about

Fieldwork: Studies in Qualitative Methodology, London, JAI Press, Vol 3, pp 33-62

Hughes, C (1999a) Learning to be intellectually insecure: the dis/empowering effects of reflexive practice,

International Journal of Social Research Methodology, 1, 4, pp 281-296

Hughes, C (1999b) The Woman Manager: How Do We Know Her? Paper presented at the Department of

Management Seminar, University of Keele

Jayratne, T (1993) The value of quantitative methodology for feminist research, in M Hammersley (Ed) Social

Research: Philosophy, Politics and Practice, London, Sage

Lather, P (1992) Post-critical Pedagogies: A Feminist Reading, in C Luke and J Gore (Eds) Feminisms and

Critical Pedagogy, New York, Routledge, pp 120-137

McPherson, D (1996) Order out of Chaos: The Autobiographical Works of Maya Angelou, London, Virago

Maynard, M and Purvis, J (Eds) (1994) Researching Women's Lives from a Feminist Perspective, London,

Taylor and Francis

Mayo, A and Lank, E (1994) The Power of Learning: A Guide to Gaining Competitive Advantage, London,

IPD

Mason, J (1996) Qualitative Researching, London, Sage

Morgan, D (1986/1997) Focus Groups as Qualitative Research, London, Sage

Oakley, J (1981) Interviewing Women: A Contradiction in Terms, in H Roberts (Ed) Doing Feminist Research,

London, RKP, pp 30-61

Oleson, V, Droes, N, Hatton, D, Chico, N and Schatzman, L (1994) Anlayzing Together: Recollections of a Team

Approach, in A Bryman and R Burgess (Eds) Analyzing Qualitative Data, London, Routledge, pp 111-128

Punch, K (1998) Introduction to Social Research: Quantitatie and Qualitative Approaches, London, Sage

Rath (1999) Getting Smarter, Coventry, Department of Continuing Education, University of Warwick (unpublished

PhD)

Richardson, L (1992) The Consequences of Poetic Representation: Writing the Other, Rewriting the Self, in C Ellis

and M Flaherty (Eds) Investigating Subjectivity: Research on Lived Experience, Newbury Park (Calif), Sage,

pp 125-140

Ribbens, J and Edwards, R (Eds) (1998) Feminist Dilemmas in Qualitative Research: Public Knowledge and

Private Lives, London, Sage

Scheurich, J (1997) Research Method in the Postmodern, London, Falmer

Skeggs, B (1997) Formations of Class and Gender, London, Sage

Slim, H and Thompson, P (1993) Listening for a Change: Oral Testimony and Development, London, Panos

Smith, M (1998) Social Science in Question, London, Sage/Open University

Stanley, L (1990) A Referral was made: behind the scenes during the creation of a Social Services Department

`elderly' statistic, in L Stanley (Ed) Feminist Praxis, London, Routledge

Stronach, I and MacLure, M (1997) Educational Research Undone: The Postmodern Embrace, Buckingham,

Open University Press

Steyaert, C and Bouwen, R (1994) Group Methods of Organizational Analysis, in C Cassell and G Symon (Eds)

Qualitative Methods in Organizational Research: A Practical Guide, London, Sage, pp 123-146

Weiler, K (1995) Freire and a Feminist Pedagogy of Difference, in H Holland and M Blair with S Sheldon (Eds)

Debates and Issues in Feminist Research and Pedagogy, Philadelphia (PA), Multilingual Matters/Open

Univeristy, pp 23-44

Wilson, V (1997) Focus Groups: A Useful Qualitative Method for Educational Research? British Educational

Research Journal, 23, 2, pp 209-224

Useful Internet Sites

CAQDAS Networking Project: set up in conjunction with ESRC to disseminate an understanding of the practical

skills needed to use software to facilitate qualitative data analysis and to encourage debate about methodological

and epistemological issues raised by the use of such software. http://caqdas.soc.surrey.ac.uk/

Economic and Social Research Council: to keep up to date with developments and potential websites for both

qualitative and quantitative methods. http://www.esrc.ac.uk/

Sociological Research Online: an on-line journal. http://www.socresonline.org.uk/