Choosing a statistical test

26 Choosing a statistical test

There are very many statistical tests available to graphic means of presenting the issues in this the researcher. Which test one employs depends

chapter (see http://www.routledge.com/textbooks/ on several factors, for example:

9780415368780 – Chapter 26, file 26.1.doc).

the purpose of the analysis (e.g. to describe or explore data, to test a hypothesis, to seek

How many samples?

correlations, to identify the effects of one or more independent variables on a dependent

In addition to the scale of data being used (nominal, ordinal, interval, ratio), the kind of

variable, to identify differences between two or statistic that one calculates depends in part more groups; to look for underlying groupings on, first, whether the samples are related to, of data, to report effect sizes)

the kinds of data with which one is working or independent of, each other, and second, the (parametric and non-parametric)

number of samples in the test. With regard to

the scales of data being used (nominal, ordinal, the first point, as we have seen in previous interval, ratio)

chapters, different statistics are sometimes used

the number of groups in the sample when groups are related to each other and when

the assumptions in the tests they are independent of each other. Groups will

whether the samples are independent of each

be independent when they have no relationship other or related to each other.

to each other, e.g. in conducting a test to see if there is any difference between the voting of males

Researchers wishing to use statistics will need to and females on a particular item, say mathematics ask questions such as:

performance. The tests that one could use here are,

for example: the chi-square test (for nominal data), What statistics do I need to answer my research the Mann-Whitney U test and Kruskal-Wallis questions?

(for ordinal data), and the t-test and analysis of Are the data parametric or non-parametric?

variance (ANOVA) for interval and ratio data. How many groups are there (e.g. two, three or However, there are times when the groups more)?

might be related. For example, we may wish to Are the groups related or independent?

measure the performance of the same group at What kind of test do I need (e.g. a difference

test, a correlation, factor analysis, regression)? two points in time – before and after a particular intervention – or we may wish to measure the

We have addressed several of these points in voting of the same group on two different factors, the preceding chapters; those not addressed in

say preference for mathematics and preference for previous chapters are addressed here. In this

music. Here it is not different groups that are being chapter we draw together the threads of the

involved, but the same group on two occasions and discussion of statistical analysis and address what,

the same two on two variables respectively. In this for many researchers, can be a nightmare: deciding

case different statistics would have to be used, for which statistical tests to use. In the interests

example the Wilcoxon test, the Friedman test, the of clarity we have decided to use tables and

t-test for paired samples, and the sign test. Let us

HOW MANY SAMPLES? 587

Chapter

give a frequently used example of an experiment With regard to the number of samples in the (Box 26.1).

test, there are statistical tests which are for single In preceding chapters we have indicated which

samples (one group only, e.g. a single class in tests are to be used with independent samples and

school), for two samples (two groups, e.g. males and which are to be used with related samples.

females in a school) and for three or more samples

Box 26.1

Identifying statistical tests for an experiment

Control group

Control group

Wilcoxon test or t-test

for paired samples

t-test for independent samples for the pretest

t-test for independent

(depending on data

samples for the post-test →

Wilcoxon test or t-test

group

for paired samples

(depending on data type)

Box 26.2

Statistical tests to be used with different numbers of groups of samples

Scale One sample

More than two samples of data

Two samples

Related Nominal

Fisher exact test

McNemar

Chi-square (χ 2 )

Cochran Q

k-samples test

Chi-square (χ 2 )

Chi-square (χ 2 )

one-sample test

two-samples test

Ordinal Kolmogorov-

Kruskal-Wallis test Friedman test Smirnov

Mann-Whitney U test

Wilcoxon

matched pairs test

one-sample test

Kolmogorov-Smirnov

Sign test

Ordinal regression

test

analysis

Wald-Wolfowitz Spearman rho Ordinal regression

analysis

One-way ANOVA Repeated and ratio

Interval t-test

t-test

t-test for paired

samples

measures ANOVA

Pearson product

Two-way ANOVA

moment correlation Tukey hsd test

Scheff´e test

588 CHOOSING A STATISTICAL TEST

Box 26.3

Types of statistical tests for four scales of data

Nominal

Ordinal

Interval and ratio

Measures of

Tetrachoric

Spearman’s rho

Pearson product-moment

Point biserial

Kendall rank order

correlation

correlation

Phi coefficient

Kendall partial rank correlation

Cramer’s V

Measures of

Chi-square

Mann-Whitney U test

t-test for two independent

t-test for two related samples

Cochran Q

Wilcoxon matched pairs

One-way ANOVA

Binomial test

Friedman two-way analysis of

Two-way ANOVA for more

variance Wald-Wolfowitz test

Tukey hsd test

Kolmogorov-Smirnov test

Scheff´e test

Measures of linear

Ordinal regression analysis

Linear regression

relationship between independent and dependent variables

Multiple regression

Identifying

Factor analysis

underlying factors, data reduction

Elementary linkage analysis

(e.g. parents, teachers, students and administrative The statistical tests to be used also depend on staff in a school). Tests which can be applied

the scales of data being treated (nominal – ratio) to a single group include the binomial test, the

and the tasks which the researcher wishes to chi-square one-sample test, and the Kolmogorov-

perform – the purpose of the analysis (e.g. to Smirnov one-sample test; tests which can be

discover differences between groups, to look applied to two groups include the chi-square test,

for degrees of association, to measure the Mann-Whitney U test, the t-test, the Spearman

effect of one or more independent variables and Pearson tests of correlation; tests which can

on a dependent variable etc.). In preceding

be applied to three or more samples include the chapters we have described the different scales chi-square test, analysis of variance and the Tukey

of data and the kinds of tests available test. We set out some of these tests in Box 26.2. It

for different purposes. In respect of these is essential to use the correct test for the correct

considerations, Box 26.3 summarizes some of the number of groups.

main tests here.

HOW MANY SAMPLES? 589

Chapter

Box 26.4

Choosing statistical tests for parametric and non-parametric data

Non-parametric

data

Descriptive Correlation

Goodness

Differences

Effects of

variables on

regression

dependent

Frequencies Spearman’s

Three or Mode

Three or

more related Cross-tabulations

Wallis test

test

U test

Parametric data

Descriptive Correlation

Differences

Effect size

Effects of

Grouping of

variables on

(reduction)

dependent

Cohen’s d

variable

Eta 2

Frequencies Pearson’s

Factor Mode

Regression analysis Mean

Standard deviation correlation Median

coefficient

Two related

Paired

Two or more

Three or

Three or more related

Repeated

samples

measures

ANOVA

590 CHOOSING A STATISTICAL TEST

Box 26.5

Statistics available for different types of data

Data type Legitimate statistics

Points to observe/questions/examples

Nominal Mode (the score achieved by the Is there a clear ‘front runner’ that receives the highest greatest number of people)

score with low scoring on other categories, or is the modal score only narrowly leading the other categories? Are there two scores which are vying for the highest score – a bimodal score?

Frequencies Which are the highest/lowest frequencies? Is the distribution even across categories?

Chi-square (a statistic that charts the Are differences between scores caused by chance/accident difference between statistically expected

or are they statistically significant, i.e. not simply caused by and actual scores)

chance?

Ordinal Mode Which score on a rating scale is the most frequent? Median (the score gained by the middle

What is the score of the middle person in a list of scores? person in a ranked group of people or, if there is an even number of cases, the score which is midway between the highest score obtained in the lower half of the cases and the lowest score obtained in the higher half of the cases).

Frequencies Do responses tend to cluster around one or two categories of a rating scale? Are the responses skewed towards one end of a rating scale (e.g. ‘strongly agree’)? Do the responses pattern themselves consistently across the sample? Are the frequencies generally high or generally low (i.e. whether respondents tend to feel strongly about an issue)? Is there a clustering of responses around the central categories of a rating scale (the central tendency, respondents not wishing to appear to be too extreme)?

Chi-square Are the frequencies of one set of nominal variables (e.g. sex) significantly related to a set of ordinal variables?

Spearman rank order correlation (a Do the results from one rating scale correlate with the statistic to measure the degree of

results from another rating scale? Do the rank order association between two ordinal

positions for one variable correlate with the rank order variables)

positions for another variable?

Mann-Whitney U-test (a statistic to Is there a significant difference in the results of a rating measure any significant difference

scale for two independent samples (e.g. males and between two independent samples)

females)?

Kruskal-Wallis analysis of variance (a Is there a significant difference between three or more statistic to measure any significant

nominal variables (e.g. membership of political parties) and differences between three or more

the results of a rating scale?

independent samples) continued

ASSUMPTIONS OF TESTS 591

Chapter

Box 26.5

continued

Interval and ratio Mode Mean

What is the average score for this group? Frequencies

Median Chi-square Standard deviation (a measure of the

Are the scores on a parametric test evenly distributed? dispersal of scores)

Do scores cluster closely around the mean? Are scores widely spread around the mean? Are scores dispersed evenly? Are one or two extreme scores (‘outliers’) exerting a disproportionate influence on what are otherwise closely clustered scores?

z-scores (a statistic to convert How do the scores obtained by students on a test scores from different scales, i.e. with

which was marked out of 20 compare to the scores by different means and standard

the same students on a test which was marked out of deviations, to a common scale, i.e.

with the same mean and standard deviation, enabling different scores to be compared fairly)

Pearson product-moment Is there a correlation between one set of interval data correlation (a statistic to measure

(e.g. test scores for one examination) and another set the degree of association between

of interval data (e.g. test scores on another two interval or ratio variables)

examination)?

t-tests (a statistic to measure the Are the control and experimental groups matched in difference between the means of

their mean scores on a parametric test? Is there a one sample on two separate

significant different between the pretest and post-test occasions or between two samples

scores of a sample group?

on one occasion) Analysis of variance (a statistic to

Are the differences in the means between test results ascertain whether two or more

of three groups statistically significant?

means differ significantly)

The type of tests used also vary according

Assumptions of tests

to whether one is working with parametric or non-parametric data. Boxes 26.4 and 26.5 draw

Statistical tests are based on certain assumptions. It is important to be aware of these assumptions and

together and present the kinds of statistical tests to operate fairly within them. Some of the more available, depending on whether one is using

parametric or non-parametric data, together with widely used tests have the following assumptions (Box 26.6).

the purpose of the analysis. Box 26.5 sets out The choice of which statistics to employ is not the commonly used statistics for data types and arbitrary, but dependent on purpose. purposes (Siegel 1956; Cohen and Holliday 1996;

Hopkins et al. 1996).

592 CHOOSING A STATISTICAL TEST

Box 26.6

Assumptions of statistical tests

Data are normally distributed, with no outliers.

Mode There are few values, and few scores, occurring which have a similar frequency. Median

There are many ordinal values.

Chi-square

Data are categorical (nominal). Randomly sampled population. Mutually independent categories. Data are discrete (i.e. no decimal places between data points).

80 per cent of all the cells in a cross-tabulation contain 5 or more cases. Kolmogorov-Smirnov

The underlying distribution is continuous. Data are nominal.

t-test and analysis of

Population is normally distributed.

variance

Sample is selected randomly from the population. Each case is independent of the other. The groups to be compared are nominal, and the comparison is made using interval and ratio data. The sets of data to be compared are normally distributed (the bell-shaped Gaussian curve of distribution). The sets of scores have approximately equal variances, or the square of the standard deviation is known. The data are interval or ratio.

Wilcoxon test

The data are ordinal. The samples are related.

Mann-Whitney and The groups to be compared are nominal, and the comparison is made using ordinal data. Kruskal-Wallis

The populations from which the samples are drawn have similar distributions. Samples are drawn randomly. Samples are independent of each other.

Spearman rank order

The data are ordinal.

correlation Pearson correlation

The data are interval and ratio.

Regression (simple

Assumptions underlying regression techniques:

and multiple)

The data derive from a random or probability sample. The data are interval or ratio (unless ordinal regression is used). Outliers are removed. There is a linear relationship between the independent and dependent variables. The dependent variable is normally distributed (the bell-shaped Gaussian curve of distribution). The residuals for the dependent variable (the differences between calculated and observed scores) are approximately normally distributed. Collinearity is removed (where one independent variable is an exact or very close correlate of another).

Factor analysis

The data are interval or ratio. The data are normally distributed. Outliers have been removed. The sample size should not be less than 100–150 persons. There should be at least five cases for each variable. The relationships between the variables should be linear. The data must be capable of being factored.

Notes

1 THE NATURE OF INQUIRY – SETTING THE FIELD

1 We are not here recommending, nor would we wish of scientific research, Mishler contends, is largely to encourage, exclusive dependence on rationally

tacit and unexplicated; moreover, scientists learn it derived and scientifically provable knowledge for the

through a process of socialization into a ‘particular conduct of education – even if this were possible.

form of life’. The discovery, testing and validation There is a rich fund of traditional and cultural

of findings is embedded in cultural and linguistic wisdom in teaching (as in other spheres of life)

practices and experimental scientists proceed in which we would ignore to our detriment. What we

pragmatic ways, learning from their errors and are suggesting, however, is that total dependence

failures, adapting procedures to their local contexts, on the latter has tended in the past to lead to

making decisions on the basis of their accumulated an impasse: and that for further development and

experiences. See, for example, Mishler, E. G. (1990) greater understanding to be achieved education must

Validation in inquiry-guided research: the role of needs resort to the methods of science and research.

exemplars in narrative studies. Harvard Educational 2 A classic statement opposing this particular view of

Review , 60 (4), 415–42. science is that of Kuhn, T. S. (1962) The Structure

5 See, for example, Rogers, C. R. (1969) Freedom of Scientific Revolutions . Chicago, IL: University

to Learn . Columbus, OH: Merrill, and also Rogers, of Chicago Press. Kuhn’s book, acknowledged as

C. R. and Stevens, B. (1967) Person to Person: The an intellectual tour de force, makes the point

Problem of Being Human . London: Souvenir. that science is not the systematic accumulation of

6 Investigating social episodes involves analysing knowledge as presented in textbooks; that it is a

the accounts of what is happening from the far less rational exercise than generally imagined. In

points of view of the actors and the participant effect, it is ‘a series of peaceful interludes punctuated

spectator(s)/investigator(s). This is said to yield by intellectually violent revolutions . . . in each of

three main kinds of interlocking material: images of which one conceptual world view is replaced by

the self and others, definitions of situations, and rules another.’

for the proper development of the action. See Harr´e, 3 For a straightforward overview of the discussions

R. (1976) The constructive role of models. In here see Chalmers, A. F. (1982) What Is This Thing

L. Collins (ed.) The Use of Models in the Social Called Science? (second edition). Milton Keynes:

Sciences . London: Tavistock. Open University Press.

7 See also Verma, G. K. and Beard, R. M. (1981) What 4 The formulation of scientific method outlined earlier

is Educational Research? Aldershot: Gower, for further has come in for strong and sustained criticism.

information on the nature of educational research E. G. Mishler, for example, describes it as a

and also a historical perspective on the subject. ‘storybook image of science’, out of tune with the actual practices of working scientists who turn

2 THE ETHICS OF EDUCATIONAL AND SOCIAL out to resemble craftpersons rather than logicians.

RESEARCH

By craftpersons, Mishler (1990) is at pains to stress that competence depends upon ‘apprenticeship training, continued practice and experienced-based,

1 For example, Social Research Association (2003); contextual knowledge of the specific methods

American Sociological Association (1999); British applicable to a phenomenon of interest rather than

Educational Research Association (2000); Ameri- an abstract ‘‘logic of discovery’’ and application of

can Psychological Association (2002); British So- formal ‘‘rules’’ ’ (Mishler 1990). The knowledge base

ciological Association (2002); British Psychological

594 NOTES

Society (2005). Comparable developments may be Wade, N. (1983) Betrayers of Truth: Fraud and Deceit found in other fields of endeavour. For an exami-

in the Halls of Science . New York: Century. nation of key ethical issues in medicine, business, and journalism together with reviews of common

5 SENSITIVE EDUCATIONAL RESEARCH ethical themes across these areas, see Serafini, A. (ed.) (1989) Ethics and Social Concern. New York:

1 See also Walford (2001: 38) in his discussion of Paragon House. The book also contains an ac-

gaining access to UK public schools, where an early count of principal ethical theories from Socrates to

question that was put to him was ‘Are you one of R. M. Hare.

us?’

2 US Dept of Health, Education and Welfare, Public 2 Walford (2001: 69) comments on the very negative Health Service and National Institute of Health

attitudes of teachers to research on UK independent (1971) The Institutional Guide to D.H.E.W. Policy

schools, the teachers feeling that researchers had on Protecting Human Subjects . DHEW Publication

been dishonest and had tricked them, looking only (NIH): 2 December, 72–102.

for salacious, sensational and negative data on the 3 As regards judging researchers’ behaviour, perhaps

school (e.g. on bullying, drinking, drugs, gambling the only area of educational research where the

and homosexuality).

term ethical absolute can be unequivocally applied and where subsequent judgement is unquestionable

8 HISTORICAL AND DOCUMENTARY is that concerning researchers’ relationship with

RESEARCH

their data. Should they choose to abuse their data 1 By contrast, the historian of the modern period, for whatever reason, the behaviour is categorically

i.e. the nineteenth and twentieth centuries, is more wrong; no place here for moral relativism. For

often faced in the initial stages with the problem once a clear dichotomy is relevant: if there is

of selecting from too much material, both at the such a thing as clearly ethical behaviour, such

stage of analysis and writing. Here the two most abuse is clearly unethical. It can take the form

common criteria for such selection are the degree of of, first, falsifying data to support a preconceived,

significance to be attached to data, and the extent often favoured, hypothesis; second, manipulating

to which a specific detail may be considered typical data, often statistically, for the same reason

of the whole.

(or manipulating techniques used – deliberately 2 However, historians themselves usually reject such including leading questions, for example); third,

a direct application of their work and rarely indulge using data selectively, that is, ignoring or excluding

in it on the grounds that no two events or the bits that don’t fit one’s hypothesis; and fourth,

contextual circumstances, separated geographically going beyond the data, in other words, arriving

and temporally, can possibly be equated. As the at conclusions not warranted by them (or over-

popular sayings go, ‘History never repeats itself’ and interpreting them). But even malpractice as serious

so, ‘The only thing we can learn from History is that as these examples cannot be controlled by fiat:

we can learn nothing from History’. ethical injunctions would hardly be appropriate

3 Thomas, W. I. and Znaniecki, F. (1918) The in this context, let alone enforceable. The only

Polish Peasant in Europe and America. Chicago, answer (in the absence of professional monitoring)

IL: University of Chicago Press. For a fuller is for the researcher to have a moral code that is

discussion of the monumental work of Thomas and ‘rationally derived and intelligently applied’, to use

Znaniecki, see Plummer, K. (1983) Documents of the words of the philosopher, R. S. Peters, and to

Life: An Introduction to the Problems and Literature be guided by it consistently. Moral competence, like

of a Humanistic Method. London: Allen & Unwin, other competencies, can be learned. One way of

especially Chapter 3, The Making of a Method. acquiring it is to bring interrogative reflection to

See also Madge, J. (1963) The Origin of Scientific bear on one’s own code and practice, e.g. did I

Sociology. London: Tavistock. For a critique of provide suitable feedback, in the right amounts, to

Thomas and Znaniecki, see Riley, M. W. (1963) the right audiences, at the right time? In sum, ethical

Sociological Research 1: A Case Approach . New York: behaviour depends on the concurrence of ethical

Harcourt, Brace & World. thinking which in turn is based on fundamentally

4 Sikes, P., Measor, L. and Woods, P. (1985) Teacher thought-out principles. Readers wishing to take

Careers . Lewes: Falmer. See also Smith, L. M. (1987) the subject of data abuse further should read Peter

Kensington Revisited . Lewes: Falmer; Goodson, I. Medawar’s (1991) elegant and amusing essay,

and Walker, R. (1988) Putting life into educa- ‘Scientific fraud’, in D. Pike (ed.) The Threat and the

tional research. In R. R. Sherman and R. B. Webb Glory: Reflections on Science and Scientists . Oxford:

(eds) Qualitative Research in Education: Focus and Oxford University Press, and also Broad, W. and

Methods . Lewes: Falmer; Acker, S. (1989) Teachers,

NOTES 595

Notes

Gender and Careers . Lewes: Falmer; Blease, D. and of bullying by their pupils: a study to investigate

Cohen, L. (1990) Coping with Computers: An incidence. British Journal of Educational Psychology, Ethnographic Study in Primary Classrooms . London:

68, 255–68; Hall, K. and Nuttall, W. (1999) The Paul Chapman; Evetts, J. (1990) Women in Pri-

relative importance of class size to infant teachers mary Teaching . London: Unwin Hyman; Goodson,

in England. British Educational Research Journal, 25 I. (1990) The Making of Curriculum. Lewes:

(2), 245–58; Rigby, K. (1999) Peer victimisation Falmer; Evetts, J. (1991) The experience of sec-

at school and the health of secondary school ondary headship selection: continuity and change.

students. British Journal of Educational Psychology, 69, Educational Studies , 17 (3), 285–94; Sikes, P. and

95–104; Strand, S. (1999) Ethnic group, sex and Troyna, B. (1991) True stories: a case study in the

economic disadvantage: associations with pupils’ use of life histories in teacher education. Educa-

educational progress from Baseline to the end of tional Review , 43 (1), 3–16; Winkley, D. (1995)

Key Stage 1. British Educational Research Journal, 25 Diplomats and Detectives: LEA Advisers and Work .

London: Robert Royce. Examples of different kinds of survey studies are as follows: Francis’s (1992) ‘true cohort’ study of

9 SURVEYS, LONGITUDINAL, patterns of reading development, following a group CROSS-SECTIONAL AND TREND STUDIES

of 54 young children for two years at six-monthly intervals; Blatchford’s (1992) cohort, cross-sectional

1 There are several examples of surveys, including study of 133–175 children (two samples) and their the following: Millan, R., Gallagher, M. and Ellis, R.

attitudes to work at 11 years of age; a large- (1993) Surveying adolescent worries: development

scale, cross-sectional study by Munn et al. (1990) of the ‘Things I Worry About’ scale. Pastoral

into pupils’ perceptions of effective disciplinarians, Care in Education , 11 (1), 43–57; Boulton, M. J.

with a sample size of 543; a trend/prediction study (1997) Teachers’ views on bullying: definitions,

of school building requirements by a government attitudes and abilities to cope. British Journal of

department (Department of Education and Science Educational Psychology , 67, 223–33; Cline, T. and

1977), identifying building and improvement needs Ertubney, C. (1997) The impact of gender on

based on estimated pupil populations from births primary teachers’ evaluations of children’s difficulties

during the decade 1976–86; a survey study by Belson in school. British Journal of Educational Psychology,

(1975) of 1,425 teenage boys’ theft behaviour; a 67, 447–56; Dosanjh, J. S. and Ghuman, P. A. S.

survey by Hannan and Newby (1992) of 787 student (1997) Asian parents and English education – 20

teachers (with a 46 per cent response rate) and years on: a study of two generations. Educational

their views on government proposals to increase the Studies , 23 (3), 459–72; Foskett, N. H. and

amount of time spent in schools during the training Hesketh, A. J. (1997) Constructing choice in

period.

continuous and parallel markets: institutional 2 Examples of longitudinal and cross-sectional stud- and school leavers’ responses to the new post-

ies include the following: Davies, J. and Brember, I 16 marketplace. Oxford Review of Education,

(1997) Monitoring reading standards in year 6: 23 (3), 299–319; Gallagher, T., McEwen, A. and

a 7-year cross-sectional study. British Educational Knip, D. (1997) Science education policy: a

Research Journal , 23 (5), 615–22; Preisler, G. M. survey of the participation of sixth-form pupils

and Ahstr¨om, M. (1997) Sign language for hard in science and the subjects over a 10-year

of hearing children – a hindrance or a benefit for period, 1985–95. Research Papers in Education,

their development? European Journal of Psychol- 12 (2), 121–42; Jules, V. and Kutnick, P. (1997)

ogy of Education , 12 (4), 465–77; Busato, V. V., Student perceptions of a good teacher: the gender

Prins, F. J., Elshant, J. J. and Hamaker, C. (1998) perspective. British Journal of Educational Psychology,

Learning styles: a cross-sectional and longitudi- 67, 497–511; Borg, M. G. (1998) Secondary

nal study in higher education. British Journal of school teachers’ perceptions of pupils’ undesirable

Educational Psychology , 68, 427–41; Davenport, E. behaviours. British Journal of Educational Psychology,

C. Jr, Davison, M. L., Kuang, H., Ding, S., Kin, 68, 67–79; Papasolomoutos, C. and Christie, T.

S-K. and Kwak, N. (1998) High school math- (1998) Using national surveys: a review of

ematics course-taking by gender and ethnic- secondary analyses with special reference to schools.

ity. American Educational Research Journal, 35 Educational Research , 40 (3), 295–310; Tatar, M.

(3), 497–514; Davies, J. and Brember, I. (1998) (1998) Teachers as significant others: gender

Standards in reading at key stage 1 – a cross- differences in secondary school pupils’ perceptions.

sectional study. Educational Research, 40 (2), 153–60; British Journal of Educational Psychology , 68,

Marsh, H. W. and Yeung, A. S. (1998) Lon- 255–68; Terry, A. A. (1998) Teachers as targets

gitudinal structural equation models of academic

596 NOTES

self-concept and achievement: gender differences 2 Examples of experimental research can be seen in the in the development of math and English con-

following: Dugard, P. and Todman, J. (1995) Anal- structs. American Educational Research Journal, 35 (4),

ysis of pre-test and post-test control group designs in 705–38; Noack, P. (1998) School achievement and

educational research. Educational Psychology, 15 (2), adolescents’ interactions with the fathers, mothers,

181–98; Bryant, P., Devine, M., Ledward, A. and and friends. European Journal of Psychology of Ed-

Nunes, T. (1997) Spelling with apostrophes and ucation , 13 (4), 503–13; Galton, M., Hargreaves, L,

understanding possession. British Journal of Educa- Comber, C., Wall, D. and Pell, T. (1999) Changes

tional Psychology , 67, 91–110; Hall, E., Hall, C. and in patterns in teacher interaction in primary class-

Abaci, R. (1997) The effects of human relations rooms, 1976–1996. British Educational Research Jour-

training on reported teacher stress, pupil control nal , 25 (1), 23–37.

ideology and locus of control. British Journal of Educa- 3 For further information on event-history analysis

tional Psychology , 67, 483–96; Marcinkiewicz, H. R. and hazard rates we refer readers to Allison (1984);

and Clariana, R. B. (1997) The performance effects Plewis (1985); Hakim (1987); Von Eye (1990); Rose

of headings within multi-choice tests. British Jour- and Sullivan (1993).

nal of Educational Psychology , 67, 111–17; Tones, K. (1997) Beyond the randomized controlled trial:

11 CASE STUDIES a case for ‘judicial review’. Health Education Re-

search , 12 (2), i–iv; Alfassi, M. (1998) Reading 1 For further examples of case studies see Woods, P.

for meaning: the efficacy of reciprocal teach- (1993) Managing marginality: teacher development

ing in fostering reading comprehension in high through grounded life history. British Educational

school students in remedial reading classes. Research Journal , 19 (5), 447–88; Bates, I. and

American Educational Research Journal , 35 (2), Dutson, J. (1995) A Bermuda triangle? A case

309–22; Bijstra, J. O. and Jackson, S. (1998) So- study of the disappearance of competence-based

cial skills training with early adolescents: effects vocational training policy in the context of practice.

on social skills, well-being, self-esteem and cop- British Journal of Education and Work , 8 (2),

ing. European Journal of Psychology of Education, 13 41–59; Jacklin, A. and Lacey, C. (1997) Gender

(4), 569–83; Cline, T., Proto, A., Raval, P. D. and integration in the infant classroom: a case study.

Paolo, T. (1998) The effects of brief exposure British Educational Research Journal , 23 (5), 623–40.

and of classroom teaching on attitudes children express towards facial disfigurement in peers. Ed- ucational Research , 40 (1), 55–68; Didierjean, A.

12 EX POST FACTO RESEARCH and Cauzinille-Marm`eche, E. (1998) Reasoning by analogy: is it schema-mediated or case-based? Euro-

1 In Chapters 12 and 13 we adopt the symbols pean Journal of Psychology of Education , 13 (3), and conventions used in Campbell, D. T. and

385–98; Overett, S. and Donald, D. (1998) Paired Stanley, J. C. (1963) Experimental and Quasi-

reading: effects of a parental involvement pro- Experimental Designs for Research on Teaching . Boston,

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Africa. British Journal of Educational Psychology, Chapter 13.

68, 347–56; Sainsbury, M., Whetton, C., Mason, K. 2 For further information on logical fallacies, see

and Schagen, I. (1998) Fallback in attainment on Cohen, M. R. and Nagel, E. (1961) An Introduction

transfer at age 11: evidence from the summer lit- to Logic and Scientific Method . London: Routledge

eracy schools evaluation. Educational Research, 40 & Kegan Paul. The example of the post hoc, ergo

(1), 73–81; Littleton, K., Ashman, H., Light, P., propter hoc fallacy given by the authors concerns

Artis, J., Roberts, T. and Oosterwegel, A. (1999) sleeplessness, which may follow drinking coffee,

Gender, task contexts, and children’s performance but sleeplessness may not occur because coffee was

on a computer-based task. European Journal of Psy- drunk.

chology of Education , 14 (1), 129–39. 3 For a detailed discussion of the practical issues

13 EXPERIMENTS, QUASI-EXPERIMENTS, SINGLE- in educational experimentation, see Riecken and CASE RESEARCH AND META-ANALYSIS

Boruch (1974); Bennett and Lumsdaine (1975); Evans (1978: Chapter 4).

1 Questions have been raised about the authenticity of 4 An example of meta-analysis in educational research both definitions and explanations of the Hawthorne

can be seen in Severiens, S. and ten Dam, G. (1998) effect. See Diaper, G. (1990) The Hawthorne effect:

A multilevel meta-analysis of gender differences in a fresh examination. Educational Studies, 16 (3),

learning orientations. British Journal of Educational 261–7.

Psychology , 68, 595–618. The use of meta-analysis

NOTES 597

Notes

is widespread, indeed the Cochrane Collaboration into pupils’ perceptions of the behaviour of

is a pioneer in this field, focusing on meta-analyses white teachers towards minority pupils in school. of randomized controlled trials (see Maynard and

See Naylor, P. (1995) Adolescents’ perceptions of Chalmers 1997).

teacher racism. Unpublished PhD dissertation, Loughborough University of Technology.

15 QUESTIONNAIRES 1 This is the approach used in Belbin’s (1981)

18 OBSERVATION

celebrated work on the types of personalities in 1 For an example of time-sampling, see Childs, G. a management team.

(1997) A concurrent validity study of teachers’ ratings for nominated ‘problem’ children. British

16 INTERVIEWS Journal of Educational Psychology , 67, 457–74. 1 Examples of interviews in educational research in-

2 For an example of critical incidents, see Tripp, D. clude the following: Ferris, J. and Gerber, R. (1996)

(1994) Teachers’ lives, critical incidents and pro- Mature-age students’ feelings of enjoying learning

fessional practice. International Journal of Qualitative in a further education context. European Journal of

Studies in Education , 7 (1), 65–72. Psychology of Education , 11 (1), 79–96; Carroll, S.

3 For an example of an observational study, and Walford, G. (1997) Parents’ responses to the

see Sideris, G. (1998) Direct classroom observation. school quasi-market. Research Papers in Education, 12

Research in Education , 59, 19–28. (1), 3–26; Cullen, K. (1997) Headteacher appraisal:

a view from the inside. Research Papers in Educa-

20 PERSONAL CONSTRUCTS

tion , 12 (2), 177–204; Cicognani, C. (1998) Par- ents’ educational styles and adolescent autonomy.

1 See also the following applications of personal con- European Journal of Psychology of Education , 13 (4),

struct theory to research on teachers and teacher 485–502; Van Etten, S., Pressley, M., Freebern, G.

groups: Shapiro, B. L. (1990) A collaborative ap- and Echevarria, M. (1998) An interview study of col-

proach to help novice science teachers reflect on lege freshmen’s beliefs about their academic motiva-

changes in their construction of the role of the tion. European Journal of Psychology of Education, 13

science teacher. Alberta Journal of Educational Re- (1), 105–30; Robinson, P. and Smithers, A. (1999)

search , 36 (3), 203–22; Cole, A. L. (1991) Personal Should the sexes be separated for secondary educa-

theories of teaching: development in the forma- tion – comparisons of single-sex and co-educational

tive years. Alberta Journal of Educational Research, schools? Research Papers in Education, 14 (1), 23–49.

37 (2), 119–32; Corporal, A. H. (1991) Repertory grid research into cognitions of prospective pri-

17 ACCOUNTS mary school teachers. Teaching and Teacher Edu- cation , 36, 315–29; Lehrer, R. and Franke, M. L.

1 For an example of concept mapping in educational (1992) Applying personal construct psychology to research see Lawless, L., Smee, P. and O’Shea, T.

the study of teachers’ knowledge of fractions. Jour- (1998) Using concept sorting and concept

nal for Research in Mathematical Education , 23 (3), mapping in business and public administration, and

223–41; Shaw, E. L. (1992) The influence of meth- education: an overview. Educational Research, 40 (2),

ods instruction on the beliefs of preservice elemen- 219–35.

tary and secondary science teachers: preliminary 2 For further examples of discourse analysis,

comparative analyses. School Science and Mathemat- see Ramsden, C. and Reason, D. (1997) Conver-

ics , 92, 14–22.

sation – discourse analysis in library and informa- 2 For an example of personal constructs in educational tion services. Education for Information, 15 (4),

research, see Morris, P. (1983) Teachers’ percep- 283–95; Butzkamm, W. (1998) Code-switching in

tions of their pupils: a Hong Kong case study. a bilingual history lesson: the mother tongue as

Research in Education , 29, 81–6; Derry, S. J. and a conversational lubricant. Bilingual Education and

Potts, M. K. (1998) How tutors model students: Bilingualism , 1 (2), 81–99; Mercer, N., Wegerif, R.

a study of personal constructs in adaptive tutor- and Dawes, L. (1999) Children’s talk and the de-

ing. American Educational Research Journal, 35 (1), velopment of reasoning in the classroom. British

Educational Research Journal , 25 (1), 95–111. 3 Cohen, L. (1993) Racism Awareness Materials in

21 ROLE-PLAYING

Initial Teacher Training . Report to the Leverhulme Trust, 11–19 New Fetter Lane, London, EC4A

1 For an account of a wide range of role- 1NR. The video scenarios are part of an inquiry

play applications in psychotherapy, see Holmes, P.

598 NOTES

and Karp, M. (1991) Psychodrama: Inspiration and analysis. British Journal of Educational Psychology, 67, Technique . London: Routledge.

323–38. For examples of research using correlation coefficients, see Lamb, S., Bibby, P., Wood, D. and

24 QUANTITATIVE DATA ANALYSIS Leyden, G. (1997) Communication skills, educa- tional achievement and biographic characteristics

1 Bynner and Stribley (1979: 242) present a useful of children with moderate learning difficul- table of alphas, which lists values of r ii from

ties. European Journal of Psychology of Education, 0.05 to 0.80 and the values of item numbers

12 (4), 401–14; Goossens, L., Marcoen, A., van from 2 to 50. The values of alpha can then be

Hees, S. and van de Woestlijne, O. (1998) interpolated. See Bynner, J. and Stribley, K. M. (eds.)

Attachment style and loneliness in adolescence. (1979) Social Research: Principles and Procedures.

European Journal of Psychology of Education , 13 London: Longman and the Open University Press,

(4), 529–42; Okagaki, L. and Frensch, P. A. (1998) Table 19.1.

Parenting and school achievement: a multiethnic 2 Muijs (2004) indicates that, in SPSS, one can

perspective. American Educational Research Journal, find multicollinearity by looking at ‘collinearity

diagnostics’ in the ‘Statistics’ command box, and 4 Examples of multilevel modelling in educational re- in the collinearity statistics one should look at the

search can be seen in the following: Fitz-Gibbon, C. ‘Tolerance’ column on the output. He indicates that

T. (1991) Multilevel modelling in an indicator sys- values will vary from 0 to 1, and the higher the value

tem. In S. W. Raudenbush and J. D. Willms (eds) the less is the collinearity, whereas a value close to 0

Schools, Classrooms and Pupils: International Studies indicates that nearly all the variance in the variable

of Schooling from a Multilevel Perspective . San Diego, is explained by the other variables in the model.

CA: Academic Press; Bell, J. F. (1996) Question choice in English literature examination. Oxford

25 MULTIDIMENSIONAL MEASUREMENT AND Review of Education , 23 (4), 447–58; Hill, P. W. and FACTOR ANALYSIS

Rowe, K. J. (1996) Multilevel modelling in school effectiveness research. School Effectiveness and School

1 Robson, (1993) suggests that as few as 100 can be Improvement , 7 (1), 1–34; Schagen, I. and Sains- used.

bury, M. (1996) Multilevel analysis of the key stage 2 Self-serving bias refers to our propensity to accept

1 national curriculum data in 1995. Oxford Review responsibility for our successes, but to deny

of Education , 22 (3), 265–72; Croxford, L. (1997) responsibility for our failures.

Participation in science subjects: the effect of the 3 For examples of research conducted using fac-

Scottish curriculum framework. Research Papers in tor analysis, see McEneaney, J. E. and Sheridan, E.

Education , 12 (1), 69–89; Thomas, S., Sammons, M. (1996) A survey-based component for pro-

P., Mortimore, P. and Smees, R. (1997) Differen- gramme assessment in undergraduate pre-service

tial secondary school effectiveness: comparing the teacher education. Research in Education, 55,

performance of different pupil groups. British Educa- 49–61; Prosser, M. and Trigwell, K. (1997) Rela-

tional Research Journal , 23 (4), 351–69; Kivulu, J. M. tions between perceptions of the teaching environ-

and Rogers, W. T. (1998) A multilevel analysis ment and approaches to teaching. British Journal

of cultural experience and gender influences on of Educational Psychology , 67, 25–35; Vermunt, J.

causal attributions to perceived performance in D. (1998) The regulation of constructive learning

mathematics. British Journal of Educational Psy- processes. British Journal of Educational Psychology,

chology , 68, 25–37; McNiece, R. and Jolliffe, F. 68, 149–71; Andrews, P. and Hatch, G. (1999) A

(1998) An investigation into regional differ- new look at secondary teachers’ conception of

ences in educational performance in the Na- mathematics and its teaching. British Educational

tional Child Development Study. Educational Re- Research Journal , 25 (2), 203–23; Valadines, N.

search , 40 (1), 13–30; Mooij, T. (1998) Pupil-class (1999) Formal reasoning performance of higher

determinants of aggressive and victim behaviour secondary school students: theoretical and educa-

in pupils. British Journal of Educational Psychology, tional implications. European Journal of Psychology

68, 373–85; Musch, J. and Br¨oder, A. (1999) Test of Education , 14 (1), 109–17. For an example

anxiety versus academic skills: a comparison of two of research using cluster analysis see Seifert, T.

alternative models for predicting performance in a L. (1997) Academic goals and emotions: results

statistics exam. British Journal of Educational Psy- of a structural equation model and a cluster

chology , 69, 105–16.

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INDEX

Absolutism 51, 61–2 Access 51, 55–8, 65, 100, 109–10, 121–4, 178–80, 237 accounts 171, 197–8, 384–95; characteristics of 384–5;

examples of 385–8; problems with 392–3; procedures in 385–8 see also discourse analysis; ethogenic method; network analysis; speech acts; stories

achievement tests 417 action research 29, 30, 34, 45, 65–6, 69–70, 85,

296–313; as critical praxis 302–4; emancipatory 302–4; models of 306; nature of 296–302; planning of 304–10; principles of 296–302; problems in 311–12; reflexivity in 310; stages of 304–10 see also collaborative action research; Delphi techniques; Nominal Group Technique

advocacy 54 alpha 519, 552–3, 555 see also Cronbach’s alpha alternative hypothesis 83, 515 American Psychological Association 60, 63, 72, 414,

417, 520 analysis of variance 546–52, 585, 592 analytic induction 86, 134, 178, 472–3 anonymity 64–5, 75, 126, 318 ANOVA see analysis of variance anthropological methods see ethnographic methods applied research 41 aptitude tests 417 AQUAD 488 Assessment 86 association, concepts of see correlation assumptions of statistical tests 592 ATLAS 488

beneficence 58–61, 71, 72 see also ethics beta weighting 539–42 betrayal 65–6, 74 bias 68, 129, 151, 157, 196, 214, 410–11; in historical

research 196; in interviewing 129 biographies 86, 170 Blumer, H. 20, 224, 167 Boids 247 British Educational Research Association 72

case studies 34, 85, 170, 253–63; defined 253–8; examples of 258–60; observation in 260; planning of 261–2;

catalytic validity see validity, kinds of causal research 34, 272, 505 causal-comparative research 269 Central Limit Theorem 106–7, 246 children 53–4, 69, 154, 374–6, 390 chi-square 101, 523–8, 580–3, 592 cluster analysis 584–5 cluster sampling see sampling, kinds of Cochrane Collaboration 290 Code-A-Text 488 codes of practice 51, 53, 57, 63, 70, 71–4, 76–7 coding 86, 145, 184, 348, 477–81, 483–7, 492–3 cognitive mapping 86 cohort studies 211–12, 213–18, 219–21 collaborative action research 296–304 complexity theory 31, 33–4, 246–7, 249 computerized adaptive testing 162, 433–4 computers, see AQUAD; ATLAS; Boids; Code-A-Text;

computerized adaptive testing; CROSSTABS; ETHNOGRAPH; ethnographic methods; Geographical Information Systems; HyperQuad2; HyperRESEARCH; Hypersoft; internet; interviews; NUD.IST;

N-Vivo; QUALPRO; simulations; SphinxSurvey; SPSS Comte, A. 9–10 conceptions of social reality 7–10 concurrent validity see validity, kinds of confidence intervals 103, 522 confidence levels 103 confidentiality 64–5, 72, 73, 74, 126, 175, 181, 231, 318 consequential validity see validity, kinds of consistency, see reliability constant comparison 86, 116–17, 473, 493–4 constructs, personal see personal constructs construct validity see validity, kinds of content analysis, 86, 197, 462–6, 470–3, 475–91;

defined 475–6; example of 483–7; procedures in 476–83; reliability in 490–1

634 INDEX

content validity see validity, kinds of contingency tables see crosstabulations continuous variable 528 convenience sampling see sampling, kinds of convergent validity see validity, kinds of Cooley, J. 172 correlational studies 102 core variables 494 correlation 516–19, 528–9, 530–3; co-efficients

534–6; explained 51; interpretation of 535–6; see also curvilinearity; effect size; Pearson product moment correlation; Spearman

rank-order correlation; statistical significance; Type I errors; Type II errors cost/benefits ratio 51–2, 58, 63, 67, 126 covering letters 339–41 covert research 53, 68, 73, 124–5, 174–5, 180, 408–10 criterion-referenced tests see tests, kinds of criterion-related validity see validity, kinds of critical educational research 26–33 critical ethnography 187–9 critical pedagogy 32 critical theory 18, 26–33, 35, 139 Cronbach’s alpha 148, 162, 506 cross sectional studies 213 CROSSTABS 488 crosstabulations 508–15 cultural validity see validity, kinds of curriculum research 30–2 curvilinearity 533–4

data analysis 86–7, 91–2, 184–6, 459–592; in accounts 200; in observation 258–62; in qualitative research 184–6, 462–500; in quantitative research 501–592

data collection, 80, 181–4; in ethnographic methods

181–4; 261–2; in historical research 193–4 data reporting see reporting Data Protection Act 72 deception 66–9, 72, 450–2 see also ethics deduction 6 ‘definition of the situation’ 21, 24, 168, 189, 500 degrees of freedom 527–8 Delphi techniques 309–10 dependability of data 158 dependent variable 156, 265–6, 269, 272–3, 504–5 descriptive statistics 503–4 descriptive validity see validity, kinds of determinism 8, 11, 33 diagnostic tests see tests dichotomous questions 322–3 difference testing 523–8 542–58 discourse analysis 86, 389–92 see also speech acts distribution of sample means 106–7 documentary research 171, 183, 191–204

domain analysis 184 domain referencing 162 see also tests duration recording 402–3

ecological validity see validity, kinds of effect size 520–5 eigenvalues 563 elementary linkage analysis 559–60, 588 emancipation 29, 38, 140, 302–4, 305 emancipatory interest 27–8, 32 emic approaches 169 empiricism 11 empowerment 29, 36, 139, 302–4 episodes 391 epistemology 5, 7, 31, 47, 169 equality 32, 35, 36, 38, 71 errors, see Type I; Type II eta 522–3 ethical codes 53, 57, 59, 70, 71–4, 76–7 ethics 39, 51–77, 98, 231; in evaluative research 51,

70–1; in experiments 273–4; in interviews 382–3; in observation 408–10; in questionnaires 317–18; in role play 450–2; in sensitive research 120, 124–7; in testing 432–3 see also covert research

ETHNOGRAPH 488 ethnographic methods 39, 55, 78, 84, 135–6, 167–90,

384; characteristics of 167–71; critical 187–9; ethics in 174–5; planning 173–87; problems in 189–90; sampling in 175–8 see also naturalistic research

ethnomethodology 22–6 ethogenic method 20, 384–5, 392–4 etic approaches 169 evaluating web sites 245 evaluative research 41–6, 70–1 evaluative validity see validity, kinds of event-history analysis 224–5 event sampling 400–1 evidence-based research 290–6 see also Cochrane

Collaboration; randomized controlled trials existential phenomenology 17 experience-sampling method 177 experiments and quasi-experiments 33, 84, 272–96,

521; and ex post facto research 264–71, 272; conduct of 286–7; designs of 269–71, 272–90; examples of 284–6, 287–90; internet-based 239–41; quasi-experiments 282–6; reliability and validity in 155–7; single-case research 284–6; true 275–82, 289–90 see also Cochrane Collaboration; meta-analysis; pre-test; post-test; randomized controlled trials

exploratory data analysis 506–15 ex post facto research 264–71, 272; characteristics of

266–8; planning 269–71 external validity see validity, kinds of

INDEX 635

Index

face validity see validity, kinds of

inference 170, 185, 483

factor analysis 560–76, 587, 592; examples of 563–70,

inferential statistics 503–4

570–6 informal interview see interview, types of factorial designs in experiments 280–1

informants see ethnographic methods; gatekeepers factor loadings 567–70

informed consent 51, 52–5, 60, 61, 62, 72, 125, 174, fairness 139, 318

false consciousness 26

instantaneous sampling 401–2

feminist research 34–41, 129, 140 interactionism see ethnographic methods; naturalistic fidelity 72, 134

research

field notes 171, 182, 260–1, 404–8 see also accounts;

interaction effect 156

case studies; computers; documentary research; interests see knowledge-constitutive interests observation

internal validity see validity, kinds of fitness for purpose 39, 78, 98, 165, 181, 424

internet 226–45; internet-based experiments 239–41; focus groups 376–7

internet-based interviews 241–2; internet-based focused interview see interviews

surveys 226–39; internet searching 242–5 Frankfurt School 18, 34

interpretive paradigm 20–26, 47, 384 see also freedom, degrees of 527–8

naturalistic methods

frequencies 506–15 interpretive validity see validity, kinds of Friedman test 527, 557–8, 587

inter-rater reliability see reliability, kinds of interval recording 402

Garfinkel, H. 23–4

interval scale 502, 586–90

gatekeepers 109–10, 123–4, 128, 139, 179 interviews 129, 130, 131, 150–5, 171, 218–22, 349–83; generalizability 135, 137, 169, 190

analysis of 155, 368–72; children 374–6; conduct of Geographical Information Systems 251–2

361–5, 366–7; ethical issues 382–3; focused 376–9; Goffman, E. 175

group 373–6; internet 241–2; nature of 349–50; grid technique see personal construct theory

non-directive 377–9; kinds of 349–51; planning of grounded theory 117, 168, 170, 491–500; defined

129, 356–61; purposes of 351–2; questions in 491–2; example of 495–9; procedures in 492–5

357–61; reliability and validity in 150–5; response group interviewing 373–6 see also focus groups

modes in 359–61; telephone 153, 222–3, 379–82; Guttman scales 325

transcription of 155, 365–8; 370; types of 352–6 see also focus groups; questions; triangulation

Habermas, J. 18, 26–32

item analysis see tests

halo effect 18–19

item difficulty see tests

Hawthorne effect 144, 156, 160, 189

item discriminability see tests

hermeneutic interest 27, 32

item response theory see tests

hermeneutics 18, 26, 29 historical research 191–204 see also biographies; life

jury validity see validity, kinds of

histories; stories humanistic psychology 19

Kelly, G. 435–7

HyperQuad2 488

Kendall’s tau 528, 529

Hypersoft 488 knowledge-constitutive interests 27–30, 31–2 hypothesis 6, 14–15, 82–3, 168, 169–70, 173, 195,

Kruskal-Wallis analysis of variance 527, 554–6, 592 207, 469; alternative 83, 515; null 83, 515, 526

Kuder-Richardson formula for reliability 162 hypothesis testing 519–20

laddering, see personal constructs

ideology 26–9, 31

leading questions 151

ideology critique 28–30, 34, 35, 37

legitimacy 26–7

idiographic approach to human behaviour 8, 19, 168

levels of data see scales of data

illuminative approaches see ethnographic methods

Levene test 523, 543–5

independent variable 265–6, 269, 272–3, 280–1,

life histories 86, 164, 198–9

504–5 Likert scales see questions, Likert scales indexicality 24

linear regression 537–9

induction 6, 134, 168 linkage analysis see elementary linkage analysis inductive-deductive reasoning 6

longitudinal studies 170, 211–13, 214–18, 219–21

636 INDEX

Mann-Whitney U-test 527, 552–4, 592

organizational culture 95–8

Marxism 27, 39

ownership of research 175

matched pairs design in experiments 279–80 matrix planning 87–93, 95 Mead, G.H. 24

panel studies 211–12, 213–18, 219–21 Mean 503, 592

parametric data 503, 589, 591 McNiff, J. 305, 308

parametric designs in experiments 281 median scores 503, 592

parametric tests 589

meta-analysis 291–6

parsimony, principle of 11

Milgram, S. and role-playing 53, 67, 451–2 participant observation 54, 69, 171, 179, 258–60, modal scores 503, 592

404–8 see also case studies, covert research, multidimensional measurement 559–92 see also cluster

ethnographic methods

analysis; elementary linkage analysis participatory research 34 see also collaborative action multidimensional scaling 576–80

research

multilevel modelling 583–4 Pearson’s product moment correlation coefficient 528, multi-phase sampling see sampling, kinds of

multiple choice questions see questions, multiple choice Percentages 506–15, 529–30 multiple regression 539–42, 588, 592

Permission 54, 55–6 see also ethics personal constructs 435–47; characteristics of 435–6;

narrative 198–9 elicited and provided 436–7; examples of 443–5; grid naturalistic research 19, 134, 136–7, 167–90;

analysis of 439–43, 446–7; laddering 439; procedures characteristics of 167–71; data analysis in 184–6;

for 437–8; pyramid constructions in 439; repertory data collection in 181–4, 258–60; ethics in 174–5;

grids 440–5

interviews in 182; observation in 404–8; planning of phenomenology 22–5, 167–71 see also definition of the 173–87; problems in 189–90; sampling in 175–8 see

situation; ethnographic methods; interpretive also ethnographic methods

paradigm; naturalistic research negotiation 24

piloting 341–2

network analysis 388–9 planning educational research 78–99 Nominal Group Technique 309

policy-making and research 46–7 nominal scale 502, 586–90

politics 30, 44, 46–7, 126

nominalism 9

population see sampling

nomothetic approach to human behaviour 9–11, 168 positivism 7–11, 17–19, 21, 33, 35, 37, 47, 136, 143, non-directive interview see interviews

non-maleficence 58–61, 66, 72, 75, 174 see also ethics postal questionnaire 345–6, 347 non-parametric data 503, 589

post-test 272–3, 276–9, 282–4, 287, 432 non-parametric tests 589, 590

power 26–9, 35, 38, 39, 43, 46, 120, 127–30, 151–2 non-participant observation 258–60, 397–403

power of a test 522

non-probability sampling see sampling, kinds of powerful people 127–30, 151–2, 154 normative paradigm 21–2, 47

practical interest 27 see also hermeneutic interest norm-referenced tests see tests

prediction 27

NUD.IST 488 predictive validity see validity, kinds of null hypothesis 83, 515, 526

pre-test 272–3, 276–9, 282–4, 287, 432 N-vivo 488

primary source of data 193–4 principal components analysis see factor analysis privacy 61, 63–4, 65–6, 71, 72, 73, 120, 125 see also

objectivity 11, 18

ethics

observation 169–70, 394, 396–413; critical incidents probability sampling see sampling, kinds of 404; duration of 402–3; ethics of 408–10; kinds of

progressive focussing 184

179; naturalistic 169, 258–60, 404–8; reliability and purposive sampling see sampling, kinds of validity in 158–9, 412–3; structured 397–403 see also

accounts; case studies; covert research; field notes; non-participant observation; participant observation

QSR 488

one-tailed tests 504 qualitative data analysis 461–500; content analysis in ontology 5, 7

470–3; examples of 462–6; grounded theory in open-ended interviews see interviews

491–5; introduction to 461–2; presenting data in opportunity sampling see sampling, kinds of

467–72; systematic approaches to 468–72; tabulating ordinal scale 502, 586–90

data in 462–6

INDEX 637

Index

qualitative research 19–24, 39, 40, 134, 148–9, respondent validation 66, 92, 134, 149

right to know 59, 66 see also ethics

QUALPRO 488 right to privacy 59, 66 see also ethics quantitative data analysis 500–92

role 171, 179–80 see also access; ethnographic methods; quantitative research 14–19, 39, 40, 134, 146, 355

gatekeepers; naturalistic research quasi-experimental designs 282–6

role-playing 68, 448–57; ethics in 450–2; examples of questionnaires 56, 317–48; administration of 344–6;

455–7; in educational settings 448–50, 452–3; computer usage in 226–39; construction and design

procedures for 453–4; uses of 455–7 see also Milgram, 320–39; ethical issues in 317–8; internet in 226–39;

S and role playing; Stanford Prison Experiment layout 338–9; operationalization 318–21; piloting

rotation in factor analysis see factor analysis 341–2; planning 318–21, 342–3; postal 345–6, 347;

R-squared 541

practical considerations in 342–4; reliability and validity in 157–8; sequence in 336–7 see also

sample 60 see sampling, kinds of

interviews; questions; sensitive research; surveys

sample, access to 55–8, 109–10

questions 130–1, 151, 153–4, 320–37, 357–61, 362–5, sample mortality 144, 175, 215 380–1; closed 321; constant sum 328–9; contingency sample size 100–5, 106, 113, 118, 162, 237, 527 332–3; dichotomous 322–3; filters 332–3; Guttman

sampling 78, 90, 97, 100–18, 121–4; in internet surveys scales 325; Likert scales 325–8; matrix 331–2;

236–9; sampling error 106–8; sampling frame 117; multiple choice 323–4; open-ended 329–31, 357;

sampling, kinds of: boosted 115; cluster 112; rank ordering 325; rating scales 325–8, 331–2, 402;

convenience 114, 176; critical case 176; dimensional ratio 329; semantic differential scales 325–8;

115; event 400–1; extreme case 176; instantaneous sensitive 333–4; sequencing 336–7; Thurstone scales

401–2; matched 279–80; multi-phase 113; 325, 327; wording 334–6 see also interviews; sensitive

non-probability sample 113–17; 237; opportunity research

114, 176; probability 110–13, 151; purposive115–77; quota sampling see sampling, kinds of

quota 114; random 102–3, 110–11; reputational case 176; snowball 116, 122, 176; stage 112–3; stratified

randomization 276–7 109; 111–2; 177; systematic111, 114–5; theoretical randomized controlled trials 277–8

116–7, 176, 177–8, 492; typical case 176; unique random sampling see sampling, kinds of

case 176; volunteer 116, 157, 237 see also case studies ranking response 325

sampling, representativeness of 100, 102–3, 108–9 rating scales 325–32

scales of data 207, 322, 360, 502–3, 586–91 ratio scale 329, 502–3, 586–90

Schutz, A. 22–3

reactivity 156, 160, 189 see also Hawthorne effect scientific method 5, 6, 11–19, 35, 42 records 183 see also documentary research; field notes;

scree plot 564–5

historical research

secondary sources of data 193–4

reflective practice 302–4 self-completion questionnaires 344–5 reflexivity 23, 35–6, 90, 171–2, 310

semantic differential scales 325–8

regression 536–42, 588, 592 see also multiple regression semi-structured interviews see interviews, kinds of relativism 51, 61–2

sensitive research 62, 65, 75, 109–10, 119–32, 318 reliability 133–64, 234; consistency 161; equivalence

significance, statistical see statistical significance; see also 147; in experiments 155–7; in interviews 150–5; in

effect size

life histories 164, 203–4; in observation 158–8; in

simulations 60, 245–51

qualitative research 181, 189, 490–1; in quantitative

single-case research 284–6

research 506; in questionnaires 157–8; internal

situated ethics 61, 69, 74, 127, 132

consistency 147–8; inter-rater 148, 411; in tests

skewed data 504

159–64; parallel forms 148; stability 146–7, 148 see snowball sampling see sampling, kinds of also triangulation

social episodes 391

repeated measures experiments 281–2

sociology of knowledge 31–2

repertory grid, see personal constructs Solomon design of experiments 278 reporting 87, 186, 195–6, 372–3

Spearman-Brown formula for reliability 147–8, 162, reputational case sampling see sampling, kinds of

research 41–2; design 78–99; methodology 36–7, Spearman rank-order correlation 517, 529, 587, 592 47–8, 78, 83–6, 89–91, 95; operationalization 81–3;

speech acts 389–91, 498

purposes 93, 94, 95; reporting of 87, 92; research

SphinxSurvey 488

questions 80, 95, 468; sponsorship 41–7, 74–5;

SPSS 500–85

timing of 95, 98, 180

stability see reliability

638 INDEX

stage sampling see sampling, kinds of thick description 21, 134, 169, 405 standard deviation 504, 512–14

Thurstone scales see questions standard error of mean 106–8, 504

transcription see case studies; ethnographic methods; standard error of proportions 107–8

field notes; interviews; naturalistic research Stanford Prison Experiment 61, 450

transformative research 26–7, 29 statistical significance 515–19 see also effect size

trend studies 213–18, 219–21 statistical tests 586–592

triangulation 37, 141–14, 158; defined 141–4; types of stories 394–5

142–4

stratified sampling see sampling, kinds of

t-test 543–6, 587, 592

stress 483–7

two-tailed test 504

structured interview see interview, kinds of

Tukey test 548–50, 588

structured observation see observation

Type I error 145, 520

subjectivity 8, 17, 25, 162, 294

Type II error 145, 520

surveys 84, 205–25; cross-sectional 213, 214–18, typical case sampling see sampling, kinds of 219–21; internet 226–39; longitudinal 211–13, 214–18, 219–21; planning 208–11; postal 218;

unique case sampling see sampling, kinds of sampling 211; telephone 218–23; trend studies 213,

unstructured interview see interview, kinds of 214–18, 219–21 see also questionnaires, sampling

unstructured observation see observation symbolic interactionism 22–6 see also ethnographic methods

validity 133–64; defined 133–5; in experiments 155–7; systematic sampling see sampling, kinds of

in interviews 150–5; in life histories 164, 203–4; in systemic validity see validity, kinds of

observation 158–9, 412–13; in questionnaires 157–8; in tests 159–64

technical interest 27, 31 validity, kinds of: catalytic 139–40; concurrent 140–1, telephone interviewing 153, 379–82

163; consequential 140, 152, 163; construct 138, 163; test/re-test 146–7, 162

content 137–8, 162–3; convergent 138; tests 86, 414–34; achievement 417; aptitude 417;

criterion-related 140–1, 163; cultural 139; commercially produced 416–18; computerized

descriptive 135; discriminant 138; ecological 138–9, adaptive testing 433–4; construction of 418–32;

157; evaluative 135; external 136–7, 156–7, 158; criterion-referenced 415–6; diagnostic 418;

face 163; internal 135–6, 155–6, 214; interpretive domain-referenced 415–6; ethics of 432–3; format

135; jury 163; predictive 140–1, 163; systemic 163; 426; item analysis 418–29; item difficulty 418–21;

theoretical 135

item discriminability 418, 421, 422–3; item response variables: dependent 156, 265–6, 269, 272–3, 504–5; theory 422–3; item writing 426–9; layout of 429;

independent 265–6, 269, 272–3, 280–1, 504–5; in non-parametric 424–5; norm-referenced 415–6;

experimental research 265–6, 272; kinds of 504–5, one-tailed 504; parametric 414–5; power of 522;

528

pre-test 432; post-test 432; purposes of 414, 418–9; varimax rotation see factor analysis reliability and validity in 159–64; scoring 430–2;

verstehen approaches 27

timing of 429–30; two-tailed 504 see also questions

voluntarism 8, 52

Textbase Alpha 488 theoretical validity see validity, kinds of

web sites, evaluation of 245

theory 12–14, 36; grounded 178, 491–5; theory Wilcoxon test 527, 554, 587, 592 generation 185–6, 491–5

writing research reports see reporting

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