Research methods for education peter newby, routledge, 2014 scan

Praise for the first edition: Peter Newby is an affable and welcoming guide, but don’t let that fool you; his introduction represents the crystallization of careful, sophisticated practical, technical, and conceptual thinking, combined with a sure-footedness in and around the folk-ways of educational research This is a book with a considerable span of interests, permeated with a strong sense of why such work, and a commitment to doing it well, really matters That Newby has done all this without compromising the complexities and challenges that make all this work important is an achievement that makes this an especially useful and enjoyable book for beginning and experienced researchers alike Peter Freebody, The University of Sydney Crafting an appropriate and effective research design is a challenging task for many students, novice and experienced researchers Users of this comprehensive text will find it very helpful in designing suitable research tools, seeking consistency in theoretical underpinnings and making critical research decisions The text is intelligently grounded – it provides useful insight into reallife research situations and examples It is a very accessible text, easy to read and navigate through I would have no hesitation in recommending it to students embarking on educational research and to lecturers about to teach a course in research methodology Marc Shäfer, Rhodes University, South Africa An excellent text for students studying at all levels from undergraduate to doctoral qualifications The structure of the book leads the reader through the complete research process, highlighting the many ambiguities and challenges faced during the research Clear language makes the text accessible and helps to clarify some of the more difficult issues without minimising their complexity This book will be a great asset to many first time as well as experienced researchers There are few things more important than good research into education, and in this book Peter Newby makes sure his readers can meet this challenge He is a reliable, thorough and confident guide for anyone setting out on their research journey The text is particularly helpful for researchers developing action or policy in this field James Wisdom, Visiting Professor of Educational Development, Middlesex University Sheine Peart, Nottingham Trent University This is an excellent, up-to-date and accessible methods text which will greatly appeal to students grappling with the research process The style of the book is clear and user-friendly, whilst the content anticipates many of the problems which students are likely to encounter during their research in education Comprehensive and good value for money Samantha Punch, University of Stirling A refreshing approach to basic research issues, in a comprehensive research text that should stand the test with students who find some issues difficult to grasp Its combination of theory and practical illustrations guides the reader through all aspects of the research process, the management of quantitative methodology and analysis a particular strength The relaxed style of writing and presentation, and online features, will be appreciated by staff and students alike Molly Cumming, University of Strathclyde (retd) One of the most thorough and comprehensive research texts available The author offers a thorough presentation of all aspects of the research process, draws on a wide range of real examples from practice and offers particular support to those students who might struggle when presenting quantitative data in their research process Liz Keeley-Browne, Oxford Brookes University Combines comprehensive detailed coverage with accessibility and practical guidance This will become a core text for many students of educational research A serious and important attempt to simplify the complex process of research, without restricting or overly classifying the range and power of techniques available to us Steve Strand, University of Warwick Stephen Gorard, The University of Birmingham I am impressed by Newby’s concrete and structured way of guiding the student through the entire research process His descriptions of complex theories and procedures is conveyed in an interesting and accessible way Students will also enjoy the writing style and pedagogical organization of the book Carina Rưnnqvist, Umể School of Education, Sweden Peter Newby provides a lucid and accessible guide to research methods for education His approach, which sees such methods as a means to an end, is a much needed reminder that the main aim of research is to answer difficult questions and to break new theoretical and empirical ground Richard Andrews, Institute of Education, University of London Research Methods for Education Research Methods for Education, Second Edition takes the student by the hand and guides them through the complex subject of research methods in an engaging, witty and clear way The book covers the philosophical approaches and epistemology, as well as the practical aspects of research, such as designing questionnaires and presenting conclusions Each chapter is split into ‘Context’ and ‘Practice’ and both sections are packed with exercises, examples and comparative international material from other educational contexts Peter Newby’s book is the student-friendly text which demystifies the research process with clarity and verve Key features: t written in a clear and friendly manner to help students feel more confident dealing with the complexities of research and particularly useful for those new to research or less confident with numbers t a mixed methods approach, which doesn’t simply prioritise quantitative or qualitative methods, allowing for greatest possible coverage t contains guidance on analytic procedures that require more advanced tools such as SPSS and Minitab t many excellent international examples and case studies specifically from education, which break away from a parochial focus on UK education system Additional support such as activities, multiple choice questions, data-sets, examples of good and bad research tools and help with mathematics is available on the website www.routledge.com/cw/newby Peter Newby headed up educational development at Middlesex University for ten years After this he set up an education research and development centre where the focus of the work was the exploration of learning processes and frameworks that could deliver prosperity and greater social equality to communities Peter is now Emeritus Professor of Higher Education at Middlesex University This page intentionally left bank Research Methods for Education SECOND EDITION Peter Newby Emeritus Professor of Higher Education, Middlesex University This edition published 2014 by Routledge Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 711 Third Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2014 Routledge The right of Peter Newby to be identified as author of this work has been asserted by him in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988 All rights reserved No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe First published by Pearson in 2010 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalog record for this book has been requested ISBN: 978-0-415-73585-8 (hbk) ISBN: 978-0-273-77510-2 (pbk) ISBN: 978-1-315-75876-3 (ebk) Typeset in 9.5/12 Giovanni Book I would like to dedicate this book first to Radka It is, I know, a small recompense for all the support you have given me I would, as well, like to offer it to Josephine, Matilda, Elspeth, Clara and Beatrice whose experience of education will lay the foundations to become the next generation of researchers This page intentionally left bank Contents List of figures List of tables List of case studies List of activities About the author Preface Acknowledgements Part The Context for Your Research Chapter Research: A messy business 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 xv xviii xxi xxiii xxv xxvi xxix Learning themes Introduction What you put first? Who is this text for? Why we educational research? Who are the educational researchers? What are the objectives of educational research? Some guidelines on research Finally, some things we ought to know about educational research before we start Is research a messy business? 5 6 10 14 19 Summary Further reading References 29 30 30 Chapter Understanding the research process Learning themes Introduction 24 29 33 33 33 GLOSSARY 669 Regression A line used to represent the trend in a data plot (see scattergraph) The line can be inserted by eye but is more usually computed so that the total of the squares of the distance between any data point and the line is minimised Representative In sampling, ensuring that the characteristics of the sample reflect those of the population from which it is drawn Research agenda This is the priority given to research problems and themes A university department could have its research agenda but those with the power to influence what research is done are those of funding bodies such as research councils, national governments and the EU Research ethics See ethics Research hypothesis The opposite of a null hypothesis and the issue that we are really investigating The research hypothesis can be non-directional (there is a difference between a and b) or directional (a is larger than b) Research issue The area of interest to the researcher The first task of a researcher is often to focus down the issue and isolate a research question or research questions Research methodology See methodology Research philosophy A term used to describe the principles governing research practice What this description fails to convey, however, is that there is not just one research philosophy, so while we can use the term in a portfolio sense, we have to talk about the different principles that drive research practice The study of these different sets of principles can also be referred to as research philosophy Research problem More general than a research question, a research problem is an issue that requires further focus before it can be investigated Research question A formulation of a research issue that isolates the specifics to be researched and to which an answer is required Research strategy The overall approach to obtaining and processing data that will answer the research question A research strategy is informed by whether a researcher chooses to work within a paradigm and the level of resources available Essentially a research strategy is a combination of methodology and method designed to meet the needs of a particular research situation Response rate In survey research, the proportion of those contacted who take part It is usually expressed as a percentage (of respondents) of the sample The response rate is important in establishing the quality of the survey and in identifying the possibility of bias Review journal a field A journal whose principal purpose is to summarise the latest developments in Running mean See moving average Sample A selection from a population (see probability and purposive sample) Sample frame The section or area of the population from which the sample will be drawn In many cases the population will be the sample frame (for instance, if we want to interview a sample of a year group in a school, the year group would be both the population and sample frame) In other cases the sample frame will be a sub-set of the population For example, if we were studying the problems faced by head teachers in schools that drew their pupils from socially deprived areas, it would be difficult to sample the whole population of head teachers in such schools in a country, so we could identify three areas with different levels of deprivation and sample the head teachers in these areas In this case, the three deprived areas constitute the sample frame 670 GLOSSARY Sample size The sample size is the number of people or bodies that participate in a survey Sample size is expressed both as a number and a proportion of the population The determination of sample size is important before a survey if data are to be processed to yield results that meet standards of statistical rigour Sampling fraction The proportion of the population accounted for by the sample (that is the sample as a fraction or percentage of the population) Scale Used in two ways in social research: (a) A framework for measurement (attitude scale) (b) a level at which data are presented and analysed This level can be geographical (national, local) or hierarchical (expenditure on food, leisure or expenditure on pre-pepared food, on fruit and vegetable, on meat, on fish, on cereals etc.) Scattergraph A way of portraying the interaction of two variables on a graph Each axis represents a variable Data points are plotted against the two axes Scatterplot See scattergraph Scientism Both an approach to research and a belief about how research should be conducted Scientism believes in the primacy of a scientific approach to research, with key principles being the neutrality of the researcher, the demonstration of the effect of an individual variable or set of variables upon a situation and the development of a model to reflect a particular circumstance or a theory to describe a general relationship Secondary data Data used by the researcher or the research team but collected originally by another researcher Self-selecting sample A sampling procedure in which a population identifies the survey opportunity and takes part Semantic differential A rating scale based on adjectival opposites that can be used to profile respondent views about an issue Semi-structured interview An interview in which themes are identified and lead questions specified but where the interviewer is given training to ask supplementary questions that will provide the data needed by the research programme Semiotics An approach to qualitative research that seeks to identify the deeper meanings behind behaviour and the way text and objects are used Sign The fundamental unit of semiotics A sign stands for something What it stands for is socially and/or culturally determined What it stands for (its meaning) can be analysed (see connotation) Significance level This is the level of probability at which a researcher chooses to reject the null hypothesis The most usual value selected is 5% but in some circumstances significance in social or educational research terms could be less than this (for instance 10 or 20%) Skew A pattern in data in which the data are not equally divided and balanced either side of a central value (usually the mean) When plotted as a frequency distribution, the appearance of the data set is that it leans to the left or to the right Smoothed mean See moving average Snowball sampling Developed as a purposive sampling approach and often used for hard to reach populations The researcher uses subjects already identified to identify new subjects, the assumption being that in hard to identify groups those already in the group are likely to know other group members Soft system Whereas a system normally requires a quantitative relationship between inputs and outputs, a soft system is an expression of the relationship between broader variables for which there may not be a simple quantitative measure For instance, if we were researching children’s progress in pre-school, we might suggest that a contributory factor was the amount GLOSSARY 671 of time parents spent reading with them Further investigation suggests that socio-economic status might be a factor that influences this As well, we ought to consider whether one or both parents worked was something to take into consideration We are exploring a process but, at this stage, in a non-quantitative way Special educational needs Conventionally this refers to the additional or special educational or care provision required by students with specific physical or learning disabilities Theoretically it could also apply to those who are gifted in a general or specific context but it is now usual to describe these as ‘gifted and talented’ Spectral analysis A quantitative approach to the analysis of time series in which data plotted through time is decomposed into a series of sine waves of different amplitude and wavelength When these curves are combined, they reproduce the original data set The task of the researcher is to identify the significance of the individual curves in terms of processes at work Standard deviation A measure of spread in a distribution based on the total of differences between every element in the distribution and the mean of the distribution Standard error The idea that a characteristic of a sample reflects the characteristic of a population within known limits The standard error is usually calculated for the mean of a sample It is expressed as the sample mean plus or minus a value determined through calculation with a 68% (approximately) or 95% (approximately) likelihood Star plot A way of portraying the sizes of a number of variables (usually more than five) The variables are plotted in a circle on radii that are at equal angles from each other The value of the variable is shown by the length of the radius Statistical significance A way of assessing the likelihood that the difference between two distributions could be random in nature The conventional critical level of significance adopted by statisticians is that an event occurring one time in 20 (5%) indicates that chance is unlikely to be a factor However, for some investigations this level may be too strict and researchers should judge significance taking other factors into account Stem and leaf diagram A means of visually representing the digits of a numeric data set Numbers are broken down into component parts, such as tens and units In this case the tens constitute the stem and are written as a column of figures from high to low or vice versa The units constitute the leaf These are written against the appropriate stem, thus the of 52 is place against 50 and the of 55 against 50 Stepwise correlation An approach to estimating the relationship between variables in which variables are introduced in turn into the correlation on the basis of their contribution to the value of the correlation coefficient The correlation coefficient is interpreted as normal (see correlation coefficient) Stratification A procedure used in sampling that divides the population into groups (strata) and allocates the sample size according to the relative sizes of the strata The effect of this is to reduce the sample size because we are sampling units of the population that are more homogeneous than the population overall Structuralism A broad class of philosophical standpoints in social science that interprets situations in the context of power relationships, in particular the persistence of strong and weak, rich and poor Structured interview An interview in which the themes to be covered are identified A questionnaire interview is structured but there is no necessity to use a questionnaire approach All that is required is that the themes are identified and covered in sequence (see unstructured interview) Structured question See closed question Student’s t test A statistical test for assessing whether two small samples are drawn from the same population 672 GLOSSARY Summative assessment Used to describe the position a student has reached in terms of knowledge, understanding and learning at a point in time (see also formative assessment) Survey A systematic collection of data through questionnaire, interview or observation A systematic procedure is necessary in order to ensure that a representative set of data are collected Synchronous Something that happens at the same time as something else Classroom teaching and student interaction, for instance, are synchronous activities Synthetic phonics A method of teaching reading in which the reader is taught the ‘sounds’ of letters and letter combinations, then break up the word into these sounds and assembles them to make the word For example, SH – I – P to make ship System A sequence of inputs and outputs that constitute a process We can think of the number of undergraduates in a university academic year as a system in which the inputs were the number entering the year one plus any that were required to retake the year less those who failed the previous year and were required to leave Systematic sampling A sampling approach in which a sample is drawn as a sequence according to a given sampling fraction For example, if the sampling fraction is in 50, one respondent is drawn out of every 50 If the first respondent is the third in sequence, the second is the fiftythird, the third is the one hundred and third and so on t test See student’s t test Tagging In grounded theory, the process of attaching a code to a piece of data (thereby effectively converting data to evidence) Template analysis Analysis of data according to a pre-existing framework This framework can be a standard template, a template devised for another piece of research (which we might use to test previous results) or one devised to assess a theory or model Test statistic All statistical tests compute a test statistic, which is assessed against its probability of occurrence under a null hypothesis The test statistic is the outcome of the numerical processes required by the test itself Theory A statement about functional and process relationships that create cause and effect Theories can be derived as a result of data analysis or prior to analysis and tested through data collection and analysis Think aloud interview An interview approach in which a subject is asked to perform a task and, at the same time, describes the thinking process that underpins his or her actions Time series A line graph that shows how a variable (such as birth rate or students in higher education) changes over time Tree map A tree map is a way of representing data with a hierarchical structure in two dimensions The computer programs that produce the visualisation are usually interactive and allow researchers to manipulate data Triangulation A process of verifying data Data given by one source is confirmed by another and preferably a third The sources can be people, documentation, statistics, reports and so on Two-tailed test The tail refers to a test distribution In a two-tailed test, we examine the probabilities associated with test result in both tails of the distribution This requires us to have defined a non-directional positive or research hypotheses (that there is a difference) Type error The incorrect rejection of a null hypothesis, that is when there is no difference between our samples, we conclude that there is Type error The failure to reject the null hypothesis when it should be rejected, that is when data sets are different, we conclude that they are not GLOSSARY 673 Univariate This means one variable It is usually used in the context of a distribution A univariate distribution is one where one variable (such as age) is plotted in terms of frequency Unstructured interview A free form interview Themes will have been identified by the researcher but there is freedom to follow up on new themes, to return to themes as new information is gathered and to probe for additional information Unstructured interviews are particularly useful for scoping an issue before more detailed investigation is carried out They are used extensively in policy analysis (see structured interview) Unstructured question See open question Variable A factor that can influence an outcome or be influenced by an outcome The concept of variable is important in modelling research situations Variance The square of the standard deviation Z score/value Z scores are values for a unit of data standardised in relation to the data sets mean and standard deviation Z scores associated with a research data set can be compared with scores for a normal distribution, where the proportions of a data set lying at specific distances from the mean are known These known proportions are presented as a Z table and can also be computed A Z table shows the proportion of a normal distribution that lies between the mean and a Z value, between Z values and below and above Z values Index Note: Page numbers in bold are for figures, those in italics are for tables Aalsvoort, G 501 academic culture 17 academic researchers 7, 8, 30 academic writing 219 acceptability 19 accuracy 291–2, 293 Acorn lifestyle classification 150–1 action plans 23, 64 action research 45, 63–6, 67, 71, 103, 137, 368, 630–1; key features 631; uses of 64–6 activity-based stimuli 345–6 Adult Literacy and Life Skills Survey (ALL) age-sex pyramids 426–9, 430, 431, 432 aggregation of data 163–5, 167 Ahlin, Asa 592 alcohol consumption 495 American Educational Research Association (AERA) 52, 110, 208, 210–11, 584 American Evaluation Association 110 American Psychological Association 584 An, Wayne 486–7 analysis of variance (ANOVA) 601–7; one-way 602–3; two-way 604–7 analytic information 304 Anderson, Edgar 449 Anderson, M 513 Anghileri, J 522 anonymity 52, 318–19, 386 anti-social behaviour 349, 626–7 AQUAD 481, 483 archives see data archives Ariely, D 51 Arnon, Sara 108–9 Artinian, Barbara 496 Askehave, Inge 510 assessment practices 41 association 574–5 attention 98 attitude scaling: methods 321–8; principles 306–9 audio recordings 367; analysis of 465 Audit Commission 58 auditing: research literature 87, 194; resources 20–1, 231 Australian Education Index 15 autocoding 480 average see mean Avramidis, Elias 327 axial coding 498 Babad, Elisha 347 Babbage, Charles 499 Bailey, John 465 Balen, Rachel 50 Ball, Stephen 641 Ballinger, Gillian 353–4 Baltar, F 255 bar graphs (bar charts) 425–9, 451, 454 Bayes, Thomas 75 Bayliss, Phil 327 Beauchamp, C 346 behaviour, as source of data 155, 458 belief networks 75 Bell, Alice 364–5 bell curve 542, 568 Belohlav, James 46 benchmarking 58 benefiting from education research 646–8 Benson, Tammy 501–2 Benwell, Bethan 507 Bergendahl, Christina 630, 631 Berkowitz, E 364 Berks, Melanie 496 Bessel’s correction 542 Bham, M S C 76 bias 161; citation 198; in observation 387–8, 389; response, in questionnaires 292, 320–1; sampling 116, 265 bibliographic search engines 207–8 Billingsley, Bonnie 498 bimodal distribution 533, 567 bivariate data 436, 439, 454 `blind’ experiments 127 Boccacini, Marcus 613–15 Bolhuis, Sannelee 374–5, 376 Bologna process 600 Bonawitz, Elizabeth 75 Boolean searches 212, 480 boundaries, research 88 Bourdon, Sylvain 468 Bowen, Glenn 496 Bowker, Rob 627–8 Bradburn, Norman 314 British Education Index 15, 207 British Educational Research Association (BERA) 51, 52, 209, 210 British Educational Research Journal (BERI) 210, 216 British Sociological Association 52 Brooker, Barry 493 Brouwers, Andre 76, 597–8 Brunet, I 255 bullying 475–6, 524 Burden, Robert 327 Burgess, Ernest 38 INDEX Burke, C 343 burnout 76, 597–8 Byrd-Bredbenner, Carol 490 Byrne, B M 76 CACI 150, 151 Cameron, Claire 351 capitalism 43–4 case studies 45, 53–6, 66, 67, 69, 103, 105, 139, 626; exploration, explanation and description 54–5; key features 625; problems associated with 56; as research strategy 624–5; as samples 257–8, 259, 260, 261 categorical data 533 Cater, J 342 Catterall, Miriam 343, 344 causal analysis 70, 71 causal knowledge 75 censuses 160 Centers for Disease Control and Prevention (USA) 488 central tendency see mean; median; mode CESSDA (Council of European Social Science Data Archives) 69, 161 Chabris, Christopher 98 Chan, Kara 346 Chang, Lin Chai 150 Charmaz, Kathy 493–4 Chatterji, Madhabi 109, 110 Cheeseman, Jill 344–5 Chen, X 522 Chernoff faces 447–8, 451, 454 Cheung, Maria 344 chi-square test 593–8 Chicago School 348 children 50, 52; cognitive/behavioural development 75, 193, 349; ethical issues concerning 370–1, 372; gifted 364; interviewing 368–9, 372, 373; language patterns 501–2; obtaining data from 373; questionnaire research with 364–5; selfpresentation 375–6 Children Act (2004) 13, 277 Cichelli, T 364 citation bias 198 Clark, Burton 257–8 Clarke, Barbara 344–5 Clifton, Jonathan 507–8 closed questions 300, 301–2, 303, 304, 357, 384 closed systems 275 cluster sampling 246–7, 251 coding qualitative data 376–7, 381, 383–5, 466–77, 503; autocoding 480; creating links and themes 473–5; downside of 469; grounded theory 491–7, 498; making it work 469–73; observations 376–7, 381, 383–5; teamwork 476–7; technology and 479–83 coefficient of determination 559, 611 coefficient of variation 544–5 cognitive development 75, 193 cognitive interviews 362–5 Cohen’s d 583 cohort studies 55 Commission on Social Sciences 109 commissioning culture 17 comparative studies 166–70; international 317 completeness 18 complex data sets 444–50 compliance, evaluation to ensure 57–8 compound indicators 282–4 compressing data 413–15 computer assisted research: data coding and analysis 477–83; early stage thinking 477–9; telephone interviewing (CATI) 342; transcription 477; see also Internet Comte, Auguste 98, 99 concept mapping 468 concept searching 212 Concordance software 478 confidence intervals 262, 263, 608–9 confidence levels 262, 263, 617 confidence limits 608–9 confidentiality 52, 319, 371, 373 confirmability 129 conflict oriented evaluation 60 connotation 511–12 consent 50, 52, 369–71, 385–6 constant comparison 494–6 constructivism 483 content analysis 484, 488–91 contractor culture 17 control groups 122, 123, 127 control variables 284–5 convenience sampling 257, 259, 260, 261 conventional content analysis 489 conversation analysis 484, 507–8, 646 convincing others 16–17 Cook, Anthony 322 Corbin, Juliet 497–8 corporate data 161 correlation analysis 441 correlation coefficients 119, 557–60, 607–9; multiple correlation 611–12; partial correlation 609–11, 612; 675 point-biserial 613–14; stepwise correlation 612–13 Corrie, D 513 corruption 147 counted data 533 covert participation 353 credibility 129, 459; of sources 227, 228–9 critical discourse analysis 484, 485, 508–11 critical pedagogy 368 critical theory 42–4, 49, 89, 218, 483 cumulative frequency 531 cumulative frequency graphs (ogives) 430–34, 451, 454, 532, 598 Cunningham, W G 76 Cupchik, Gerald 483 Dabney, D 387 Daniel, Larry 459, 460 DANS (Data Archiving and Networked Services), Netherlands 315–16 data 145–51; aggregation 163–5, 167; bivariate 436, 439, 449, 454; character of 146–7; class boundary problems 117–18; coding see coding qualitative data; dichotomous 613; excess 235; goodness in 289–93; and information distinction 225; making sense of 16, 645–6; measurement of 152–4, 289, 291–3; multivariate 445–50, 454; periodicity of 550–6; poor 116; preparing data for analysis 117–20; purpose of 147–8; saturation 472, 496; tagging 471–3; transforming 547–8; types of 148–51; units of 471; univariate 436, 449, 454; see also primary data; qualitative data; quantitative data; secondary data; statistical data data analysis 23, 24, 68, 163–5, 395; identifying variables through 284–6; see also qualitative data analysis; quantitative descriptive nalysis; statistical data; statistical testing data archives 68, 69, 161, 177 data collection 16, 23, 24, 88, 287–9; see also focus groups; interviews; observation; questionnaires data decomposition 553–4 data mining 449 data sets: complex 443–50; number of, and statistical testing 585–6 data visualisation see portraying data 676 INDEX databases 15, 40; accessing literature via 206–8, 209; review 209 Davies, Graham 368 Davis, Anita Ann 471 deciles 537 decision making, evaluation 57–60 deductive method 107, 483 deep learning 513 degrees of freedom 591, 595, 602 Delphi technique 256 Denmark 210, 371; Index of Social Progress 284; statistical data 177 denotation 511 density plots 443 Department for Business, Innovation and Skills (BIS) 12, 13 Department for Children, Schools and Families (DCSF) 12, 206, 210 Department for Communities and Local Government 176 Department for Education (DfE) 12, 13, 174–6, 209 dependability 129, 459 dependent variables 279, 585 deprivation, measurement of 282–3 description 69, 70; case study as means of 54–5 descriptive coding 470 descriptive statistics see quantitative descriptive analysis Desimone, Laura 364 determinism 157 development, and action research 65–6 Dewey, John 73, 497 dichotomous data 613 directed content analysis 489 disclosure 371 discourse analysis 484, 499–503, 646; critical 484, 485, 508–11 disengagement and observation 388–9 distributions: bimodal 533, 567; characteristics of 526–7; flat and peaked 546–7; leaning (skewed) 526, 545–9, 567; normal 542–4, 567, 568–9; sampling 543, 544 divided bar charts 454 Doyal, Len 102 Drury, John 389 du Toit, Stephanos 628–9 dual (pair) interviews 365–6 Durkheim, Emile 119 Dutton, Dennis 218 Dye, J 474 Early Intervention Foundation 12–13 EBSCO 207 ecological fallacy 119–20, 165 Economic and Social Data Service (ESDS) 177 Economic and Social Research Council (ESRC) Question Bank 315 Education Act (1944) 45 Education Funding Agency 13 Education Network Australia 207 Education Review 349 Educational Research 349 Educational Research and Review 285 effect size 459, 583–4, 617 Ejieh, Michael 598 electoral areas 167–70 elite interviews 370 Ells, Harvey 343 emancipatory learning 368 emotional behaviour and environmental context 157 Englund, T 39 environment, as source of data 156–7 epistemology 36, 97, 109 EPSEM (Equal Probability of Selection Method) see probability sampling ERIC 15, 40, 207 Escalate 377, 380–1 Estell, David 362 Estes, Richard 284 ethical issues 51–2, 90, 147, 195; assessing 89; competing interests 17; confidentiality 52, 319, 371, 373; consent 50, 52, 369–71, 385–6; falsification of data 49; interviews and focus groups 369–72; observation 385–7; plagiarism 51, 490; and research philosophy 35; see also misuse of research The Ethnograph 481, 482–3 ethnography 60–3, 66, 67, 68–9, 103, 139, 348; key features of 630; phrasing the research question 628–9 European Educational Research Journal 211 European Foundation for Quality Management (EFQM) 65 Eurostat 186, 317 Eurydice 184–6 evaluation 56–60, 66, 67, 70–1, 139, 626–8; impact 626; key features of 628; phrasing the research question 626; process 59, 626 Evans, C 584 Every Child Matters initiative 277, 404–5 Evidence for Policy and Practice Information and Coordinating Centre 209, 210 existentialism 40, 41, 42, 89 experimental approaches 67, 121–8, 138–9; design strategies 122–7; key features 634; phrasing the research question 631–2; problems with 127–8 experimental groups 122, 123, 127 explanation, goodness in 289–91 explanatory theory 73–4 explanatory variables 279 eyeballing see reading a data set F ratio 602–3 Facial Action Coding system 469 factorial experimental design 125, 126 factual coding 470 falsification of data 49 Family Spending 249 Fan, Z.-Z 599 Faraday, Michael 37 Fast Track project 384 feminist research 360, 361 Feng, Shoudong 501–2 Ferdinand, Jason 387 Fereday, Jennifer 487–8 film 344–5 Finland 210, 371; cluster sampling in health education 248; statistical data Finlay, Linda 129 Finnish Adolescent Health and Lifestyle Survey 248 Finnish Social Science Data Archive 316 Fisher, Ronald 577, 589, 601 Fisher’s test 600–1 Fitz, John 258 Flanders Interaction Category framework 384–5 focus groups 103, 255, 289, 339, 340, 365, 366–8, 372; ethical issues 369–72; and naturalistic enquiry 105; visual stimuli in 344, 367 Foorman, Barbara 199–201 Fore, C 76 foresight planning 256 formative evaluations 59 France 55, 371 Fraser, Heather 505–6 Freire, Paulo 368 frequency diagrams see bar graphs Friborg, Oddgeir 328 Friedman, Stephen 197 Frisby, W 361 Futurelab 209, 210 Gallardo, David 448 Gallivan, Michael 135 INDEX gambling 320 Gantt chart 231, 232 gateways (portals) 206 Geake, John 328 gender gap in attainment 290 geostatistics 570 Germany 55 Gibbons, N 584 Giddings, Lynne 111 gifted children 364 Gillies, David 510 Gino, F 51 Giske, Tove 496 Glaser, Barney 491, 492, 498 glyphs 449, 454 goal oriented evaluation 59 goals: influence of education theory on 72–6; theory testing as a goal 74–5 Goddard, R 76 Godfrey, Celia 470 Google Books 15 Google Scholar 15, 207, 213 Gorard, Stephen 109, 110, 134, 637–9 Gossett, William 589 government departments 12, 13, 25, 174–7, 209 graduation rates 611–12 Grant, Rose 41 Greatorix, Jackie 363 Greaves, Nigel 43 Gredler, M E 209 Grey, Donald 109 Grey, P 277 Grobler, Ilze 505 Gross, Miraca 328 grounded theory 105, 107, 484, 491–9, 503, 646 group cultures 349 group interviews 288, 289, 366, 373 grouped data: calculating standard deviation for 540–2; estimating spread using 538–9 guidelines on research 19–24 Hagelin, Joakim 313 Halliday, Michael 502 Harris, Lois Ruth 513–14 Harris, Roger 102 Hastings, R P 76 Hawthorne effect 127, 130, 387 head lice studies 524–5 health education research, sampling in 248, 259 Heckathorn, Douglas 255 Heinonen, Tuula 344 Heise, David R 325 Higgins, Steve 263 high stakes testing 486–7 Higher Education Research Council for England 19 Higher Education Statistics Agency (HESA) 176, 398, 400, 523 Highet, Gill 365–6 Hill, Dave 43 Hinchcliffe, V 342 histograms 425–9, 451, 454 Hockey, Jenny 357 holistic approach 104–5 Hollway, W 356 Home Office 176, 368 Homes, Amy 365 honesty 49–50, 52 Honey, Peter 74–5 Hopperstad, Marit Holm 512–13 Horkheimer, Max 43 Hossain, D 547 Howley, C and Howley, A 548 Hsieh, Hsiu-Fang 489 Hughes, Martin 641 Huisman, Arnold 316 human capital theory 74 Human Development Index 284 humanism 37–9, 40, 49 hypotheses: directional 113; nondirectional 113; and qualitative research 107, 108; and quantitative research 37, 101–2, 107, 108, 112–15, 121; testing 634–7; see also null hypotheses; research hypotheses Ibbotson, Patrick 343, 344 ideas 304; as primary data 157–8; research 640–2 ignorance of relevant research 196–7 imaged-based techniques see visual images immigration 634–5, 636–7 impact evaluation 626 improving practice 10, 11, 15; and action research 65; evaluation as means of 58 in-depth interviews 359–62 independent variables 279, 285, 585, 587; and hypothesis testing 637; multiple correlation 611; partial correlation 609, 610 Index of Child Well-Being 284 index numbers 419 Index of Social Progress 284 indicators: quantitative, in qualitative research 489–90; of variables 280–4, 294 inductive method 107, 289, 483, 492 inequality 43, 431 information: categories of 302–5; and data distinction 225; overload 523; right sources of 227–30 677 INGENTA 207 Ingersoll, R M 197 innovation diffusion 77 INSIDE 207 integrative approach 104–5 integrity 131 intellectual enquiry 11 intellectual imperialism 50 intellectual property 50 interaction effect 292 International Adult Literacy Survey International Association for the Evaluation of Educational Achievement (IEA) International Bureau of Education (IBE) 183 International Centre for Distance Learning 207 international comparative studies 317 international data sources 182–7 International Standard Classification of Education (ISCED) 37 Internet 15, 51; data sources 159, 172–87; interviews 342; sample size calculators 261; sampling using 255, 258–9; search terms 211–12; web portals (gateways) 206 interpersonal process coding 467–8 interpretive analysis 463 interpretive coding 470–1 interquartile range 537–8, 539 interval scales 153–4, 308, 533, 586 intervening variables 279–80 intervention effect 127, 130 interviews 103, 105, 287, 288–9, 339, 340–6, 372; activity-based stimuli in 345–6; with children 368–9, 372, 373; cognitive 362–5; collective 365–8, see also focus groups; group interviews; computer-based 342; elite 370; and empowerment 361; ethical issues 369–72; face to face 342; in-depth 359–62; interview guides 356, 357–8; narrative 356; narrative analysis 504; pair (dual) 365–6; schedules 341; semi-structured 356–9; structured 341–2; telephone 342; template analysis 487–8; think aloud 363–5; visual imagery techniques in 343–5 iPOLL databank 315 Ireson, Judith 281 Ivanic, Roz 503 Iwanick, E F 76 Jacobson, Jessica 368 Jacobson, Leonore 347 678 INDEX Janesick, V 374 Jasinskaja-Lahti, Inga 634 Jefferson, Gail 507 Jefferson, T 356 Jenkins, J 325 Johnson, Burke 110 Jones, Sonia 345 Journal of Educational and Behavioural Statistics 211 journals 15, 196, 349; key 209–11; review 196–7, 208–9, 210 JSTOR 207 Kalamanou, D 597 Kelle, Udo 482–3, 498 King, Ronald 355 Kirby, Amy 368 Kirkpatrick, Marion 349 Klaasen, Cees 256 Kloep, M 248 knowledge 227; causal 75; see also epistemology Knowledge Engineering Web 624, 625 knowledge information 303 Kolmogorov-Smirnov test 598–600, 656 Konapasek, Zdenek 483 Kondratiev waves 555 Krasny, K A 209 Krippendorf, KLaus 490 Kristen, C 55 Kroeger, Stephen 325 Krosnick, Jon 150 Kuhn, Thomas 38, 46 kurtosis 546–8, 567 Kyronlampi-Kylmanen, T 368 Labov, William 504 Lake, Jeannie 498 Land, Kenneth 419 latelearners 41 latent analysis 463 Latvia, cluster sampling in health education 248 Le Floch, Kerstin 364 Leach, Fiona 345–6 leadership, theory of 277–8 leaning distributions (skew) 526, 545–9, 567 learning 513–14; deep and surface 513; emancipatory 368; problem-based 375, 384 learning disabled 330, 372, 373 learning theory 74 least squares method 557 lecturers 7, 8, 11 Lee, V E 549 Legendre, Alain 157 Lehmann, Erich 586 Leichtentritt, Ronit 51 leptokurtic distribution 546 lesson observation 376–85 lesson observation schedule 382–3 Lewin, Kurt 63 Lewins, Ann 479–80 Lewis, A 372 Liebkind, Karmela 634 lifestyle questionnaires 305 lifeworlds 39, 484 Likert scales 321–4, 326, 327, 328 Lillemyr, Ole 193 Limlingan, Maria Cristina 193 line graphs 437–9, 451 Lintonen, T P 248 Lipton, Peter 291 literature reviews 16, 28, 194, 215–22 literature searches 202–14; author and citation 212–13; framework 203; identifying sources 205–11; mapping the issue 202–5; posing questions 205; reviewing outcomes 213–14; search options 211–12 Lloyd, Gwendolyn 504 local authorities 12–13 Local Safeguarding Children Boards 13 Locke, John 74 longitudinal studies 55, 125, 137, 177, 264 Los Angeles Community Analysis Bureau 445 Lowe, Houston 322 Maatta, K 368 McCall Smith, Alexander 40, 42 Macdonald, Nancy 44 McDonald, Seonaidh 354–5 McGowan, Brian 82 McKay, Brendan 467 McLean, Alan 108 MacQueen, K 476 McVee, M B 209 McWilliam, Angus 109 Magenta Book (UK Treasury) 177 Maisuria, Alpesh 43 Malematsa, M M 475 Malinowski, Bronislaw 348 Mallik, Maggie 82 manifest analysis 463 map of educational research 10 Marks, Gary 278 Marshall, Helen 469 Martin, Elizabeth 311, 313 Marton, Ference 513 Marx, Karl 43 Mason, Jennifer 133 Mass Observation 348 matched groups 125 matched samples 592–3 mathematical modelling 77–8 Matheson, Jennifer 477 matrix charts 448–9, 451, 454 MAXqda 481, 483 Mayston, David 286 Meadows, Pamela 524 Meak, Angela 357 mean 526, 528–30, 533, 539, 544, 546; difference between two means 589–93; see also standard error of the mean measurement of data 152–4, 289; goodness in 289; problems in 291–3 measurement scales 646; see also interval scales; nominal scales; ordinal scales median 526, 530–3, 539, 545–6 Meeus, Wil 631–4 Meijher, Joost 632 memoing 492, 496 Merrell, Kenneth 383 meta analysis 486 metaphor analysis 346 metaphor mapping 454 methodology 20, 47, 53–67, 195 methods 194–5, 644–5; and methodology, distinction between 53 metroglyphs 449, 454 Miles, Matthew 480 Millward, A 471 Minitab 588 misuse of research 195–201; ignoring relevant work 196–7; misrepresention of findings 198–201; misunderstanding research 197–8 mixed method design 132–3 mixed methods research 47, 48–9, 67, 97, 132–9, 138, 139, 140; character of 108–11; and positivism 111; problems with 135–7; triangulation 134; value of 133–5 mixed model design 133 mode 526, 533–6 models 89–90; influence on research outcomes 76–8; systems 274–8 modernism 44, 45 Mohr, G 445 Montessori, Maria 73 Morgado, Marcia 463, 513 Mortimore, Peter 19 Mosaic classification 151 moving average 551–3 Mueller-Benedict, Volker 555 Muir-Cochrane, Eimear 487–8 Mullis, I multi-level analysis 285, 286 INDEX multi-level design 133 multi-method approaches 72 multi-phase sampling 249–50 multi-stage sampling 247–9, 250, 251 multi-variable analysis 286 multiple correlation 611–12 multivariate data sets 444–50, 454 Mumford, Alan 74–5 Mungazi, Dickson 74 Murray, Lorraine 365 Myberg, Eva 615 Myrich, F 497 narrative analysis 484, 503–7 narrative interviews 356 National Bureau for Economic Research (USA) 147 National Centre for Education Statistics (USA) 180, 181 National Centre for Social Research 459, 460 National College for School Leadership 13 natural settings 350 naturalistic enquiry 105 neo-conservatism 43 Netherlands: benchmarking 58; DANS (Data Archiving and Networked Services) 315–16; education research 55; education system 13–14; Index of Social Progress 284; journals 210; Life Situation Index 284; quality management 65; statistical data 178–9 neutrality 27–8 Neville, S 522 Newman, Mark 384 No Child Left Behind (USA) 277 noise 163 nominal scales 154, 586 non-parametric tests 586 non-probability sampling 238, 252–61, 266; case studies 257–8, 259, 260, 261; convenience sampling 257, 259, 260, 261; quota sampling 252–4, 259, 260, 261; selfselecting samples 258–9, 259–60, 261; snowball sampling 254–5, 259, 260; specialist group sampling 255–7, 259, 260, 261 non-response 116, 160, 264 non-verbal communication 347, 367 normal distribution 542–4, 567, 568–70 normal distribution function 569 normative models 77 normative theory 73 Norway 210; action research 65–6; cluster sampling in health education 248 NSPCC (National Society for the Prevention of Cruelty to Children) 209, 210 null hypotheses 102, 108, 113, 114–15, 120, 574; and statistical testing 576–82, 617; Type I error 580, 581; Type II error 581 objectives: of educational research 14–19; evaluation of 59 objectivity 129, 131 objects, as source of data 155–6, 458 observation 288, 289, 339, 347–55, 374–89; bias in 387–8, 389; characteristics of approach 350–1; close 351–2; coding data 376–7, 381, 383–5; data analysis 465; disengagement issues 388–9; ethical issues 385–7; lesson 376–85; nonparticipant 352, 353–5; participant 62, 352–3, 386; remote 352 observatories 83–4 O’Farrell, Clare 45 Ofqual (Office of the Qualifications and Examinations Regulator) 13 Ofsted (Office for Standards in Education) 12, 13, 106, 209, 210; lesson observation criteria 376, 378–80 ogives see cumulative frequency graphs Ohi, Sarah 502 one-tailed test 578, 579, 580–2 Ong, Anthony 38–19 ontology 35–6 Onwegbuzie, Anthony 110, 459, 460 open questions 300–2, 303, 304, 356, 384 open (substantive) coding 492–3, 495, 498 open systems 275 OpenStat 589 opinion polls 254 optical character readers (OCRs) 330 ordinal data 153 ordinal scales 154, 308, 533, 586 Organisation for Economic Cooperation and Development (OECD) 9, 184–7, 317; Programme for International Student Assessment (PISA) 184, 336, 405, 422–4 organisational change, action research and 64–5 679 organisational management and practice, evaluation as means to improve 58 Ormerod, Fiona 503 Oughton, Helen 510 outcomes 65; evaluation of 59; models’ influence on 76–8; theory development as an outcome 75–6 outliers 441, 537 pair (dual) interviews 365–6 paired comparisons 124–5 paired samples 592–3 Papatheodorou, Theodora 248–9 paradigms 46–9 parametric tests 586 partial correlation 609–11, 612 participant observation 62, 352–3, 386 participants 50, 51; anonymity 52, 318–19, 386; children as see children; consent 50, 52, 369–71, 385–6; withdrawal 50, 52 pattern searching 212 peakedness 526 Pearson coefficient 559 Pearson, Karl 577, 589, 593 Peirce, Charles 48 Peled, Einat 51 Pellico, L 374 perception 98 periodicity of data 550–6 Persson, Gun 353 Peterson, D 604 Petter, Stacie 135 Pettigrew, Simon 495 phenomenography 484, 513–14 phenomenology 39, 41, 42, 89; social 487 Phillips, Linda 325 philosophy 35–46, 89 phonics 9, 10, 199–201 Physical Quality of Life Index 284 Piaget, Jean 75, 208, 349 pictures see visual images pie charts 435–6, 451 places, as source of data 156–7 plagiarism 51, 490 planning research 16, 24, 230–2 platykurtic distribution 546 point-biserial correlation 613–14 polar diagrams 444 Pole, Christopher 375 policy 219, 640; education policy in England 9, 12; evaluation as tool for formulation of 58; shaping and testing 9, 11–12, 13, 29, 30, 70, 71 680 INDEX Polite, Vernon 354 politics 35, 70, 105–6; and critical theory 42–3; and education research 25–6, 30, 87 Popper, Karl 37, 102, 635 population pyramids 428–9, 430, 431, 432 portals (gateways) 206 Porter, J 372 portraying data 421–53, 454, 646; bar graphs 425–9, 449, 451, 454; Chernoff faces 445, 447–8, 451, 454; complex data sets 443–50; concept maps 451; cumulative frequency graphs 430–34, 451, 454; glyphs 449, 454; histograms 425–9, 451, 454; line graphs 437–9, 451; matrix charts 448–9, 451; metaphor mapping 445–8, 454; pie charts 434–7, 451; scatterplots 440–44, 451; star plots 443–5, 446, 449, 451, 454; stem and leaf diagrams 422–4, 451, 454; tree maps 449–450, 451, 452, 454 positivism 37, 38, 39, 43, 49, 89, 98–9, 111, 121, 237, 635 postgraduate students postmodernism 44–5, 53, 89 Power, Robert 352 practice oriented research 10, 11, 15, 29, 30, 350 pragmatism 48–9, 67 praxis 11 precedence 153 precision 292–3 predictive texts 466–7 presentation 51, 218 prestige effect 321 primary data 225, 232; sources of 154–8 principles 49–52 privacy 386 probabilities 114; and standard deviation 539, 543; and statistical testing 115, 567, 572–3, 575–7, 578, 608, 617 probability sampling 237–8, 239–51, 266; cluster sampling 246–7, 251; multi-phase sampling 249–50; multi-stage sampling 247–9, 250, 251; simple random sampling 239–42, 251, 261; stratification 244–6, 247, 248, 251; systematic sampling 243–4, 251 probability value 576 problem-based learning 375, 384 process evaluation 59, 626 professional researchers 7, 8, 17 Programme for International Student Assessment (PISA) 184, 336, 405, 422–4 Progress in International Reading Literacy Study (PIRLS) projective techniques 343–4, 345 proof: and qualitative research 108; and quantitative research 100–3, 108, 114–15 Prosser, J 343 proxy research 147 Prskalo, Ivan 600 psychology 74, 75, 98, 109, 343, 348–9 publication 50 Punch, Samantha 373 punctuation 466 purpose of research 85–6 purposive sampling see nonprobability sampling qualitative data 148; measurement of 3, 152, 154; types of 149–51 qualitative data analysis 457, 463–519, 646; coding see coding qualitative data; computer assisted 477–83; content analysis 484, 488–91; conversation analysis 484, 507–8; critical discourse analysis 484, 485, 508–11; discourse analysis 484, 499–503; grounded theory 105, 107, 484, 491–9, 503; interpretation of data 464, 470–1; narrative analysis 484, 503–7; organising data 464; overview 463–77; phenomenography 484, 513–14; preparing the data 463, 464–6; semiotics 484, 491, 511–13; template analysis 484, 485–8, 492 qualitative research 38, 47, 48, 49, 89, 96–7, 128–32, 139, 140, 285; character of 103–8; complexity of 458–9; hypotheses 107, 108; and nature of proof 108; objectivity and integrity 131; quality in 459–62, 514; quantitative indicators in 489–90; reliability and validity 130–1, 459; and theory generation 107–8; triangulation 130–1; see also mixed methods research quality: improvement 65; in qualitative research 459–62, 514; of secondary data 170–1, 183 quality assurance in schools 105, 106 quantitative analysis 646; see also quantitative descriptive analysis; statistical testing quantitative data 148, 149; measurement of 153–4; see also statistical data quantitative descriptive analysis 521–62; distributions see distributions; kurtosis 546–9, 567; mean value 526, 528–30, 533, 539, 544, 546; median value 526, 530–3, 539, 545–6; mode value 526, 533–6; skew 526, 545–9, 567;spread 526, 536–45; time series analysis 549–56 quantitative research 47–8, 96, 104, 129, 138–9, 139, 140; character of 98–103; hypotheses 37, 101–2, 107, 108, 112–15, 120; and positivism 37, 49, 98–9, 111, 121; problems with 115–20; and proof 100–3, 108, 114–15; and theory generation 99–100; see also mixed methods research quartiles 537–9 quasi experiments 124, 631 questionnaires 287, 288, 299–337, 340, 645; administration 321–4; and anonymity 318–19; and children 364–5; confidentionality 319; piloting 334–5, 364–5; and projective techniques 345; questions 310–21 (closed 300, 301–2, 303, 304; length 312; open 300–2, 303, 304; order effect 312; phrasing 313–15; on sensitive issues 317–19; simplicity 310–12; use of existing questions 315–17; wording 310–12); response bias 292, 320–1; scaling (methods 321–8; principles 306–9); structure and layout 328–31; and think aloud approach 364–5 questions: closed or structured 300, 301–2, 303, 304, 357, 384; and literature searches 205; open or unstructured 300–2, 303, 304, 356, 384 quintiles 537 quota sampling 252–4, 259, 260, 261 radar diagrams 444 Rademaker, L 479 INDEX random allocation of subjects 122–5 random sampling 239–42, 251, 261 range of a data set 537–9, 545–6 ratio scales 153–4 re-ordering data 416–18 reading 15–16, 196, 276; phonics approach to teaching 9, 10, 199–201 reading ability: influences on 121–2, 126; and questionnaire development 310, 311; and social background 615–16 reading a data set 399–421, 453–4; compressing data 413–15; re-ordering data 416–18; reconstructing data 419–21; shaping data tables 404; stripping out data 404–13; visual inspection 399–404 realism/realists 36, 483 reality, and qualitative research 105–7 reconstructing data 419–21 refereeing 27 references 217, 221 referencing: analysing 199; selective 198 reflective practice 630 regression analysis 441, 609–13 regression line 556–8 Reichel, Nirit 108–9 Reid, Colleen 361 Reid, Lesley 473 relatedness 574–5 relationship mapping 204–5 relativists 36 Relevance of Science Education (ROSE) project 300 reliability 129, 130–1, 459; of sampling results 263 representativeness 18 reputation 227, 229 research agenda 79, 80–1 research community 11, 14, 20, 79 research hypotheses 113, 120, 574–5, 578, 580, 617, 646; directional or non-directional 580, 581 research issues 20, 67; determination of 81; exploring 8, 11; mapping 202–5; selection of 79–85; systems representation of 276–7, 278 research literature 191–222, 640; auditing 87, 194; identifying the research method in 194–5; misuse of 195–201; scoping the research topic 194; as stimulus to research thinking 192–4; synthesising research findings 195 research priorities 79 research problems 79, 80; scoping 82–5, 194 research programmes 640–6; arguing the case for 642–4; determining strategy and methods 644–5 research progression 23–4 research proposals 85–9 research puzzle research questions 67–72, 79, 80, 138, 622–3; breaking down 270–2; case studies 624–5; and data mismatch 234–5; developing and justifying 88–9; ethnography 628–9; evaluation 626; experimentation 631–2; and mixed methods research 135–7; posing 637–9 research strategy 138–9, 622–3; action research as 630–1; case study as 624–5; determining 644–5; ethnography as 628–30; evaluation as 626–8; experimentation as 631–4; hypothesis testing as 634–6; identifing 641; posing research questions as 637–9 research students researcher–subject relationship 130 residuals 552 resources, auditing 20–1, 231 response bias 292, 320–1 review databases 209 Review of Education Research 209, 210 review journals 196–7, 208–9, 210 Review of Research in Education 209, 210 Rice, Mabel 383 Rice rule 534–5, 536 Richardson, John 292 Richmond, Heather 504–5 Rigotti, T 445 risk: assessment 21–2, 88; in interview situations 372 Robinson, Vaughan 359–60 Robinson, Vicki 357 Robinson, William 119 Rodgers, Dennis 353, 386 Roegger, Daniel 524 Rogers, Rebecca 510 rose diagrams 444 Rose, J 9, 11 Rosen, Monica 615 Rosenthal, Robert 347 Ross, J 277 running mean 551–3 Russell, Helen 41 Samdal, O 248 sample size 261–3; measuring 652–5 sample variation 127–8 681 sampling 116, 160, 226, 233–66, 388; bias 116, 265; case studies 257–8, 259, 260, 261; choice of method 260–1; cluster 246–7, 251; convenience 257, 259, 260, 261; frame 236, 237, 264, 265; framework of 236–8; and Internet 255, 258–9; multi-phase 249–50; multi-stage 247–9, 250, 251; non-probability 238, 252–61, 266; non-response 116, 264; probability 237–8, 239–51, 266; problems with 264–5; quota 252–4, 259, 260, 261; reasons for 234–6; reliability of results 263; respondent driven 255; self-selecting samples 258–9, 259–60, 261; simple random 239–42, 251, 261; snowball 254–5, 259, 260, 261; specialist group 255–7, 259, 260, 261; stratification 244–6, 247, 248, 251; systematic 243–4, 251; with/ without replacement 240, 242 sampling distribution 543, 544, 653, 654 Sandford, Rachel 626–7 saturation 472, 496 scaling see attitude scaling Scandinavian Journal of Educational Research 210, 349 scanning see reading a data set scatterplots 439–44, 451, 556 sceptical perspective 26–7 schemas 208–9, 304 Schlichting, Leesbeth 592–3 Schmitt, Dudolph 346 schools: admissions policies 45; inspections 106; and parental choice 100, 101–2, 112–15; quality assurance in 105, 106 Schuman, Howard 302 Schutz, Alfred 208, 487 Schwarz, Norbert 150 scientism 37, 53, 89 scoping the research problem 82–5, 194 Scottish Education Research Organisation (SERA) 386 secondary data 154, 159–88, 225, 232, 269; comparison 66–70; international data sources 182–7; Internet access to 159, 172–87; level of analysis 163–5; levels of confidence in 160–2; manipulation 168–9; national data sourcwes 172–82;process 682 INDEX of data collection 160–2; quality 170–1, 182; and research question mismatch 234–5; resolving problems with 168–9; scale of data presentation 162–3; spatial organisation of 167–70; types of 159 Segrott, Jeremy 359–60 selective coding 493–4, 495, 498 selective referencing 198 self-presentation 375–6 self-referenced data 162 self-selecting samples 258–9, 259–60, 261 semantic differential scales 324–6, 327–8 semi-structured interviews 356–9 semiotics 155–6, 484, 491, 511–13, 646 sensitive issues in questionnaires 317–19 sequential estimation 655 sex education 612–13 sexual attitudes/health 369 shadowing 354–5 Shannon, Sarah E 489 shaping data tables 404 Shaw, Ian 51 significance 566–7 significance level 115, 617 significance testing 567, 573, 575–82, 617 signifier/signified 511 Silver, Christina 479–80 Simons, Daniel 98 simple random sampling 239–42, 251, 261 Simpson’s Paradox 165, 167 SISA (Simple Interactive Skills Analysis) 589 Sitaram, Shashikala 345–6 skew 526, 545–9, 567 Slovakia, cluster sampling in health education 248 Smith, Peter 524 snowball sampling 254–5, 259, 260, 261 social classifications 150–2 social phenomenology 487 social value of research 647–8 socialisation 349 sociology 38–9 Sokal, Alan 45 Solheim, Erling 634 Sosu, Edward 109 sources: authoritative 227–9; reputable 227, 229; stability over time 229–30 Sparling, J 368 spatial analysis 570 Spearman coefficient 559, 560, 608 special needs students 327 specialist group sampling 255–7, 259, 260, 261 spectral analysis 554–5 speech data analysis 465 Spelberg, Henk 592–3 Spencer, Liz 459–60, 479 spider diagrams 87 spread 526, 536–45; estimation of using grouped data 538–9; range as measurement of 537–9; standard deviation as measure of 539–45 SPSS (Statistical Package for the Social Sciences) 588 standard deviation 539–45, 546, 567, 571, 653 standard error of the difference 590 standard error of the mean 544, 590, 654–5 standardising data 570–2 standards 51 Standards and Testing Agency 13 Stanley, Julian 611 star plots 444–5, 449, 451, 454 Stata 588 statistical data 69, 71, 159; extracting information from see portraying data; reading a data set; international 184–7; level of confidence in 160; national data sources 172–82; see also quantitative descriptive analysis Statistical Science 466–7 statistical significance 575–8 statistical testing 115, 116–17, 120, 128, 565–619; choice of test 584–6; determining a test result 575–82; for difference 587, 589–607 (analysis of variance (ANOVA) 601–7; chisquare test 593–8; Fisher’s test 600–1; Kolmogorov-Smirnov test 598–600, 656; Student’s t test 589–93); non-parametric 586; and normal distribution 567, 568–70; and number of data sets 585; parametric 586; for relationships 587, 607–15; variables 585, 586 Statistics Canada Statistics Netherlands 426–8 Statpages 589 stem and leaf diagrams 422–4, 451, 454 stepwise correlation 612–13 Stevens, Peter 208 STILE (Statistics and Indicators on the Labour Market in the e-Economy) 316 stimulating research thinking 192–4 Stokoe, Elizabeth 507 Stones, Anthony 270 Stott, Clifford 389 stratification 244–6, 247, 248, 251 Strauss, Anselm 491, 492, 497–8 stripping out data 404–13 structured interviews 341–2 structured questions see closed questions Student’s t test 589–93, 607 Stumpf, Heinrich 611 Sturges rule 534, 535–6 style guides 51 substantive (open) coding 492–3, 495, 498 Sudman, Seymour 314 summative content analysis 489 summative evaluations 59 Sun, S 583–4 Super Output Areas 170, 173 supervisors surface learning 513 survey and analysis approaches 112–20, 121, 138, 160–1 sustainability, evaluation of 59 Suto, Irenko 363 Sweden 210, 371; education research 39, 55; education system 13, 14; Index of Social progress 284; quality assurance in schools 106; statistical data 180 Swedish Research Council 52 symbolism 511 synthesis, of research findings 28, 195 synthetic phonics 9, 10 systematic sampling 242–4, 251 systems model 274–7; variables 279–80 tagging data 471–3 Tanner, Howard 345 target populations 236, 237–8 Tashakkori, Abbas 132 Taylor, Barbara 199–201 Taylor, Chris 109, 110, 134 teacher burnout 76, 597–8 teacher recruitment 637–9 Teaching Agency 13 teamwork, coding qualitative data 476–7 Teddlie, Charles 132 telephone interviews 342 Tella, Adedeji 592 template analysis 484, 485–8, 492 territoriality 349 INDEX test statistics 576; calculation of 588–9 text analysis software 477–83 Textanz 478 thematic apperception test 343 themes: identifying research themes 61–2, 642; and qualitative data coding 473–5 theoretical coding 494 theoretical perspective 15 theory 77, 89–90; development of, as an outcome 75–6; explanatory 73–4; influence on goals 72–6; normative 73; and qualitative research 107–8; and quantitative research 99–100 think aloud technique 363–5 Thomas, L 346 Thomas, N-E 592 Tihanyl, Krisztina 628–9 time, allocating 24, 88 time series analysis 549–56 timeline for research 230–1 Tizard, Barbara 641 Tom, A 362 Tomic, Welko 76, 597–8 total quality management 65 transcription 465, 466, 477 transforming data 547–8 transparency 5, 18–19, 293, 459, 514 Treasury (UK) 177 tree maps 449–450, 451, 452, 454 triangulation 130–1, 134, 155, 229 Trotman, D 39 Truss, Lynne 466 truth 16, 37, 47, 48, 97 Tukey, John 422 two-tailed test 578, 579–82 Tynjälä, P 597 United Kingdom: cohort studies 55; Every Child Matters initiative 277, 404–5; government departments 12, 13, 25, 174–7, 209; government and public data sources 172–7; Index of Child Well-Being 284; Index of Social Progress 284; statistical data 172–7 United Nations Educational, Scientific and Cultural Organisation (UNESCO) 182–3, 317 United States (USA) 83; Census Bureau 428; Centers for Disease Control and Prevention 488; cohort studies 55; data sources 180–2; education theory 73, 74; No Child Left Behind initiative 277 univariate data 436, 449, 454 Universities and Colleges Admission Service (UCAS) 152, 176–7 university admissions 152 university alliances 624–5 unmatched groups 124–5 validity 17–19, 129, 459 value added 286 value of research 646–8 values 35, 36, 46, 304–5 Van Petergern, Peter 632 variables 78, 294, 295, 436; and data analysis 284–6; dependent 279, 585; indicators 280–4, 294; and statistical testing 585, 586; types of 279–80; see also independent variables variance 540; see also analysis of variance (ANOVA) video recording, of focus groups 367 video surveillance 386 videos 344–5, 367 visual images 343–5, 367; discourse analysis 502–3; as source of data 458; as stimuli 343–4, 367 visual inspection of data 399–404 683 visualisation of data see portraying data Voeten, Marius 374–5, 376 Vygotsky, Lev 73–4 Walford, Geoffrey 641 Walker, D 497 Warburton, Bill 498 Warford, Mark 77 wavelength 553, 555 Weatherford, Jack 352 Web of Knowledge 207 weighted moving averages 553 Weitoft, Gunilla 578 Weitzman, Eben 480 well-being indices 419–21 Wessa, Patrick 442 West, A 45 Westcott, Helen 368 Westera, Wim 624–5 Wetherall, Margie 501, 502 What Works clearing house 110, 209, 210 Wheway, R 471 Whitaker, K S 76 Whyte, W.F 130, 348, 352, 386 wicked problems 136–7 Wiefferinck, C 612–13 Willett, Rebekah 507 word clouds 478 word trees 478, 479 World Bank 186, 187 World Health Organisation (WHO) 248 Wright, Patricia Ann 471 Young People’s Social Attitudes Survey 305 Z table 569, 579, 580, 617 Z values 569–70, 571, 572–3, 579–80, 617 Zhou, L 599 Zientek, Linda 591 Zolingen, Simone 256 ... State for Education has introduced a mandatory phonics screening check for children at the end of Year 1, the first year of education 10 Research Methods for Education Part The Context for Your Research. .. this 8 Research Methods for Education Part The Context for Your Research (ii) If you are preparing a research project If you are a student preparing a research proposal and plan, the goal for you... Context for research 14.1 Pointers to a research strategy Part B: Research practice 14.2 Styles of research 14.3 Preparing a case for a research programme 14.4 Benefiting from education research