social science research

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Please read the attached reading to answer the question below.

Select both a qualitative software program and a quantitative software program that could be used when conducting social science research.

Answer must be a minimum of 200 words and use in text citation from the reading. No Plagiarism.

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Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. 8 Preparing to analyse data Introduction • The shape of your data • The nature of data • Managing your data • Computer-based data management and analysis • The process of analysis • Summary • Exercises • Further reading Introduction We hope that you are reading this chapter well before you have finished collecting your research data. You are likely, after all, to begin analysing your data before you have collected it all, possibly starting as soon as you have some data to work on. Analysis is an ongoing process which may occur throughout your research, with earlier analysis informing later data collection. Research is, as we have said a number of times in this book, a messy business, and the stages and processes involved do not simply follow one after the other. You might like to refer back at this point to the section on Getting a flavour of possibilities in Chapter 1. You would probably be best advised to look through this chapter, and the following one, before you finally decide how you are going to focus your study, and what kinds of approaches and techniques you will apply. It makes sense to have some understanding of the kinds of data analysis you might engage in, and how the kinds of data you collect will affect and limit this, before you commit yourself to a particular project. EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. 212 8: PREPARING TO ANALYSE DATA The purpose of this chapter is to help you get your data into shape, and to discuss the process of analysing data. We start from those unsure, initial feelings, which are so common to both novice and more experienced researchers, of having an overwhelming or chaotic collection of research data. Chapter 9 then examines the techniques involved in analysing documents, interviews, observations and questionnaires. This chapter is organized in terms of the following themes: • The shape of your data. The condition which your research data are in, and the facilities you have available to analyse them. • The nature of data. What research data are, the meaning of numbers and words. • Managing your data. Coding, reducing and summarizing your raw data. • Computer-based data management and analysis. Using software packages with quantitative and qualitative data. • The process of analysis. Thinking about and planning your analysis. Hint: If you feel traumatized or terrorized by the process of analysing the data you have collected, you might like to think of it as analogous to cooking. What and how you cook depends on your taste, skills and the resources you have available. You may like your food simple and freshly prepared, or carefully blended over a long period, or fast and processed. You may mix the ingredients together using a recipe, or based on previous experience, or you may buy a packet already prepared. You may use a range of tools in your cooking, from a simple knife or spoon through to an expensive food processor. You may be preparing food just for yourself or for a banquet. See if you can find further parallels as you cook your data! The shape of your data Two basic issues affecting your whole approach to data analysis are considered in this section: • The condition of your data. • Where, and with what facilities, you are able to analyse them. Order or chaos? You will probably spend a considerable amount of time collecting your research data, and – unless you are relying entirely on secondary data sources EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. THE SHAPE OF YOUR DATA 213 with which you are already familiar – the shape of the data collection that you end up with will almost certainly be rather different from the way you had envisaged it when you started. While your plans for data collection may have seemed very methodical, the data you have actually collected may initially appear to be anything but. They may seem more chaotic than ordered (see Box 8.1). Box 8.1 Ordered or chaotic data? Appearance of order Appearance of chaos Neat notebooks Card indexes Sorted piles of questionnaires Colour-coded folders Labelled, transcribed tapes Highlighted photocopies Clear plan and schedule Computer database Odd notes Scraps of paper Baskets of cuttings Bulging or empty files Jotted down quotes Half-remembered references Back of an envelope Illegible handwriting Whether your data appear ordered or chaotic depends in part on your preferences, and in part upon your perceptions: one person’s chaos may be another’s order. The real issue is what works well for you. So long as you know where to find what you want or need to find, that’s OK. If you are new to the process of research, you may be finding this out as you go along. There is no single ‘right’ strategy for carrying out research, or for ordering and analysing data. Much of what is said in this book can be taken to indicate a preference for planning, structure and order, but these qualities may be conceived of very differently in practice. The condition your data are in will undoubtedly change during the process of analysis. However poor, ill-organized or inadequate you may think they are at the beginning, you are likely to find strengths in them as you proceed. Similarly, even if you start from the position that you have all the data you need, you are likely to recognize deficiencies as you get into the depths of analysis. Data analysis is about moving from chaos to order, and from order to chaos. Data which seem under control are likely to become somewhat more disorganized, at least for a while; whereas some semblance of order will be found, or imposed upon, even the most chaotic collection. Your data may, at times during the process of analysis, appear to be both messy and structured. Areas where you think that your data add to an understanding of the topic you are researching may be seen as ordered, whereas areas in which your work has raised more questions than answers (the normal pattern) may appear as more chaotic. EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost 214 8: PREPARING TO ANALYSE DATA Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. Where to analyse, and with what? The resources you have available for your research, and how you might tailor your research plans to them, have already been considered elsewhere in this book. You might like to have another look at the sections on Choosing a topic in Chapter 2, and Using computers in Chapter 5. Obviously, you are restricted in how, where and when you carry out your data analysis by the available resources. There are, however, practical issues concerned with the place, space and time in which you do your analysis which are worth further consideration. For example: • Do you prefer working at a desk or in an armchair? • Will you want to spread your work over a floor or a wall? • Do you like to work with paper and pen (or pencil)? Or straight on to a computer screen? • Does your analysis require extensive dedicated periods of time, or can it be done in smaller chunks? Or are there elements of both? • Can you do your analysis in one place, or will it require visits to a number of separate facilities? Clearly, your answers to these and related questions will help to determine how you go about analysing your data. You will need to reconcile your preferences with what is feasible, and with the nature of the data you have collected. The nature of data The data you have collected are likely to be in a number of forms, though it is perfectly possible to carry out interesting and valid research with just one form of data. Your data might include, for example, completed questionnaires, interview transcripts, notes on readings or observations, measurements of behaviour, internet materials, policy documents, academic articles, charts, diagrams and photographs. Some may be in digital form. Now might be a good point to take some time to remind yourself about the nature of your data, the amount you have, where they have come from and how they have been produced. Boxes 8.2 and 8.3 include a variety of examples of different sorts of quantitative and qualitative data to remind you of some of the possibilities. EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. Box 8.2 Examples of quantitative data Order No. of responses Factor 1 2 3 4 5 6 7 8 9 10 113 73 70 64 59 51 49 48 41 35 Higher pay Feeling valued by stakeholders in education Desire to help children learn Less administration More non-contact time for planning and preparation More support with pupil discipline issues A reduction in overall work load Good working relations with managers and other staff Good prospects of career advancement Smaller class sizes (Source: Rhodes et al. 2004: 74) (Source: Chandra 2004: 185) Profile of Returnees Start Finish University Course College in NZ? Scholarship Work in NZ PR Twin Program 1959 1965 1963 1963 1966 1967 VUW VUW Canterbury N N N Y Y Y N N N N N N N N N 1964 1961 1967 1967 N N Y Y N N N N N N 1961 1970 1974 1969 1973 1977 Canterbury Canterbury and Otago Canterbury VUW VUW BA/MA (Geog) Accounting BE/BSc/MA (Chemical Engineering) Economics & Agriculture B.Sc & PGDip Statistics N N N Y Y Y N N N N N N N N N 1988 1991 VUW N N N N N B.E./1/2 M.E. BA (Hons:Psychology) BA (History and Ed)/Hons (Ed) BA (Eng & Pol)/Hons (Engl Lit.) (Source: Butcher 2004: 280) Factor Analysis of Predictors of Identification with the Employing Organization Factor Loadings Predictor Organizational attributes Relationship with management Relationship with colleagues Positive distinctiveness Providing opportunities to creatively solve problems Keeping up to date with changes in IT Providing career advancement opportunities Doing high-quality work Providing a work environment that is free of politics I trust that this person will advance my best interests when decisions which affect me are made. I have trust and confidence in that X employee regarding his/her general fairness. I feel free to discuss the problems and difficulties in my job with that X employee. .86 .82 .81 .77 .77 .27 .18 .15 .18 .28 .16 .88 .12 .01 .11 .00 .11 .16 .23 .24 .28 .24 .01 .23 .29 .88 .11 .21 .18 .87 .15 .18 (Source: Chattopadhyay 2005: 69) EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. Box 8.3 Examples of qualitative data 1 2 Chapter 6 We recommend to the Government that it should have a long term strategic aim of responding to increased demand for higher education, much of which we expect to be at sub-degree level; and that to this end, the cap on full-time undergraduate places should be lifted over the next two to three years and the cap on full-time sub-degree places should be lifted immediately. Chapter 7 We recommend to the Government and the Funding Bodies that, when allocating funds for the expansion of higher education, they give priority to those institutions which can demonstrate a commitment to widening participation, and have in place a participation strategy, a mechanism for monitoring progress, and provision for review by the governing body of achievement. (Source: National Committee of Inquiry into Higher Education 1997: 42) Cris: Kathy: Cris: Kathy: Cris: I remember playing dress up and I got to be the princess and you had to be the prince, you were the older sister and you had to be the prince. I remember that; that was a lot of fun. I remember I hated the way Mom used to always make me wear pink and you always got the blue dress. I always hated dressing up like that anyway. Regardless of what it looked like. It was kind of cute. It was so uncomfortable. (Source: Davis and Salkin 2005) Principle Explanation Foundational citations 1 Reformation of the professor– student relationship A feminist pedagogy offers the professor and the students new relational roles. Individuals involved in the learning experience share knowledge and thus enact the teaching role as well as acquire knowledge and thus enact the learner role (Parry 1996). To empower a student is to enact ‘a participatory, democratic process in which at least some power is shared’ (Shrewsbury 1993: 9). The professor can acknowledge power as evaluator and grader, while also redefining the teaching role from knowledge leader to ‘activation of multiple perspectives’ (Scering 1997: 66). Bowker and Dunkin 1992, Bell 1993, Bright 1993, Shrewsbury 1993, Foss and Griffin 1995, Christie 1997, Scering 1997, Stanovsky 1997 Bright 1993, Shrewsbury 1993, Woodbridge 1994, Chapman 1997, Scering 1997, Middlecamp and Subramaniam 1999 2 Empowerment (Source: Webb et al. 2004: 425) (Source: Bagnoli 2004: 11) EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost THE NATURE OF DATA 217 Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. The qualitative/quantitative divide Among these different kinds of data we may recognize a basic distinction between the quantitative (i.e. numbers) and the qualitative (i.e. words and everything else). This distinction has a major influence on how data may be analysed, and also reflects the varied ‘traditions’, philosophies and practices of different social science disciplines or sub-disciplines. You are almost certain to have examples of both types among your data, though either the qualitative or the quantitative may predominate. You may wish to refer back to the sections on Which method is best? and Families, approaches and techniques in Chapter 3. However, the distinction between words and numbers is not as precise as it may appear to be at first sight. Both offer representations of what we as individuals perceive of as our ‘reality’. It may be that qualitative data offer more detail about the subject under consideration, whereas quantitative data appear to provide more precision, but both give only a partial description. Neither are ‘facts’ in anything but a subjective sense. The accuracy of the representation is also likely to be reduced further during the research process, as we attempt to summarize or draw out key points from the vastness of the data available. The quantitative and qualitative also have a tendency to shade into each other, such that it is rare to find reports of research which do not include both numbers and words. Qualitative data may be quantified, and quantitative data qualified. For example, it is common practice in analysing surveys to assign, sometimes arbitrarily, numerical values to qualitative data, such as, ‘successful’ (1), ‘unsuccessful’ (2). Researchers who adopt an explicitly qualitative stance can find themselves giving prominence to numbers. Thus, if you conduct your research entirely through interviews, and analyse the results by searching for similarities and differences in the interview records, you are quite likely to end up using numbers or expressions of quantity in your writing: e.g. ‘all of the interviewees’, ‘most of the respondents’, ‘half of the women I spoke to’. On the other hand, if you base your study wholly on numerical data, you will still introduce qualitative factors in your analysis, as in discussing the relative worth of different data sources, and in interpreting what your results mean for practice. The next two subsections aim to make these points clearer. You may want to skip one or other of them if you are already familiar with quantitative or qualitative approaches. What do numbers mean? Exercise 8.1 asks you to examine the examples of quantitative data included in Box 8.2. Box 8.2 does not, of course, include examples of all the different kinds EBSCO : eBook Collection (EBSCOhost) - printed on 4/3/2019 1:34 PM via TOURO COLLEGE - LOS ANGELES AN: 353622 ; Blaxter, Loraine, Tight, Malcolm, Hughes, Christina.; How to Research Account: ns011983.main.ehost Copyright @ 2010. McGraw-Hill Education. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. 218 8: PREPARING TO ANALYSE DATA or uses of numbers which you might come across in the course of your research, but it does contain some of the most common. If you have carried out a survey or experiment as part of your research, you are quite likely to have produced figures not unlike some of them. These may include, for example: • • • • direct measurements, or what might be called ‘raw’ or ‘real’ numbers; categories, where responses have been coded or assigned a numerical value; percentages, a measure of proportion; averages, which summarize a series of measurements. The second question posed in Exercise 8.1 highlights a key point about quantitative data (and data in general), namely that they might tell you a lot if you only knew how they were arrived at and how to interpret them. Every data source needs to be interrogated as to its representativeness, reliability and accuracy. Researchers ideally need to know by whom the data were produced, for what purpose and in what ways. Numbers, by their very seeming precision, can hide their manufacture, imprecision and subjectivity. These issues are considered further in the section on Interpretation in Chapter 9. The third question posed in the exercise indicates that, once you are presented with a set of quantitative data, you can usually start to do other, quantitative or qualitative things, with it. You may have found yourself calculating averages, or thinking that one item was bigger or smaller than another, or of the same value. If you have sufficient information, you can calculate percentages from raw data, or produce the raw data from the percentages reported. The quantitative data presente ...
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Running head: SOCIAL SCIENCE RESEARCH

Social Science Research
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SOCIAL SCIENCE RESEARCH

Social Science Research
Research can become messy if it is not conducted in the right way. It is essential to have
the right tools that will aid in the...

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