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This book is for both students and practitioners in public services. By ‘public services’ I mean services run by society for society, such as health, social care, criminal justice, and education services from pre-school to university. Some public services are paid for by the state from our taxes, and others are run as charities, businesses, or social enterprises. ‘Practitioners’ are people who work in these services, whether paid or unpaid. They may also be users of the services in which they work and/or of other public services. They may also be studying for a formal qualification.

In the current climate, research is becoming an increasingly common requirement of public service jobs. This may be workplace research, such as evaluation of a service or intervention, a service user satisfaction survey, a skills audit, or training needs analysis. Or it may be academic research such as a diploma or a Master’s degree for the purpose of professional development or to support career progression. The differences between workplace and academic research are not as pronounced as you may think, because good quality research demands most of the same approaches and techniques, regardless of context.

There is also less difference than might at first appear between academics, practitioners, students and service user/carer researchers. People move much more freely between educational and practice roles these days. Any academic or practitioner may also be a service user and/or a carer, and/or be studying for a qualification. Work across disciplinary boundaries is also more common, whether by multi-disciplinary teams or by a single researcher who has, and wants to use, knowledge from more than one discipline.

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As a whole, research is complicated. There’s no point pretending it’s easy and straightforward. It is hard to do your own research well, and it can be difficult to understand other people’s research.

The good news is that research and evaluation can be broken down into component parts that are much easier to understand. As you become familiar with each piece of the jigsaw, you will begin to see how they can fit together to create a whole picture. This will make it easier for you to plan and carry out research. It will also help you to assess research done by other people.

This chapter and the next will introduce you to some of these component parts. The terminology begins to become more demanding at this point, so you may wish to use the glossary which you will find at the end of this book.

In the simplest terms, quantitative research deals with numerical data, that is, things you can count or measure. Qualitative research deals with textual data, that is, words and other forms of data such as pictures, sound, or multi-media.

Looked at another way, the two types of research can be held to express different philosophies or standpoints (Langdridge and Hagger-Johnson 2009: 13). Classic quantitative research follows the more traditionally scientific or ‘positivist’ methodology where a hypothesis is formulated, with a defined ‘independent variable’ whose variation can affect a ‘dependent variable’ or outcome. The questions are: ‘What? How much? How many?’ So a hypothesis might be that the amount of homework (independent variable) given to school pupils will affect their class test results (dependent variable).

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It is easy to get confused about the difference between ‘methods’ and ‘methodologies’, particularly as ‘methodology’ sounds like a posh word for ‘method’. Even some textbooks get these terms confused, so it’s not surprising that students and practitioners can find them bewildering.

Methodologies, epistemology, quite complex ideas really, going back to the philosophical underpinnings of knowledge, I do find that quite challenging. I find it interesting, but I do find that sometimes you can get really bogged down in it.

In fact, methods and methodologies are not the same. They have a relationship with each other, and with research approaches and questions, which is both important and useful. This chapter will help you to understand: the difference between methodologies, approaches, and methods; the important concepts of epistemology and ontology, and how they relate to methodologies, approaches, and methods; and the role of theory in research. This may sound complicated, and in a way it is, but we will go through it one step at a time. Definitions of all the key concepts can be found in the glossary at the end of this book. Understanding these concepts will help you to create a successful research or evaluation project.

A methodology is a coherent and logical framework for research based on views, beliefs, and values. This framework guides the choices made by researchers. For example, a researcher working within a participatory methodology would choose to involve their participants in the research process as much as possible.

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It can be tempting to rush through the early stages of the research or evaluation process and plunge straight into data collection. This is a bad idea. The more careful thought and exploration you put in at the start, the more time you will save yourself later on. This chapter will guide you through the process of choosing and refining a research topic and writing a research proposal. These are the first building blocks for the construction of your research, and it is always easier to build on solid than on flimsy foundations.

Choosing a topic can be a challenge, particularly if the choice is very wide, such as research for a Master’s dissertation or evaluation within a whole organisation. Where possible, the best approach is to focus on whatever interests you most (Robson and McCartan 2016: 49).

If you’re going to do a postgrad and you have the opportunity to design your own question, make sure it’s something that really interests you because you won’t like it halfway through anyway.

Research can be a hard and lonely journey, and a passion for your topic will help you to keep going through the difficult times.

Some people have topics chosen for them, whether they are doing workplace or academic research. This may feel like a blessing in the short term, but people who are not enthusiastic about their research topic may have difficulties later on.

I was talking to a colleague, someone else has chosen her topic for her, she’s not committed to it.

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Research isn’t a sprint, it’s a marathon. Successful completion of a marathon takes planning, organisation and time management. This chapter will help you to learn techniques for these essential parts of the research and evaluation process. It contains a great deal of advice from practitioners who are experienced in conducting research, in the hope that their hindsight can be your foresight.

Research by Richard Wiseman from the University of Hertfordshire in 2009 (reported in the Guardian newspaper on 28 December 2009) showed that people were more likely to stick to their New Year’s resolutions if they followed six easy steps:

  1. made a plan for their project;

  2. broke down their overall goal into smaller goals, for each week or fortnight, which were SMART (specific, measurable, achievable, realistic and time-based);

  3. told their friends and family about their goals;

  4. rewarded themselves whenever they achieved one of the smaller goals;

  5. kept a diary of their progress;

  6. regularly focused on the benefits of success.

These steps are also ideal for helping researchers to complete their projects successfully.

The people I interviewed for this book were unanimous about the need to plan research carefully. You may feel inclined to skip this section. Perhaps you don’t think there’s any point in reading about planning, either because you’re not a natural planner, or because you’re already good at planning. Either way, I would advise you to read on. If you’re not a natural planner, don’t worry; project management is a skill that can be learned. If you are already good at planning, this chapter covers specific ways of planning research which will help you to complete your project and meet deadlines more easily.

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Whatever kind of research or evaluation you’re doing, some background research is always helpful. This can range from just a few project documents and perhaps one or two pieces of national policy, for a small evaluation, to several hundred items of published and ‘grey’ literature for a full-scale literature review.

When you’re dealing with material that you can engage with and understand, the bit where it’s exciting, firing off all sorts, the thrill of it, it’s the bit that’s pleasurable.

The main point of background research is to provide the context for your own research or evaluation. As a result, much of the work on this is best done early in the research process – although it may also be necessary to include any legislation, policy, and/or key articles or books that are published as you do your research. Also, you may decide to do more background research at a later stage, such as if your data analysis reveals something unexpected and you want to put that finding into context for your readers.

If you’re doing research for postgraduate academic study, you will be required to do a formal literature review. This is covered later in the chapter. If you’re doing research in and for your workplace, you will need to do a document review.a So what is the difference?

In conducting a literature review for academic research, you start from the literature and work towards your research topic, using the literature to help you develop your research questions.

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Secondary data is data which has been collected by someone else, for a purpose other than your research or evaluation project, and has been made available for re-use. Most of this is in digital form. Each individual collection of digital research data is known as a ‘dataset’; these can include numbers, texts, images, blog posts and so on. Due to great efforts to open up data to the public, there is now a huge amount of secondary data freely available on the web, and more is being added all the time. Secondary data is also held in libraries, museums and archives. Examples of quantitative secondary data are government statistics and census data. Examples of qualitative secondary data are criminological data, health data, web archive data, and oral history data.

I use secondary data to evaluate health related services. They collect information and outcome related data from their clients for their own purposes, which I then use to evaluate service effectiveness.

Freely available secondary data is a tremendous resource for any research or evaluation project. However, only two of the people I interviewed for this book mentioned secondary data. This chimes with findings from the research literature which state that secondary data is generally under-used (Lewis and McNaughton Nicholls 2014: 53). Yet secondary data has a great deal to offer to researchers.

Although there are several advantages to using secondary data, there are also some disadvantages.

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The collection of data is often the first thing people think of in connection with research or evaluation. A common misconception is that more data always leads to better research. As we saw in Chapter Four, this is not the case.

There’s a danger of people burying themselves in data. Don’t ask too many research questions. Set the agenda firmly at the beginning and find out only what you need to know, or you will be drowning in numbers and information which is nice to have but not necessary.

Too much data, particularly qualitative data or data in a variety of formats, can become unwieldy and difficult to analyse.

When it came to analysing data I think I dumped about a third of the questions that were asked which weren’t relevant to what we were doing.

Collecting data that you don’t use is unethical because it places an unnecessary burden on participants. However, insufficient data will definitely lead to poorer quality research. It isn’t always easy to figure out how much data you need.

You don’t have to collect data using one method alone. In fact, using more than one method can be helpful, and this is known as ‘triangulation’. The term has its origins in physical sciences, such as land surveying and water navigation, where two known points are used to find the location of a third.

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When you have collected all your data, you need to analyse it; to find a way to understand what it can tell you. This is both one of the most challenging and one of the most rewarding parts of the research or evaluation process.

One of the people I interviewed for this book said that they found data analysis to be the easiest part of the research process. Another said quantitative data analysis was the easiest. On the other hand, four said that for them, data analysis was the hardest part of the research process. Also, two of the interviewees who teach novice researchers commented that new researchers often find data analysis to be one of the most difficult parts of the process.

Starting to work with your data can feel quite daunting.

You’ve got it all, you’ve got this great mountain of stuff and then it’s ‘OK, what do I do with it now?’ This is all very interesting, but what do you do with it? I didn’t have a computer program or anything like that, so it was your highlighter pen and your cut-and-paste to get things into categories. How was I deciding how to get things into categories? Was it what people said? If some of them said ‘integrated working’, did they all mean the same thing or something different?

Even if your own data is mostly quantitative or mostly qualitative, you need to understand the principles of data analysis for both types of research. This chapter will cover each approach separately before looking at how to cross-analyse and synthesise different datasets.

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Although writing has a chapter all to itself in the second half of this book, it is not a discrete activity which happens late in the research or evaluation process. Writing begins at the very beginning, with notes and plans and lists and, more formally, your written research proposal or plan. It continues throughout as you make records of literature and documents that you read, write a research journal (see Chapter Five), take notes, and code data. And it doesn’t finish when your report, dissertation or thesis is complete, because you will continue to write your research as part of presenting your findings at a meeting, conference, viva or other event, or in a newsletter or journal article, or on a web page.

This chapter aims to debunk some of the main myths about writing. The first myth is that writing happens in the same sequence as reading, that is, you start with the words ‘Chapter 1’, carry on until you get to ‘The End’, and then stop. Actually, there is plenty of reading which doesn’t happen like that either; I’ll bet most of the people who use this book don’t read from the first page all the way through to the last. But some books are read as linear narratives, particularly fiction, and that is probably where the myth developed.

A second myth is that writing is easy and anyone can write if they feel in the mood. In fact, writing is hard graft.

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