*Little Quick Fixes* are small, inexpensive books from Sage that deal with specific areas of a research project or dissertation.

There are about 20 books at present, and I’ve had a good look at some of them.

Each book is about 120 pages and can be read – so they say – in one hour.

All the books start with a contents page and a summary of the chapters (Everything in this book). There are 6, 7 or 8 chapters in each book and they all have explanations, diagrams, charts, tests, summaries. The books finish with a glossary. There is no index, no bibliography and we are told nothing about the authors. There is no cross referencing – it would be useful.

The books vary in quality and there is a large amount of repetition, but I don’t suppose anyone will read them all! The series editor could have done a better job in this matter. The one book that is missing concerns writing the research report. Books on observation and document analysis would be useful too.

Zina O’Leary – *Research question* (2018)

Zina O’Leary – *Research proposal* (2018)

Robert Thomas – *Turn your literature review into an argument *(2019)

Janet E Salmons – *Find the theory in your research* (2019)

Helen Kara – *Write a questionnaire* (2019)

Helen Kara – *Do your interviews* (2019)

Janet E Salmons – *Gather your data online* (2019)

Nicola Thomas – *Get your data from social media* (2020)

Paul Silvia –* Select a sample* (2020)

John MacInnes – *Know your numbers* (2018)

John MacInnes – *See numbers in data* (2019)

John MacInnes – *Understand probability* (2018)

John MacInnes – * Statistical significance* (2019)

John MacInnes – *Know your variables* (2019)

Maureen Hacker – *Choose your statistical test* (2019)

Helen Kara – *Use your interview data* (2019)

Helen Kara – *Use your questionnaire data* (2019)

Robert Thomas – *Find the theme in your data* (2019)

Zina O’Leary – *Present your research* (2019)

Here are some quotations from the books – taken from the “Everything in this book” sections – and my opinions (in **bold**) about their usefulness for ESP – particularly EAP – students.

Zina O’Leary – *Research question* (2018)

Section 1. A well-articulated research question is absolutely critical to research. Research questions define an investigation and provide direction. They tell you where you need to go, and even indicate how you should get there.

Section 2. Research questions are designed to provide insights into queries and dilemmas that are yet to be understood or solved. Good topics for research are therefore topics where the unknown is important and the resulting insights are practical and useful. This high utility must be checked against your ability to conduct your research in a credible way.

Section 3. Moving from a topic to the articulation of a researchable question is tricky. Following a series of logical steps that focus your thoughts will put a tangible question within your grasp. Practicalities such as appropriateness of topic, supervisory support, and funding/resources will also guide the development of your question.

Section 4. A hypothesis is a logical conjecture between two or more variables (one dependent variable and one or more independent variables). A hypothesis is not always appropriate, particularly in the case of more exploratory questions.

Section 5. Four steps that will help you to generate your own research question.

Section 6. A good research question should… be right for you, add to a body of knowledge, be well articulated, be ‘doable’, and have necessary support.

**This is useful. There is a good distinction between topic and research question. And there are some good examples of formulating research questions.**

Zina O’Leary – *Research proposal* (2018)

Section 1. A research proposal is one of the most important documents you can craft. The proposal ‘sells’ your project to those in power and covers the merits of the researcher, the research question, and proposed methods.

Section 2. As well as being a critical document for others, a well-considered proposal helps you clarify your thinking, bed down ideas, and articulate thoughts. it is a blueprint for action!

Section 3. In order to write a proposal, you will need to work out a clearly articulated research question and a general research plan. From there, you will need to articulate elements, such as title; abstract; aims/objectives; research question/hypothesis; introduction/background/rationale; literature review; theoretical perspectives; methodology; methods; respondents; limitations/delimitations; ethical considerations; timelines; budget/funding; and references.

Section 4. Writing a winning proposal is about writing purposively while following any guidelines to the letter. Research proposals are often high stakes documents, so be prepared, be logical, and be concise.

Section 5. Obstacles are many, but they are opportunities to hone your thinking. This can happen when: you are forced to challenge your assumptions; your proposed research methods don’t fit proposal guidelines; or your methods are evolving or emergent. Being knowledgeable, confident, and open are the keys to getting past them.

Section 6. The DIY workbook at the end of this book is your chance to put your ideas to paper!

**Quite useful. There is a clear description about what should be included and how to do it. A nice timeline is included. There are some some examples of parts of research proposals.**

Robert Thomas – *Turn your literature review into an argument* (2019)

Section 1. First, you need to know what a literature review is. A well-designed and presented literature review is central to the success of your research project. Without a review we have no way of establishing where your research fits in.

Section 2. There are fundamental differences between an argument and an academic argument. General arguments can be emotional, lacking in clarity, and may never be resolved, while an academic argument should be considered, rational, logical, analytical, and above all persuasive.

Section 3. A good argument is based on what’s already known. To be able to present an argument it’s important to consider where the factual and theoretical underpinnings of your argument come from. Using the right data will help build a convincing argument.

Section 4. When building your argument, it’s important to understand that your argument already exists within the available literature. You’re not creating something original, you’re just reorganizing what’s out there.

Section 5. While building your argument, you must consider how to convey your argument. How you say things in building an argument will help convince the reader that your work is worthwhile.

Section 6. Your argument alone will not be enough to suggest your work has merit. The strength of your argument will need to be considered in the light of opposing views and through counter- argument. You need to provide a balanced review that will question itself.

Section 7. To make sure you’re presenting your arguments properly you’ll need structure. Good structure will ensure that what you present is cohesive, transparent, and above all readable.

Section 8. Making sure you’re on the right track with argument development is crucial. You need a consistent plan for getting feedback and this should always be at the back of your mind.

**Good little book. Looks at what a literature review is, its purpose and how it is structured. There is little on language. Diana Ridley’s (2008) book is more useful for EAP students.**

Janet E Salmons – *Find the theory in your research* (2019)

Section 1. Scholarly research lives within a particular school of thought. it is important to clarify where and how the study fits into a school of thought by explaining the ontological and epistemological positions that inform the study.

Section 2. Theories are what differentiates scholarly work from other types of research and writing. Theories help explain specific relationships between factors or variables the researcher hopes to explore.

Section 3. Scholars talk about theory in academic literature to describe research in ways others with similar interests will understand. Most disciplines have theories that serve as foundations for new research.

Section 4. Each type of research design has its own approach for using theory. Selection of the theory (or theories) means finding the one(s) that fits your quantitative, qualitative, or mixed methods research. The ways theory aligns with your research questions or hypotheses creates a unique framework for organizing the study.

Section 5. Discover ways to think about theory. How you describe discoveries about emerging, contemporary, or classic theories allows you to make a theoretical contribution.

Section 6. Practical steps to integrate theory into your research start at the beginning. Finding and understanding the appropriate theory are first steps for scholarly research design.

Section 7. Theories can be difficult to define and understand, but you can ask questions of the literature in your field to identify and use the relevant important theories in your own research.

**Good on theories. It is good on where in your research you need to use theories. It doesn’t help you to develop theories from your own research, which I hoped it might. Not a good title for the book. It will need supplementing with examples of language.**

Helen Kara – *Write a questionnaire* (2019)

Section 1. First you need to know whether questionnaires are right for your project. Are you researching a topic that is not too personal or sensitive? And do you want specific, measurable information? If so, questionnaires are likely to work for you.

Section 2. The practicalities of using questionnaires depend on how you decide to administer them — on paper, online, by text message, or on a device such as a tablet.

Section 3. You can use any or all of a wide variety of question types, such as demographic questions, yes/no questions, dichotomous questions, multiple-choice questions, scaled questions, and open questions.

Section 4. You can find people via postal or email address lists, in person, or online.

Section 5. Every questionnaire should be tested to find out how well each individual question works and how well the questionnaire works as a whole.

Section 6. Some options for increasing your response rate include follow-ups (for posted or emailed questionnaires), give-aways (for questionnaires administered by post or in person), incentives, and visually interesting questionnaires.

Section 7. When you have all your completed questionnaires you need to prepare your data for analysis.

**Good overview. It include whether to use a questionnaire, practicalities, question types, finding participants, piloting, and preparing for data analysis. Nothing much on the language used in questionnaires.**

Helen Kara – *Do your interviews* (2019)

Section 1. First, you need to know if interviews are right for your project. Do you need to discover people’s thoughts, feelings, knowledge or experiences? If so, interviews will work for you.

Section 2. Finding people to interview is not always easy. Your personal and professional networks can be a great help.

Section 3. You need to find out, politely and carefully, if people will agree to be interviewed. Provide full information about your research. Take ‘No’ for an answer and withdraw gracefully when necessary. Don’t give up.

Section 4. Open questions are most useful for collecting detailed data. A closed question can be helpful if you want to separate your interviewees into groups. Questions should be simple, specific, and socially appropriate.

Section 5. You need to plan where to hold each interview. They can be held face-to-face in public or in private, by phone, or over the internet. You also need to plan how to capture your data; usually by writing, typing, or recording.

Section 6. During the interview, you should behave politely and with consideration for your interviewee. You want to put your interviewee at ease.

Section 7. When the interview has concluded you should take care of your own well-being and of your data.

**Good overview. It includes whether to use interviews, finding people, ethics, appropriate questions, doing the interview, preparing for analysis. There is some useful information on language.**

Janet E Salmons – *Gather your data online* (2019)

Section 1. There are two main types of online data collection. Extant or elicited approaches can be carried out using qualitative or quantitative methods.

Section 2. Choose the type of online data collection that fits the research problem. Decide what you want to know, and from whom, ¡n order to decide the best approach.

Section 3. Find and use data that exists online. It is important to know your options and limitations for using data in your study.

Section 4. Recruit and gain consent from participants online. Knowing how to develop a sampling strategy, recruit suitable individuals, then build trust and credibility, are important steps if you plan to engage human participants.

Section 5. Interact with participants online. Ask structured or unstructured questions with online interviews, surveys, or questionnaires.

Section 6. Take scholarly and practical steps to prepare. Being able to find and use extant data or recruit and interact with participants is essential to online research. Thinking through your design strategy and building the skills needed to carry it out will make your research a success!

**This is a useful little book. It was the first one I saw and the author is the only one I had heard of. It distinguishes between extant data – created for another purpose, not as a result of our questions – and elicited data – created as a result of our questions or prompts. It gives procedures for collecting both types, both qualitative and quantitative – interviews, surveys and questionnaires. It deals with ethics, synchronous and asynchronous data collection, and technology.**

Nicola Thomas – *Get your data from social media* (2020)

Section 1. Social media data can serve many research needs. It is important to know the potential, and pitfalls, of social media data. Decide how social media data can provide a good fit for your research,

Section 2. Conduct your social media research to high ethical standards. Understand and address ethical dilemmas when obtaining and using social media data.

Section 3. Choose the type and scope of social media data. Decide what type of data you need, from where, when and from whom or what, to decide the best approach.

Section 4. Develop a robust sampling strategy to obtain social media data. Identify your population, sampling frame, sample and sampling technique(s) to best meet your research needs.

Section 5. There are three ways to extract social media data. Know how to extract social media data, either manually, computationally, or a hybrid approach, to best suit your research needs, capabilities and resources.

Section 6. Take strategic steps to plan your data collection. Thinking through, and planning out, your data collection will increase your chances of generating valuable insights from your social media data.

**Useful and probably important in the future. It starts by asking if social media can help you with your research? It then discusses ethics, what kind of data you can get and how to get it? It includes a section on sampling, as do other books in the series.**

Paul Silvia – *Select a sample* (2020)

Section 1. Sampling is studying a ‘part’ to understand the ‘whole‘. Studying samples enables researchers to understand large dispersed groups.

Section 2. Your population is the broader group you want to understand, not ‘everyone everywhere’. Defining your population narrowly gives your project focus and credibility.

Section 3. Probability sampling methods, the gold standard in sampling, should be your first choice. They create a small-scale replica of the population by randomly selecting members from it.

Section 4. Because non-probability methods – quota, convenience and purposive sampling – introduce subjective judgement into the sampling process, you should view them as fallbacks for when probability sampling isn’t feasible.

Section 5. Asking participants for referrals, known as snowball sampling lets you recruit members of ill-defined, hard-to-reach, and wary populations.

Section 6. To sample ethically, researchers must think through the legal and ethical issues and consult their local ethics office.

Section 7. Reducing error involves targeting both random error and systematic, consistent biases in sampling.

Section 8. Plan for the largest feasible sample size: large samples reduce your margin of error and increase statistical power.

**A useful book. Distinguishes sample/population and then goes through typical sampling methods. It has a section on ethics, as do many of the other books in the series.**

John MacInnes – *Know your numbers* (2018)

Section 1. Numbers are important. Without practice, it’s easy to get rusty with handling numbers. Being fluent with numbers makes all kinds of tasks easier. Numbers are basic to understanding many aspects of society, and with the data revolution taking place, they’re more important now than ever.

Section 2. A fraction is the building block of a comparison. Fractions are the basic way of expressing proportions or parts of a whole number, usually between zero and one. Being able to describe and manipulate fractions of a number is the basis of making comparisons. Every fraction has a numerator and a denominator.

Section 3. Five interlocking rules tell you everything you need to know about dealing with fractions. Practise these rules and you’ll have everything you need for using numbers to compare things.

Section 4. Learn to read a table. Tables convey massive amounts of information. The numbers in them allow us to make so many different kinds of comparisons, all based on fractions.

Section 5. We can compare things through a table’s rows and columns. Numbers in the rows and columns of a table can be expressed in fractions. Comparing fractions based on rows along a column, or fractions based on columns along a row, tells us two different but equally powerful stories.

Section 6. Superfluous numbers are like fog. They make it harder to see clearly any message in the numbers. Three numbers are usually plenty. Get rid of the fog of numbers by rounding.

Section 7. Percentages and ratios help. Instead of just making comparisons, we often want to put a number on a comparison. That number is also a fraction or ratio, often expressed as a percentage. Note that there are a couple of traps to avoid!

**This is a simple introduction to working with numbers It includes numbers, fractions, tables, rounding, percentages and ratios. Useful if you have difficulty.**

John MacInnes – *See numbers in data* (2019)

Section 1. Why is most data numerical data? Numerical data is everywhere! We need numbers to help us describe things with detail and precision.

Section 2. What is the average, level or central tendency of data? The average for some data is the simplest summary available of any data, whether it is your income or the number of stars in a galaxy. It tells you about the size of the typical observations in a set of data.

Section 3. What is the spread or dispersion of data? The spread of data concerns whether the data clusters round the average or if the data is more spread out. Finding the spread means we can better understand the variation in our data.

Section 4. How do I understand the data in a graph? Pictures are often better than numbers to describe data. While sizes, shapes and patterns can be intuitive, three questions make understanding any graph easier.

Section 5. What are the five main kinds of graph? Distinguishing different types of graph will help you better understand the numerical data they communicate. Bar charts, histograms, box plots, scatter plots and line charts are the most common types of graph you will encounter.

Section 6. What is scientific notation? Powers and scientific notation allow us to express very small or large numbers without resorting to endless zeroes.

Section 7. How can I use and interpret numbers in data well? Like anything else, numerical data can be used well or badly. Follow these seven simple rules to use and interpret numerical data well.

**This is a good simple introduction, but most of it exists in other books in the series.**

Maureen Hacker – *Choose your statistical test* (2019)

Section 1. Descriptive statistics summarize your data and should be reported in every statistical analysis. Measure frequency, central tendency, and dispersion with descriptive statistics.

Section 2. Three easy questions will help you identify your level of measurement and tell you whether your variable categorizes, ranks, or scales your data. Make sure your conclusions about your data are correct.

Section 3. You can compare groups or draw associations or correlations between variables with inferential statistics. Inferential statistics help you generalize from your sample to the wider population.

Section 4. Paired groups share a common characteristic that allows you to match up participants or scores in different groups. Independent groups, on the other hand, just group your data and you cannot match scores from different groups.

Section 5. Normal distribution is a spread of scores that graphs into a symmetrical, bell-shaped curve. Most data is assumed to be normally distributed, but watch out for data with outliers, skew or kurtosis, or ordinal level data, which break this assumption and require a different kind of statistical test.

Section 6. Walk through this step-by-step process to evaluate variables and data and decide on the most appropriate statistical test to run.

**This is a good introduction. It starts with descriptive statistics: frequency, central tendency and dispersion. A little bit on sampling. And then moves on to inferential statistics for associations – chi squared, Pearson, Spearman – and comparisons – t-test, ANOVA etc. It does not show you how to do it, though.**

John MacInnes – *Know your variables* (2019)

Section 1. Variables are important. Societies, and the people who make them up, are in constant motion. In order to be able to describe and measure them we have to think in terms of variables.

Section 2. Variables describe a feature of a person, organization or society. We cannot ‘see’ them directly, so we measure cases to find the value that each one takes for the variable we are interested in.

Section 3. The more clearly and carefully a variable is defined, the better. The language of variables, values and cases may not always be used explicitly, but to help spot the variable we can ask questions like who, when and where?

Section 4. Since the characteristics that describe a person or organization vary in different ways we have two main kinds of variable. Continuous variables measure characteristics that vary quantitatively, like age. Categorical variables classify cases into different groups or categories, like sex.

Section 5. Examining relationships between variables is the basic building block of all social science. We can examine categorical variables through tables and continuous variables through scatter plots.

Section 6. Often we are interested in how one variable varies with one or more other variables. Even if we have no direct evidence of cause and effect it can be useful to think of one variable as dependent on one or more independent variables.

Section 7. Just because two variables are correlated does not mean one causes the other. While a relationship, correlation or association between two variables is necessary for there to be a relation of cause and effect between them, this is not sufficient evidence.

Section 8. How you use variables will depend on the kind of project you undertake. Qualitative research can use a large number of variables but only a few cases in depth, while quantitative research can use a larger number of cases but with more tightly defined variables.

**A good introduction to variables. It includes what a variable is, and how they are used. It then moves on to different types of variable – continuous and categorical. and finishes with graphs and tables, and dependent and independent variables.**

John MacInnes – *Understand probability* (2018)

Section 1. Probable events may or may not happen. Probable knowledge may or may not be true.Probability is important because all of science consists of probable knowledge. We infer the most probable conclusion given the evidence we have. But that conclusion might be wrong.

Section 2. We can calculate probabilities using trials, outcomes and sample spaces. A trial is any process whose outcome is uncertain. or any question whose certain answer we do not already know. in social science, experiments or social surveys are examples of trials. The sample space comprises all the possible outcomes of a trial.

Section 3. The trickiest part of probability is being able to see and analyse everyday life in terms of the probabilities. The secret is to see things in terms of trials and outcomes. After that, determining probability is just arithmetic.

Section 4. Marginal probability distributions sound technical but are just a way of describing how everyday things vary. Thinking about data in this way allows us to imagine the world as an endless array of trials with indeterminate but predictable outcomes. The addition rule allows us to understand these outcomes.

Section 5. When we look at the way probabilities join together, we can start to understand whether different social phenomena are related or not. They are a stepping stone to conditional probabilities. Conditional probability is a powerful way to describe how different conditions affect the probability of something happening. They bring joint and marginal probabilities together.

**This is a nice introduction to probability but it is not really clear about how it is relevant to the research student.**

John MacInnes – *Statistical significance* (2019)

Section 1. Because populations are so big, we infer population characteristics from samples . Information from samples is not perfect, so we describe potentially interesting results as statistically significant.

Section 2. In a random sample every member of a population has a known chance of being included. Only random samples allow us to estimate the values of population parameters.

Section 3. In a normal distribution, most of the observations are close to the means, and as we move away from it, there are fewer and fewer of people them. A neat formula tells us the proportion of cases near the mean.

Section 4. Sampling distributions make all the estimates we have from random samples possible. From them we can calculate standard errors, which tell us how close to the population parameter a sample estimate is likely to be.

Section 5. Using standard errors, we can identify a range of estimates that we can be confident includes the population parameter we wish to know.

Section 6. When we test a null hypothesis, we get a p-value. If this value is low enough, we reject the null hypothesis and have a statistically significant finding.

Section 7. We can never be 100% sure that any individual result from a sample gives us the right information about a population. The p—values, confidence and significance describe how good our procedures for getting that information have been.

**This is an important book, but – like every introductory book on the subject – it is a mixture of the simple and over complicated. It deals with sampling and the normal distribution. Standard error, P value and confidence intervals are explained well. Not much on how to write about it. However, much of it is written in yellow and I cannot see it.**

Helen Kara – *Use your interview data* (2019)

Section 1. It is important to work systematically with data. This helps to avoid bias and ensure your results are accurate.

Section 2. The first step is to get to know your data. This involves reading and re-reading, thinking, and making notes.

Section 3. You can devise a coding frame from your research question, relevant literature, or your data itself. Then you need to test and refine that question, and make brief notes of your decisions.

Section 4. Now you can use your coding frame with your data! Read your data slowly and carefully, looking for sections that relate to the words and phrases you’ve used. Mark all the relevant sections you find.

Section 5. Emergent coding is created by bringing in all your experience and knowledge in a systematic way. It is more complex than using a coding frame, so it’s best used with smaller datasets.

Section 6. Use your codes to extract ‘slices’ of data for analysis. This will help you to identify categories and themes.

Section 7. Once you have identified themes through your analysis, interpret your analysis to generate findings that make sense of your data. Combine your findings with the context for your research to tell a story to your readers

**Quite good. It distinguishes between devising a coding frame from the literature (deductive) or from the data itself (inductive or emergent coding). It does not say much about how to use your findings in your writing – despite the title of the book. But neither does any other book I know of!**

Helen Kara – *Use your questionnaire data* (2019)

Section 1. Data analysis is the best part of research, because this is where you find out what your data can tell you. It is important to analyse data carefully and honestly and to keep notes of your insights.

Section 2. Data preparation must be meticulous, even though it can be a monotonous task. It is essential to check your prepared data to ensure that it is as accurate as possible.

Section 3. Quantitative data is coded by assigning a number, letter, word or phrase to each piece of data. You also need to address any missing, contradictory, or unsolicited data.

Section 4. Descriptive statistics are straightforward calculations that help you to describe and summarize your data. They include frequencies, percentages, averages, and ranking.

Section 5. Qualitative data analysis uses all your own experience and knowledge in a systematic way. This involves working with codes, categories and themes.

Section 6. Interpreting your data makes your findings accessible to others. You will create a story from your key findings that meets the needs of your audience(s).

**Some useful information. Analysis of quantitative data is descriptive. Some useful analyses of qualitative data – although does not match very well with author’s using your interview data book. A little bit on writing, but needs more.**

Robert Thomas – *Find the theme in your data* (2019)

Section 1. Finding a theme is at the heart of qualitative research and brings together smaller categories or ideas that represent significant trends in your qualitative data.

Section 2. The first thing to do when starting to look for themes is to organize your data and engage with it. You must develop a relationship with your data through transcription.

Section 3. Once you’ve transcribed your data you can start to analyse it initially through a robust reading strategy. Through reading you’ll begin to familiarize yourself with what might be contained within your data.

Section 4. You will need to understand what coding and codes are before you begin a deeper analysis. Codes are the building blocks of your final themes.

Section 5. Now you can begin coding properly. Codes represent the researcher’s first attempts to make sense of their data, so it’s important to know what to look for.

Section 6. Next turn your codes into categories. To begin to form categories, compare all your codes to see if there are relationships between them that might be summed up by a larger category.

Section 7. Once you’ve developed your categories you can start to develop themes. Themes are built on significant and repeated ideas that have been established in your categories. Themes help you answer your research questions, but they need verification.

Section 8. Constructing your themes is sequential but also a very personal process. So, to ensure your themes are relevant, meaningful and representative, it is important that you get your themes and your work overall verified.

**Starts with data transcription. Then coding and categorising. Covers same area as Kara’s books, but they overlap and don’t work together well (Series editor could have done a better job.) There is a little bit about writing but needs more.**

Zina O’Leary – *Present your research* (2019)

Section 1. lt may be hard to believe, but yes, anyone can signiﬁcantly improve their presentation skills. Once you set the right focus and develop self-trust, you will have the ability to become much more impactful.

Section 2. We generally consider what we want to say, but rarely do we consider the impact we want to have. But knowing exactly what you want your audience to think, do or feel differently about, based on your presentation, is crucial.

Section 3. To influence your audience, you need to know your audience. You want to bring them along on your journey and to do this you need to have a sense of who they are.

Section 4. To take your audience from where they are to where you want them to be, you need to ensure they respond to you. One of the most effective ways to do this is to involve them with a compelling storyline.

Section 5. Story can be seen as ‘fluffy’, while data can be dry and boring. The key to effective data presentation is to have data bring credibility to story and have story bring life to data.

Section 6. Being an effective presenter is all about tapping into your best—self qualities. Why lead with nerves, when you can just as easily lead with your innate charm, passion or wit?

**I was disappointed as I hoped it would include written and oral presentations of research, but it only includes oral. A book is therefore missing from the series. Most EAP books can do better on oral presentations (eg Bell, 2008).**

# References

Bell, D. (2008). *Passport to academic presentations*. Reading: Garnet Education.

Ridley, D. (2008). *The literature review: A step-by-step guide for students. London: Sage.*