You will be expected to use the library and its resources extensively for your research. In order to assist you with using information resources effectively and succeed in your studies, the librarians have designed a course call information literacy (IL).
What is IL?
Have a look at the diagram below:
The IL course will empower you with skills to collect, analyse, organize and critically evaluate information from a variety of sources and use the information appropriately and responsibly. Look out for dates on IL courses on the library notice boards or contact your Subject Librarian.
|Analyzing the topic and formulating a research strategy|
|Find information resources to increase familiarity with your topic|
|Identify keywords and other related words on the topic|
|Critically identify, find and analyze and select relevant information|
|Construct an effective search strategy|
Selecting a research topic
Choosing a unique dissertation topic is a lengthy and important process. Getting it right will mean you enjoy writing this key piece of work and are well on your way to obtaining a great grade. However, rushing into a title simply because it flows well, or seems unusual, can result in a lacklustre essay and many sleepless nights. Start planning your dissertation well in advance, giving you plenty of time for each stage in the process. For most postgraduate qualifications, students need to submit a comprehensive proposal that demonstrates not only original thought, but a sound foundation and the beginnings of thorough research.
How do you think of a unique idea?
Initially, unique dissertation ideas are often the result of a verbal collaboration; this could be between the student and either a friend or tutor. It’s rare that the ideal topic will just turn up in your head – choosing which path to follow is matter of identifying which question can develop your passion for the subject. It should also have further study potential, possibly encompassing a number of sub-questions. A good unique dissertation idea should be enjoyable to write, whilst also giving you a chance to show off your powers of argument and breadth of understanding. The themes covered by your postgraduate course maybe many and varied, so be open to a variety of topics, without losing sight of the ideas which appeal to you personally.
Be realistic in your goals
Next, consider whether you are expert enough in the field to write at the level required. Always set yourself realistic goals, ambition is commendable, but so is handing in a completed piece of work. Spend plenty of time deciding if you have the motivation and time to acquire any new skills, and if this can be done within an allotted timeframe. If not, stick to a topic you feel comfortable and confident with. Remember, the dissertation is an extremely long work, there is always potential for widening or deepening your exploration of the question. So do not be afraid to start with a unique dissertation idea that seems small scale or conventional, so long as the subject has scope, you can diversify and the ideas can be extended.
Do you have the resources to make it a success?
Before committing to a question, consider whether you can research this topic to the necessary standard. You should check what sources are available and how much data can be obtained; starting to write prior to gathering background information may lead to a frustrating dead end. However, finding a few good quality sources is different to having none at all. Try not to let your interest in a marginal topic be dampened by scarcity of information, with enough drive and determination almost any subject can become a success. Past students have investigated ideas as diverse and unusual as the possible existence of unicorns and the significance of Acid House culture, going on to receive a favourable grade from their tutor. Obviously you don’t have to go to those lengths to find a unique dissertation idea, but try to find an interesting topic with the right balance between innovation and workability. Originality is significant so long as you can formulate an effective thesis around your idea.
Don’t be daunted
Although you have written countless essays and participated in many debates, the final dissertation may still be the most daunting aspect of obtaining your postgraduate qualification. Frequently, students will feel overwhelmed at the task ahead, producing 20,000 words of tightly structured, expertly researched academic writing, is undoubtedly a demanding process. However, bear in mind that you earned your place on a postgraduate course and have confidence in your opinions. If you find a unique dissertation idea that gets you thinking and inspires you, this will be obvious in the finished work.
Writing a literature review
A literature review is an account of what has been published on a topic by accredited scholars and researchers. Occasionally you will be asked to write one as a separate assignment (sometimes in the form of an annotated bibliography—see the bottom of the next page), but more often it is part of the introduction to an essay, research report, or thesis. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries
Besides enlarging your knowledge about the topic, writing a literature review lets you gain and demonstrate skills in two areas
Information seeking: the ability to scan the literature efficiently, using manual or computerized methods, to identify a set of useful articles and books
Critical appraisal: the ability to apply principles of analysis to identify unbiased and valid studies.
A literature review must do these things:
Be organized around and related directly to the thesis or research question you are developing
Synthesize results into a summary of what is and is not known
Identify areas of controversy in the literature
Formulate questions that need further research
To understand the use of statistics, one needs to know a little bit about experimental design or how a researcher conducts investigations. A little knowledge about methodology will provide us with a place to hang our statistics. In other words, statistics are not numbers that just appear out of nowhere. Rather, the numbers (data) are generated out of research. Statistics are merely a tool to help us answer research questions. As such, an understanding of methodology will facilitate our understanding of basic statistics.
A key concept relevant to a discussion of research methodology is that of validity. When an individual asks, “Is this study valid?”, they are questioning the validity of at least one aspect of the study. There are four types of validity that can be discussed in relation to research and statistics. Thus, when discussing the validity of a study, one must be specific as to which type of validity is under discussion. Therefore, the answer to the question asked above might be that the study is valid in relation to one type of validity but invalid in relation to another type of validity.
Each of the four types of validity will be briefly defined and described below. Be aware that this represents a cursory discussion of the concept of validity. Each type of validity has many threats which can pose a problem in a research study. Examples, but not an exhaustive discussion, of threats to each validity will be provided. For a comprehensive discussion of the four types of validity, the threats associated with each type of validity, and additional validity issues see Cook and Campbell (1979).
Statistical Conclusion Validity: Unfortunately, without a background in basic statistics, this type of validity is difficult to understand. According to Cook and Campbell (1979), “statistical conclusion validity refers to inferences about whether it is reasonable to presume covariation given a specified alpha level and the obtained variances (p. 41).” Essentially, the question that is being asked is – “Are the variables under study related?” or “Is variable A correlated (does it covary) with Variable B?”. If a study has good statistical conclusion validity, we should be relatively certain that the answer to these questions is “yes”. Examples of issues or problems that would threaten statistical conclusion validity would be random heterogeneity of the research subjects (the subjects represent a diverse group – this increases statistical error) and small sample size (more difficult to find meaningful relationships with a small number of subjects).
Internal Validity: Once it has been determined that the two variables (A & B) are related, the next issue to be determined is one of causality. Does A cause B? If a study is lacking internal validity, one can not make cause and effect statements based on the research; the study would be descriptive but not causal. There are many potential threats to internal validity. For example, if a study has a pretest, an experimental treatment, and a follow-up posttest, history is a threat to internal validity. If a difference is found between the pretest and posttest, it might be due to the experimental treatment but it might also be due to any other event that subjects experienced between the two times of testing (for example, a historical event, a change in weather, etc.).
Construct Validity: One is examining the issue of construct validity when one is asking the questions “Am I really measuring the construct that I want to study?” or “Is my study confounded (Am I confusing constructs)?”. For example, if I want to know a particular drug (Variable A) will be effective for treating depression (Variable B), I will need at least one measure of depression. If that measure does not truly reflect depression levels but rather anxiety levels (Confounding Variable X), than my study will be lacking construct validity. Thus, good construct validity means the we will be relatively sure that Construct A is related to Construct B and that this is possibly a causal relationship. Examples of other threats to construct validity include subjects’ apprehension about being evaluated, hypothesis guessing on the part of subjects, and bias introduced in a study by expectancies on the part of the experimenter.
Populations and Samples
When conducting research, one must often use a sample of the population as opposed to using the entire population. Before we go further into the reasons why, let us first discuss what differentiates between a population and a sample.
A population can be defined as any set of persons/subjects having a common observable characteristic. For example, all individuals who reside in the United States make up a population. Also, all pregnant women make up a population. The characteristics of a population are called a parameter. A statistic can be defined as any subset of the population. The characteristics of a sample are called a statistic.
This brings us to the question of why sample. Why should we not use the population as the focus of study. There are a few major reasons to sample.
One of the reasons to sample is that testing the entire population often produces error. Thus, sampling may be more accurate. Perhaps an example will help clarify this point. Say researchers wanted to examine the effectiveness of a new drug on Alzheimer’s disease. One dependent variable that could be used is an Activities of Daily Living Checklist. In other words, it is a measure of functioning o a day to day basis. In this experiment, it would make sense to have as few of people rating the patients as possible. If one individual rates the entire sample, there will be some measure of consistency from one patient to the next. If many raters are used, this introduces a source of error. These raters may all use some slightly different criteria for judging Activities of Daily Living. Thus, as in this example, it would be problematic to study an entire population.
Another reason to sample is that testing may be destructive. It makes no sense to lesion the lateral hypothalamus of all rats to determine if it has an effect on food intake. We can get that information from operating on a small sample of rats. Also, you probably would not want to buy a car that had the door slammed five hundred thousand time or had been crash tested. Rather, you probably would want to purchase the car that did not make it into either of those samples.
Types of Sampling Procedures
As stated above, a sample consists of a subset of the population. Any member of the defined population can be included in a sample. A theoretical list (an actual list may not exist) of individuals or elements who make up a population is called a sampling frame. There are five major sampling procedures.
The first sampling procedure is convenience. Volunteers, members of a class, individuals in the hospital with the specific diagnosis being studied are examples of often used convenience samples. This is by far the most often used sample procedure. It is also by far the most biases sampling procedure as it is not random (not everyone in the population has an equal chance of being selected to participate in the study). Thus, individuals who volunteer to participate in an exersise study may be different that individuals who do not volunteer.
Another form of sampling is the simple random sample. In this method, all subject or elements have an equal probability of being selected. There are two major ways of conducting a random sample. The first is to consult a random number table, and the second is to have the computer select a random sample.
A systematic sample is conducted by randomly selecting a first case on a list of the population and then proceeding every Nth case until your sample is selected. This is particularly useful if your list of the population is long. For example, if your list was the phone book, it would be easiest to start at perhaps the 17th person, and then select every 50th person from that point on.
Stratified sampling makes up the fourth sampling strategy. In a stratified sample, we sample either proportionately or equally to represent various strata or subpopulations. For example if our strata were states we would make sure and sample from each of the fifty states. If our strata were religious affiliation, stratified sampling would ensure sampling from every religious block or grouping. If our strata were gender, we would sample both men and women.
Cluster sampling makes up the final sampling procedure. In cluster sampling we take a random sample of strata and then survey every member of the group. For example, if our strata were individuals schools in the St. Louis Public School System, we would randomly select perhaps 20 schools and then test all of the students within those schools.
There are several potential sampling problems. When designing a study, a sampling procedure is also developed including the potential sampling frame. Several problems may exist within the sampling frame. First, there may be missing elements – individuals who should be on your list but for some reason are not on the list. For example, if my population consists of all individuals living in a particular city and I use the phone directory as my sampling frame or list, I will miss individuals with unlisted numbers or who can not afford a phone.
Foreign elements make up my second sampling problem. Elements which should not be included in my population and sample appear on my sampling list. Thus, if I were to use property records to create my list of individuals living within a particular city, landlords who live elsewhere would be foreign elements. In this case, renters would be missing elements.
Duplicates represent the third sampling problem. These are elements who appear more than once on the sampling frame. For example, if I am a researcher studying patient satisfaction with emergency room care, I may potentially include the same patient more than once in my study. If the patients are completing a patient satisfaction questionnaire, I need to make sure that patients are aware that if they have completed the questionnaire previously, they should not complete it again. If they complete it more that once, their second set of data respresents a duplicate.
Preliminary decisions in questionnaire design
There are nine steps involved in the development of a questionnaire: