Lesson Structure: Research Project I

There are 7 lessons:

1  Determining Research Needs

• Introduction
• Identifying research needs
• The research goal
• The research question
• Other questions to clarify the research goal
• Sources of information
• What information is required
• Depth and bredth of data
• Constraining factors: time, resource

2  Searching For Information

• Kind of exploratory research
• Primary data research
• Literary reviews
• Research objectives

3  Research Methods

• Research terminology
• Experimentation
• A controlled environment
• Other field trial considerations
• Steps in collection and analysis of data
• Setting up a comparison trial
• Running the trial
• Evaluating the trial
• Interviewing skills
• Procedure
• Asking questions
• Types of questions
• Ways of handling difficult questions

4  Using Statistics

• Introduction
• Official statistics
• Reasons for using statistics
• Disadvantages of statistics
• Issues to consider
• Descriptive statistics
• Observed and expected rates
• Confidence intervals
• Standardising
• Conducting Statistical Research
• Reliability of statistics
• Presenting statistics: pie charts, bar charts, histograms
• Descriptive statistics: mean, median, mode, variation, standard deviation, etc
• Correlation, Probability

5  Research Reports

• Collecting quantitative data
• Conducting a survey
• Procedure for designing a survey
• Forms of data
• Planning a formal survey
• Designing a questionnaire
• Common problems

6  Research Reporting

• Report writing tips
• Structure of a report
• The report outline
• Research papers
• Referencing

7  Reporting On A Research Project

• A practical project involving construction of a proper research report

Please Note:   Each lesson concludes in an assignment, submitted to your Tutor for marking, and returned with relevant suggestions, comments, and, extra reading where appropriate.

Learning Goals: Research Project I

• Develop your ability to collect, collate and interpret data and prepare reports in ways relevant to the work environment;
• Monitor and evaluate your own work in order to develop a responsible attitude to workplace performance and quality assurance;
• Determine areas where there is a valid need for research which are relevant to area of study;
• Explain research methods, including experimental techniques, commonly used in your discipline.
• Understand of the basic statistical methods used for research;
• Locate, collect and evaluate information for a specific research purpose;
• Prepare a research report in a format which conforms to normal industry procedures.

Planning to do Experimental Research?  –  Some Issues to Consider

Some issues you should consider when planning to do experimental research based on statistical methods are:

• collecting statistics is likely to require technical, as well as general evaluation, expertise. Seek for advice from statisticians in your field of research, as there are many branches in statistics and specialists may not be able to provide the best advice out of their specialisation field.
• some statistics are likely to be provided by other departments and services in your institution; you will need to build a good working relationship with departments such as IT, computing, VLE and e-learning, and possibly work together to define sampling strategies.
• you should allow time to liaise with vendors, researchers, technicians, students, or other personnel in charge of data collection.

Descriptive Statistics

When we talk about using descriptive statistics, we mean that we plan to use statistical tools that describe the data in a way that we can better understand them (what patterns do the data show, if any? how are they distributed? do they cluster in some way?).

Descriptive statistics are used to assess information contained within data by calculating certain summary numbers. They are used to describe the data: For example, mean numbers which provide a ‘central’ number for a set of numbers. They will indicate higher and lower limits, trends, simple relationship between experimental factors.

Descriptive statistics suggests that we are not testing a hypothesis or trying to draw precise conclusions about what the data mean. We are looking at measures of locations, variations, and linear association.

Observed and Expected Rates

Comparing observed rates of disease and other health effects to expected rates is a technique used in many epidemiology studies. Results obtained in samples do not always agree exactly with the theoretical results.

Observed rates are obtained by counting the number of people who experience a certain disease and comparing it to the number of people in the group being observed. Common limitations are difficulties in finding the person or being sure that the presence or absence of disease is always defined the same way.

Expected rates are theoretical calculations based on the knowledge of a diseases common occurrence, previous observed rates, or the expected occurrence due to current factors (unpredicted disease outbreaks).

Confidence Intervals

In epidemiology it is frequently done to estimate the disease rate (the observed rate) from a random subgroup or sample to say something about what to expect in the entire population. Of course, the field of statistics is imprecise just as any science is, and we want to indicate what confidence we have in our estimate.

Scientists commonly report their findings with confidence intervals to express how precise their observed disease rate can be considered.

A confidence interval consists of a number of values that would not be rejected by a specific level in a statistical test. These values are generally significant values in the data.

If we had complete information about every person in the population and their presence or absence of the health effect being counted, our findings would be exact – a single number for the disease rate. As people are different, results we will obtain on the variable under observation will be different.

You don’t get sick as much as your neighbour, or one of your friends, and maybe another of your friends doesn’t get sick at all. Confidence intervals are calculated to know if this variability in the data is due to experimental error or because the population values are spread.

Standardising (by age)

Sometimes we can find a good comparison group with age patterns that are similar to the study group. But, if the groups are not similar with respect to their age patterns, and if we know that age has an effect on the health outcome, scientists can still interpret the findings after standardizing them by age.

Reliability of Statistics

Statistics are to be unbiased – if not, then results are not valid.

The lesson should be clear: bad statistics live on; they take on lives of their own.

Some statistics are based on nothing more than guesses or dubious data. Other statistics become bad after being mangled (as in the case of the Author’s creative rewording).

Either way, bad statistics are potentially important: they can be used to stir up public outrage or fear; they can distort our understanding of our world; and they can lead us to make poor policy choices.

We suspect that statistics may be wrong, that people who use statistics may be “lying”–trying to manipulate us by using numbers to somehow distort the truth. Yet, at the same time, we need statistics; we depend upon them to summarize and clarify the nature of our complex society.

This is particularly true when we talk about social problems. Debates about social problems routinely raise questions that demand statistical answers: Is the problem widespread? How many people–and which people–does it affect? Is it getting worse? What does it cost society? What will it cost to deal with it? Convincing answers to such questions demand evidence, and that usually means numbers, measurements, and statistics.

The results obtained from exploratory research can be influences by a range of factors, such as:

• What were the attitudes at the time?
• Who asked the questions?
• How were they presented? Were the subjects’ answers confidential?
• Could answers somehow be embarrassing to the subject?
• What biases might have affected the subject’s answers.

FREQUENTLY ASKED QUESTIONS

Here is a list of the most often asked FAQ’s.

General

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Q. Assessment – how does it work?
A. For each 100 hour module you are assessed by assignments (at the end of each lesson) and a final one and a half hour exam (or you may elect to complete a Project, instead of sitting the exam) – the choice is yours – you sit for the exam in your own location.

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A. Yes –

Q. How many assignments do I need to complete for each module?
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Q. Can I contact my tutor at any time?
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Q. Practical work – How is this done?
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