Need a Topic/Title and Research Proposal outline in Power Point format.

Learning objectives:

· Define Quantitative Research

· Learn the methods of data collection in Quantitative Research

· Explain key terms related to Quantitative Research

1.1 What is Quantitative Research?

Researchers should begin by asking themselves the following questions:

· What type of question am I asking?

· What type of data will I need to collect to answer the question?

· What type of results will I report?

For instance, a researcher may want to explore the association between income and whether or not families have health insurance. This is a question that asks “how many” and seeks to confirm a hypothesis. Hence, the methods will be highly structured and consistent during data collection (e.g. a questionnaire with closed-ended questions). The results will generate numerical data that can be analyzed statistically as the researcher looks for a correlation between income and health insurance. This is an example where quantitative research should be applied. A quantitative approach will allow the researcher to test the relationship between the two factors (i.e. income and health insurance). The data can be also used to look for cause and effect relationships and therefore, can be used to make predictions.

On the other hand, another researcher might be interested in exploring the reasons that people choose not to have health insurance. This researcher is interested in the various reasons why people make that choice and what the possible barriers may be when people choose not to get insurance. This is an open-ended question that will not provide results that can be statistically analysed. Qualitative methodology would best apply to this research problem.

Examples of research questions:

Are females more likely to be teachers than males?

Is the proportion of males who are teachers the same as the proportion of females?

Is there a relationship between gender and becoming a teacher?

In the example above, you can see that there are different ways of approaching the research problem, which is concerned with the association between males and females in teaching.

Data Collection is an important part of any type of research study. Inaccurate data collection can influence the results of a study and ultimately lead to invalid results.

Sources of Quantitative Data (Leedy and Ormrod, 2001):

The most popular sources of quantitative data include:

· Experiments/clinical trials.

· Observing and recording well-defined events. These may either involve counting the number of times that a particular phenomenon/behavior occurs (e.g. how often a specific word is used in interviews, counting the number of patients waiting in emergency at specified times of the day), or coding observational data to translate it into numbers and secondary data (e.g. company accounts).

· Obtaining relevant data from management information systems.

· Administering online, phone or face-face surveys with closed-ended questions. These require that the same questions are asked in the same way to a large number of people.

Prior to designing a quantitative research study, researchers needs to decide whether it will be descriptive or experimental, as this will specify how they gather, analyze, and interpret the results. A descriptive study is based on three basic rules: 1) subjects are usually measured once 2) the intention is to merely establish associations between variables and 3) the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes: 1) subjects measured before and after a particular treatment 2) the sample population may be very small and purposefully chosen, and 3) it is intended to establish causality between variables. Quantitative researchers try to identify and isolate specific variables involved within the study framework, seek correlation, relationships and causality, and take actions to control the environment in which the data is gathered to avoid the risk of other variables, besides the one being studied, accounting for the relationships identified.

(Earl,2010):

· Enhances the generalization of the results, as it allows for broader studies to be contacted, involving a greater number of people.

· Increased objectivity and accuracy of results. Quantitative methods are typically designed to provide summaries of data that can be generalized. In order to accomplish this, quantitative studies usually involve few variables and many cases, and uses prescribed techniques to ensure validity and reliability.

· Applying well established standards allows for replication, and the comparison with similar studies

· Allows for summarizing a vast amount of information and making comparisons across categories and over time

· It decreases personal bias can by keeping a 'distance' from participants and using established computational techniques

1.6 Some limitations associated with using quantitative methods include (Earl, 2010):

· Although quantitative data is more efficient and allows to test hypotheses, it can miss contextual detail

· Uses a static and rigid approach, and hence employs a process of discovery that tis not very flexible

· There is high risk for "structural bias" and false representation due to the development of standard questions by researchers (i.e. the data actually reflects the view of the researcher instead of the participant)

· Results provide less detail on behavior, attitudes, and motivation

· Researcher may collect a dataset that is much narrower and sometimes superficial

· Results provide only numerical descriptions (but not detailed narrative) and less elaborate accounts of human perception

· The research is usually conducted in an unnatural, artificial environment (i.e. laboratory) to increase the level of control applied to the exercise. However, this level of control might not normally be applied in real world settings thus providing "laboratory results" as opposed to "real world results"

· Preset answers will not necessarily reflect how people really feel about a topic and, in some cases, might just be the closest match to the preconceived hypothesis.

1.7 Quantitative Data (Abramson & Abramson 2008):

Before analyzing quantitative data, researchers must identify the level of measurement associated with the quantitative data. The level of measurement can affect the type of analysis that will be used. There are four levels of measurement:

· Nominal data: Data has no logical order. It is basic classification data Example: Male or Female

There is no order associated with male or female

· Ordinal data: Data has a logical order, but the differences between values are not constant Example: T-shirt size (small, medium, large)

Example: Military rank (from Private to General)

· Interval data: Data is continuous and has a logical order, data has standardized differences between values, but no natural zero

Example: Fahrenheit degrees

Remember that ratios are meaningless for interval data. You cannot say, for example, that one day is twice as hot as another day.

· Ratio (scale): data is continuous, ordered, has standardized differences between values, and a natural zero

Example: height, weight, age, length

Having an absolute zero allows you to meaningful argue that one measure is twice as long as another. For example, 10 inches is twice as long as 5 inches

Remember that there are various ways of approaching a research question and how the researcher puts together a research question will determine the type of methodology, data collection method, statistics, analysis and presentation that will be used to approach the research problem.

In another research problem the relationship between gender and smoking is explored. In this case there are two categorical variables (i.e. gender and smoker), with two or more groups in each. For example:

· Gender (male/female)

· Smoker (yes/no)

The researcher investigates whether or not there is a significant relationship between these variables.

1.8 Variables:

An experiment has three characteristics:

1. A manipulated independent variable (often denoted by x, whose variation does not depend on that of another).

2. Control of other variables i.e. dependent variables (a variable often denoted by y, whose value depends on that of another.

3. The observed effect of the independent variable on the dependent variables.

In science, the term observer effect means that the act of observing will influence the phenomenon being observed.

Example of Variables in Scientific Experiments

If a scientist conducts an experiment to test the theory that a vitamin could extend a person’s life-expectancy, then:

The independent variable is the amount of vitamin that is given to the subjects within the experiment. This is controlled by the experimenting scientist.

The dependent variable, or the variable being affected by the independent variable, is life span.

Table 1.

Key terms associated with quantitative research (Field, 2013)

Hypothesis/Null hypothesis:

A hypothesis is a logical assumption, a reasonable guess, or a suggested answer to a research problem.

A null hypothesis states that minor differences between the variables can occur because of chance errors, and are therefore not significant.

*Chance error is defined as the difference between the predicted value of a variable (by the statistical model in question) and the actual value of the variable.

In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is incorrectly retaining a false null hypothesis (a "false negative"). Simply, a type I error is detecting an effect (e.g. a relationship between two variable) that is not present, while a type II error is failing to detect an effect that is present.

Randomised, controlled and double-blind trial:

Randomised - chosen by random.

Controlled - there is a control group as well as an experimental group. Double-blind - neither the subjects nor the researchers know who is in which group.

Creswell, J. W. (2002). Educational research: Planning, conducting, and evaluating quantitative. Prentice Hall.

Earl B.R. (2010). The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth Cengage.

Kultar, S. (2007). Quantitative Social Research Methods. Los Angeles, CA: Sage