Purpose-of-exploratory-research.docx 331x16

Exploratory Research  Exploratory studies are also referred to as formulative research studies.  The primary objective or purpose of exploratory research design is that of formulating a problem for more precise investigation or of developing the working hypothesis from an operational perspective.  The main focus in these research projects is on the discovery of ideas and insights.  Therefore the research design, suitable for these types of studies must be flexible enough to offer opportunity for considering various elements of a problem under study.  In built flexibility in research design is required as the research problem, broadly defined initially, is turned into one with more precise meaning in exploratory studies, which fact may necessitate alterations in the research process of gathering relevant data.

 Information regarding the Immediate Conditions: The design offers information relating to the conditions of the problem. When the researcher doesn’t have resources and capacity to test the hypothesis he is in a position to discover facts through exploratory design that is appropriate to or in compliance with the hypothesis.  Presentations of Crucial Issues: Through exploratory and formulative designs, you’ll be able to present crucial research problems. When the problems

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have been presented, the researcher is automatically drawn towards the study of the difficulty which has higher significance for our society.  Study of the Unknown Fields: For research, theory or hypothesis is unavoidable. They offer appropriate basis. To be able to formulate a hypothesis, we will need to obtain the related information and through exploratory design this task is accomplished.  Theoretical Base: The research problem relates to our social life and social problems and data about the subject could only be gathered through exploratory design. This design is useful in offering a theoretical base to the hypothesis and theories.  Presentation of uncertain problem for study in Research: Through exploratory designs we can easily figure out these problems. This process on the one hand , focuses the attention of the researcher on the problem and, on the other, it assists him to gather facts on scientific lines to ensure that research may be completed correctly.  Exploratory studies are usually conducted when a problem is not clearly defined.  It allows the investigator to acquaint with the problem or concept to be researched, and possibly produce hypotheses (definition of hypothesis) to be tested.  In many cases, exploratory research is carried out by the use of focus groups or small group discussions, which are frequently utilized in researching the market.  Exploratory studies can be hugely useful for social research.  They are important when an investigator is breaking new ground and they usually deliver new information about a topic for research.  They’ve also been a source for grounded theory.

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Descriptive Research  Descriptive research can be explained as a statement of affairs as they are at present with the researcher having no control over variable.  Moreover, “descriptive research may be characterised as simply the attempt to determine, describe or identify what is, while analytical research attempts to establish why it is that way or how it came to be”[1]. Ethridge, D.E. (2004) “Research Methodology in Applied Economics” John Wiley & Sons, p.24  Descriptive research is “aimed at casting light on current issues or problems through a process of data collection that enables them to describe the situation more completely than was possible without employing this method”[2] Fox, W. & Bayat, M.S. (2007) “A Guide to Managing Research” Juta Publications, p.45  In its essence, descriptive studies are used to describe various aspects of the phenomenon. In its popular format, descriptive research is used to describe characteristics and/or behaviour of sample population.  Descriptive research can employ a number of variables; only one variable is required to conduct a descriptive study. Three main purposes of descriptive studies can be explained as describing, explaining and validating research findings.  Descriptive studies are closely associated with observational studies, but they are not limited with observation data collection method, and case studies, as well as, surveys can also be specified as popular data collection methods used with descriptive studies. Advantages of Descriptive Research  Effective to analyse non-quantified topics and issues  The possibility to observe the phenomenon in a completely natural and unchanged natural environment  The opportunity to integrate the qualitative and quantitative methods of data collection

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Disadvantages of Descriptive Research  Descriptive studies cannot test or the research problem statistically  Research results may reflect certain level of bias due to the absence of statistical tests  The majority of descriptive studies are not ‘repeatable’ due to their observational nature Hypothesis testing Definition  In this lesson, we will talk about what it takes to create a proper hypothesis test. We define hypothesis test as the formal procedures that statisticians use to test whether a hypothesis can be accepted or not.  A hypothesisis an assumption about something. For example, a hypothesis about family pets could be something like the average number of dogs per American household is two.  Hypothesis testing is about testing to see whether the stated hypothesis is acceptable or not. During our hypothesis testing, we want to gather as much data as we can so that we can prove our hypothesis one way or another. There is a proper four-step method in performing a proper hypothesis test:  Write the hypothesis  Create an analysis plan  Analyze the data  Interpret the results Step One: Hypothesis The first step is that of writing the hypothesis. You actually have two hypotheses to write. One is called the null hypothesis. This is the hypothesis based on chance. Think of this as the hypothesis that states how you would expect things to work without any external factors to change it. The other hypothesis is called 4

the alternative hypothesis. This is the hypothesis that shows a change from the null hypothesis that is caused by something. In hypothesis testing, we just test to see if our data fits our alternative hypothesis or if it fits the null hypothesis. We don't worry about what is causing our data to shift from the null hypothesis if it does. Keep in mind, when writing your null hypothesis and alternative hypothesis, they must be written in such a way so that if the null hypothesis is false, then the alternative hypothesis is true and vice versa. What does Sam do here? Sam's null hypothesis is that all meat that is sold to supermarkets is less than 48 hours old. Sam's alternative hypothesis is that all meat that is sold to supermarkets is more than 48 hours old. As you can see, if the null hypothesis is false, then the alternative hypothesis is true. Step Two: Analysis Plan The second step is to create an analysis plan. This involves deciding how to read your results to know whether your null hypothesis is true or your alternative hypothesis is true. Usually, this involves analyzing just one single test statistic. There are two ways to read your results: P-value method and the region of acceptance method. The P-value is the probability of observing the desired statistic. If this P-value is less than the significance level, then the null hypothesis is not valid. The significance level is the probability of making the mistake of saying that the null hypothesis is not valid when it actually is true. The region of acceptance is a chosen range of values that results in the null hypothesis being stated as valid. For this step, Sam decides to analyze his data using the region of acceptance. The statistic that Sam decides to use is the number of hours the meat is at that is being sold to supermarkets. Sam goes to various meat providers and checks to see the age of the meat that is being sold. He then analyzes this statistic to see how many meat providers are shipping meats out fewer than 48 hours. The region of acceptance is 99% or higher. This means that if 99% or more of the meat producers ships out their meat in time, then the null hypothesis is valid.

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Step Three: Data Analysis The third step is that of analyzing the data. It is the putting step two into action. It is in this step that the data is analyzed and either a P-value is found, or the data's region is found. It is in this step that Sam checks his data to see how many of his meat producers are shipping out their meats within 48 hours. Sam looks at his data and sees that 99.9% of the meat producers are shipping out their meats within 48 hours. Step Four: Interpretation The fourth step involves interpreting the results. It is in this step that the data is compared to the region of acceptance or the significance level. If the P-value is less than the significance level, then the null hypothesis is not valid. If the data is within the region of acceptance, then the null hypothesis is valid. Sam looks at this data. His data shows that the data's region is at 99.9%. He compares it to his acceptable 99%. Is 99.9% higher than 99%? It is. This means that his data is within the region of acceptance. This tells Sam that he can say that the null hypothesis is valid. Now, he has the data to prove his null hypothesis statement. This is what he wanted to happen. He wanted to be able to tell people that his meat producers are shipping out fresh meat that is less than 48 hours old.

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