Research Methods for Managers - Coursework Example

Research Methods for Managers Research Methods for Managers Qualitative Research Project Companies and individuals take projects to either venture into new disciplines or improve on the existing ones. Consequently, the quality of a research project is a key concern when any project is at stake. For researchers to carry out a quality project, they have to adhere to some few specific aspects of the projects, which determine the value of the research paper. Initial step into is to have an excellent introduction that capture the issue at the core of the project. The background information should provide an insight into the issue. The exact problem at hand needs highlighting and showing whether the issue is like a gap or a discovery or an improvement of the existing ones. The solution expected needs clarity because that will be the focus of the experiment. Methods of data collection determine the outcome of the project. Therefore, quantitative and qualitative data requires careful analysis. Aspects such the interviews should be of high integrity to validate the research. Additionally, good ethical practices are necessary during the process of doing the research. The researcher should avoid biased opinion, and confidentiality of the people interviewed is key to validating a project. Simulation: Observations as a Source of Data Direct observation is a primary source of information that involves the researcher taking part in collecting the data individually. For example, if the data about the culture of a particular group of people is the subject of the topic, then the researcher can live with the people while observing the culture and recording the data. Observation as a data source has advantages such as giving the researcher a direct access to the information needed. Additionally, the method is simple to use and offers permanent data in the mind of a scientist because personal experiences are not easy to erase from one’s mind. Nevertheless, the method is flexible and diversified in that it can be formal or informal, which makes the researchers find diversity in its use. However, the process faces critics in its credibility because it is subjective which makes it exposed to biases from the researcher. The method also consumes a lot of time and resources hence derailing the research process. Despite its anomalies, the observation remains the widely used and relatively reliable data sources in most research. Therefore, it supplements other data collection methods. Simulation: Brainstorming Solutions Based on Data Brainstorming over an issue makes the researchers get more information about the research topic that may enable them to make informed choices when carrying out analysis based on existing data. In conducting any research one needs to get the opinion of other scholars who may have done the same or related research. Research on projects depend primarily on the existing paradigms, that is, any research must have a scientific basis to draw the principle of argument. Getting more information helps the people doing research to compare the data they have with what other scholars have. The importance of this comparison is to help minimize the effect of errors that may accrue due to statistical error or procedural error, which may affect the outcome of research data. Some vagaries that may accompany data collection process during the research have the effect of making the researchers generate wrong data, some of which can be very unrealistic. Therefore, brainstorming is as a benchmark for the researchers to check which direction they are heading. However, too much brainstorming may result in biases because the research may continue with the research process having skewed thinking. Furthermore, it may limit the possibility of discovery because data outlier falls on the side of the researcher. Simulation: Testing out Ideas in the Field Nearly all research projects have a basis of practicality in that upon completion the findings should applicable in daily lives of people. As a result, the idea or thought needs field-testing to test its feasibility and impact on humanity. There are procedures for testing the ideas in the field such as pilot testing or getting test subjects. The success of any test depends on its design, which must be compatible with the topic of study, the research question and paradigm of thinking. Paradigms such as positivism and interprets exists to provide the basis for test design. However, when researchers are in the field, the test design may fail because of environmental change. Therefore, researchers have the mandate to modify the design creatively in such a way that the scientist basis still exists and no distortion on the aim of the test. Design modification in the field illustrates how researchers should be versatile and flexible in dealing with project constraints. The type of testing plans depends on the cost, time available and the research personnel. Overall, the nature of measurement model mostly affects the outcome of the project. Designing Experiments In experiments, the observation verifies the effect of changing one or more variables one or more response variable. Therefore, the statistical experimental design is an operation for planning experiments to allow for analysis of the data at hand to produce valid and objective conclusions. The experimental design primarily starts with determining the objectives of the analysis and identifying the process factors for the project study. There are pre-tests design, the control group design and assessing the participants randomly. The pre-test design dictates that the researcher collects data on the study participants degree of performance prior to intervention and collection of the same data in a study where the participants after the process. The design ensures the complete experimental process had a casual effect. The control group design maintains things as usual in the test by minimizing the errors that accrue to an experimental sampling. Evidently, the sampling methods do not perfectly eliminates the errors due to the fact they only provide a fair representation of the data population. Therefore, control group design removes any biases whatsoever and set a healthy environment for the entire experimental procedure. Good experimental designs largely determine the quality of the outcome in a research paper hence a key concern. Collecting Quantitative Data During the research process, there are many quantitative data collected to assist in the analysis of the research theory. However, not all the information is used in the analysis process. Therefore sampling helps the researcher in selecting the population to use. Sampling involves methodological or random selection of the fair representation of the data population to help in the analysis. Some of the standard sampling methods include random sampling, stratified sampling and convenience sampling. Under random sampling, the selection of a representative population of the data followed no probability pattern but done relatively fair to ensure most of the population characteristics have adequate representation. The stratified method follows a methodological pattern in selecting the representative population for use in the experimental analysis. On the contrast, convenience sampling involves selecting part of data population depending on how convenient they are such as their closeness to the expected value of the size of the value, which allows easy analysis. Quality research project depends on the sampling method because that is the starting point of project analysis and anything that tampers with the fair representation of data affects the experiment. Hence, careful consideration is necessary during the sampling stage. Analyzing Quantitative Data (Descriptive Statistics) Descriptive statistics is the analysis that assists in describing or summarizing the data in a meaningful way manner that may help generate understandable data patterns. The analysis only permits the researcher to make conclusion within the boundaries of the data at hand but not beyond it. The analysis allows presentation data in a way the concerned parties will get the information quickly and make simple interpretation even if they did not participate in the research. Therefore, descriptive statistics takes two forms, that is, a measure of the central tendency and measure of the data spread. Central tendency measures are the ways of illustrating the central position of a frequency distribution of the data population. The action takes the form of statistics such as the mode, median and mean. On the other hand, measures of spread summarize the data group by describing the distribution of the score. The analysis involves operations such as the range, quartiles, variance standard deviation and other deviations. The researcher is no supposed to use any of the two measures in isolation because they supplement each other to help in getting a good and quality research analysis. Analyzing Quantitative Data (Inferential Statistics) Inferential statistics intends to solve the deficits of the descriptive statistics. Inferential analysis method allows the researcher to use the samples to make generalization concerning the original population from which sample data originated. Inferential statistics is necessary because of the mere fact that sampling encounters with the error of non-perfect representation of the entire data population. Therefore, the method employs the use of the entire data population in its evaluation. The methods take the form of statistical operations such as estimation of parameters and statistical hypothesis testing. Hypothesis testing verifies whether the sample of the data concurs with the suggestions made, that is, null or alternative. Additionally, some rejection rules or conditions exist for treating a particular set of data. During the learning session a significant difference was made between inferential statistics and descriptive statistics. While descriptive statistics apply to populations and their properties, such as standard deviation and mean inferential statistics are methods that allow for the use of sample accurately in making generalizations about populations. Therefore, the two aspects may appear related but they bear significant variations based on their application and methodology. Read More