Initial - Middle Phase
In this section JMEIT will provide the information about the initial and middle phase of your research work. When a researcher wants to introduce himself in the field of research work, he might know the basics of the research work. First of all decide the area in which researcher want to peruse his research like Management, Engineering etc. then narrow down the path to the sub topic of the research like the subject specific for e.g. in Management the area of specialization etc. however, there are many things need to know, like what should be the topic, introduction to the topic, statement of the problem etc. a brief of each part is essential to know to develop the whole picture of research work.
The strict definition of scientific research is performing a methodical study in order to prove a hypothesis or answer a specific question. Finding a definitive answer is the central goal of any experimental process. Research must be systematic and follow a series of steps and a rigid standard protocol. These rules are broadly similar but may vary slightly between the different fields of science.
This should include the precise subject of the dissertation and end with a sentence that states what study will accomplish.
Background of the Problem / Problem sensing
The section is a brief two to four page summary of the major findings in the field of interest that cites the most current finding in the subject area. A minimum of two to three citations to the literature to state about the unresolved issues or work done earlier or the gap in the past and the present literature.
Statement of the Problem
From the background statement is this statement of the exact gap in the research area. The gap in the research part is the entire reason for the study, so state it specifically and exactly. The whole procedure will depend and revolve the problem statement
Purpose of the Study
The Purpose of the Study is a statement contained within one or two paragraphs that identifies the research design, such as qualitative, quantitative, mixed methods, ethnographic, or another design.
Significance of the Study
The significance is a statement of why it is important to determine the answer to the gap in the knowledge, and is related to improving the human condition. The contribution to the body of knowledge is described, and summarizes who will be able to use the knowledge to make better decisions, improve policy, advance science, or other uses of the new information. The “new” data is the information used to fill the gap in the knowledge.
Primary Research Questions
The primary research question is the basis for data collection like interview questions in a qualitative study, or survey questions in a quantitative study and it give the basis for the Purpose of the Study then the study are followed by both a null and an alternate hypothesis.
A hypothesis is a testable prediction for an observed phenomenon, namely, the gap in the knowledge. Each research question will have both a null and an alternative hypothesis in a quantitative study.
In this section the methodology and contains a brief outline of three things: (a) the participants in a qualitative study or the subjects of a quantitative study (human participants are referred to as participants, non-human subjects are referred to as subjects), (b) the instrumentation used to collect data, and (c) the procedure that will be followed.
The theoretical framework is the foundational theory that is used to provide a perspective upon which the study is based. There are hundreds of theories in the literature.
Assumptions, Limitations, and Scope (Delimitations)
Assumptions are self-evident truths. In a study, it may be assumed that participants will answer truthfully and accurately to the interview questions based on their personal experience, and that participants will respond honestly and to the best of their individual abilities.
Limitations of a study are those things over which the research has no control. Evident limitations are potential weaknesses of a study. Researcher biases and perceptual misrepresentations are potential limitations in a study; human typographic errors etc.
Scope is the extent of the study and contains measurements. In a study this would include the number of participants, the geographical location, and other pertinent numerical data.
Delimitations are limitations on the research design imposed deliberately by the researcher. Delimitations in a social sciences study would be such things as the specific school district where a study took place, or in a scientific study, the number of repetitions.
Definition of Terms
The definition of terms is written for knowledgeable peers, not people from other disciplines.
Parts of a Research Paper :
The principles for literature review and essay of all types follow the same basic principles.
• Conclusion / Suggestions
• Reference List
Null hypothesis In statistical inference of observed data of a scientific experiment, the null hypothesis refers to a general statement or default position that there is no relationship between two measured phenomena.
Rejecting or disproving the null hypothesis – and thus concluding that there are grounds for believing that there is a relationship between two phenomena or that a potential treatment has a measurable effect – is a central task in the modern practice of science, and gives a precise sense in which a claim is capable of being proven false.
Based on survey results, this hypothesis will be either accepted as correct or rejected as incorrect.
In probability theory, the normal (or Gaussian) distribution is a very commonly occurring continuous probability distribution—a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. Normal distributions are extremely important in statistics and are often used in the natural and social sciences for real-valued random variables whose distributions are not known.
Correlation and regression analysis are related in the sense that both deal with relationships among variables. The correlation coefficient is a measure of linear association between two variables. Values of the correlation coefficient are always between -1 and +1. A correlation coefficient of +1 indicates that two variables are perfectly related in a positive linear sense, a correlation coefficient of -1 indicates that two variables are perfectly related in a negative linear sense, and a correlation coefficient of 0 indicates that there is no linear relationship between the two variables. For simple linear regression, the sample correlation coefficient is the square root of the coefficient of determination, with the sign of the correlation coefficient being the same as the sign of b1, the coefficient of x1 in the estimated regression equation.
Neither regression nor correlation analyses can be interpreted as establishing cause-and-effect relationships. They can indicate only how or to what extent variables are associated with each other. The correlation coefficient measures only the degree of linear association between two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.
Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables. A model of the relationship is hypothesized, and estimates of the parameter values are used to develop an estimated regression equation. Various tests are then employed to determine if the model is satisfactory. If the model is deemed satisfactory, the estimated regression equation can be used to predict the value of the dependent variable given values for the independent variables.
DEFINITION of 'Sampling'
A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population. The methodology used to sample from a larger population will depend on the type of analysis being performed, but will include simple random sampling, systematic sampling and observational sampling.
The sample should be a representation of the general population.
A chi-squared test, also referred to as test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Also considered a chi-squared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-squared distribution as closely as desired by making the sample size large enough. The chi-square (I) test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. Do the number of individuals or objects that fall in each category differ significantly from the number you would expect? Is this difference between the expected and observed due to sampling variation, or is it a real difference?