ANALYSIS AND PRESENTATION OF EXPERIMENTAL RESULTS: WITH EXAMPLES, - PDF document

ANALYSIS AND PRESENTATION OF EXPERIMENTAL RESULTS: WITH EXAMPLES, PROBLEMS AND PROGRAMS Download Free Author: Costas Christodoulides, George Christodoulides Number of Pages: 526 pages Published Date: 30 Jun 2017 Publisher: Springer International Publishing AG Publication Country: Cham, Switzerland Language: English ISBN: 9783319533445 Download Link: CLICK HERE

Analysis And Presentation Of Experimental Results: With Examples, Problems And Programs Read Online Path analysis is theoretically useful because, unlike other techniques, it forces us to specify relationships among all of the independent variables. This results in a model showing causal mechanisms through which independent variables produce both direct and indirect effects on a dependent variable. Path analysis was developed by Sewall Wright, a geneticist, in Over time the method has been adopted in other physical sciences and social sciences, including sociology. There are two main requirements for path analysis:. Typically path analysis involves the construction of a path diagram in which the relationships between all variables and the causal direction between them are specifically laid out. When conducting a path analysis, one might first construct an input path diagram , which illustrates the hypothesized relationships. In a path diagram , researchers use arrows to show how different variables relate to each other. After the statistical analysis has been completed, a researcher would then construct an output path diagram , which illustrates the relationships as they actually exist, according to the analysis conducted. Let's consider an example in which path analysis might be useful. Say you hypothesize that age has a direct effect on job satisfaction, and you hypothesize that it has a positive effect, such that the older one is, the more satisfied one will be with their job. A good researcher will realize that there are certainly other independent variables that also influence our dependent variable of job satisfaction: for example, autonomy and income, among others. Using path analysis, a researcher can create a diagram that charts the relationships between the variables. This visual representation of data is called as data visualization. Various methods of data presentation can be used to present data and facts based on available data set. Widely used format and data presentation techniques are mentioned below:. Data presentation and analysis plays an essential role in every field. An excellent presentation can be a deal maker or deal breaker. Some people make an incredibly useful presentation with the same set of facts and figures which are available with others. At times people work really hard but fail to present it properly and have lost essential deals. The work which they did was unable to impress the decision makers. So to get the job done, especially while dealing with clients or higher authorities, Presentation Matters! No one is willing to spend hours in understanding what you have to show and this is precisely why presentation matters! It is thus essential to have a clarity on what is data presentation. Some of the factors which directly affects the data presentation include data quality, correlation coefficient,

vector images, colour scheme etc. Data analysis helps people in content analysis and understanding the results of surveys conducted, makes use of already existing studies to obtain new results. These two go hand in hand, and it will be difficult to provide a complete differentiation between the two. Adding visual aspect to data or sorting it using grouping and presenting it in the form of table is a part of the presentation. Doing this further helps in analyzing data. During a study with an aim and multiple objectives, data analysis will be required to complete the required objectives. Compiling or presenting the analyzed data will help in overall analysis and concluding the study. You can have a variety of data which can be used in presentations. Some of these have been described in brief with an example at the end of this article. Steps for Presenting and Analyzing Data:. Presentation of Data:. The presentation can be done using software such as Microsoft Power Point, Prezi, Google Analytics and other analytic software. It can also be done by making models, presenting on paper or sheets, on maps or by use of boards. The methods selected depends on the requirement and the resources available. Since there are number of options available while presenting data, careful consideration should be given to the method being used. Analysis And Presentation Of Experimental Results: With Examples, Problems And Programs Reviews The Presentation of Numerical Results. The Propagation of Errors. The Three Basic Probability Distributions. The Statistics of Radioactivity. Elements from the Theory of Errors. Comparison and Rejection of Measurements. The Method of Least Squares. The Written Report of the Results of an Experiment. Back Matter Pages About this book Introduction This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical methods and the theory of errors. After presenting basic theoretical concepts, the book describes the methods by which the results can be presented, both numerically and graphically. The book is divided into three parts, of roughly equal length, addressing the theory, the analysis of data, and the presentation of results. Also consider Analyzing Data and Communicating Results. Ideally, the organization's management decides what the research goals should be. Then a research expert helps the organization to determine what the research methods should be, and how the resulting data will be analyzed and reported back to the organization. If an organization can afford any outside help at all, it should be for identifying the appropriate research methods and how the data can be collected. The organization might find a less expensive resource to apply the methods, e. If no outside help can be obtained, the organization can still learn a great deal by applying the methods and analyzing results themselves. However, there is a strong chance that data about the strengths and weaknesses of a product, service or program will not be interpreted fairly if the data are analyzed by the people responsible for ensuring the product, service or program is a good one. These people will be "policing" themselves. This caution is not to fault these people, but rather to recognize the strong biases inherent in trying to objectively look at and publicly at least within the organization report about their work. Therefore, if at all possible, have someone other than the those responsible for the product, service or program to look at and determine research results. Ensure your research plan is documented so that you can regularly and efficiently carry out your research activities. In your plan, record enough information so that someone outside of the organization can understand what you're researching and how. For example, consider the following format:. To round out your knowledge of this Library topic, you may want to review some related topics, available from the link below. Each of the related topics includes free, online resources. Also, scan the Recommended Books listed below. They have been selected for their relevance and highly practical nature. Related Library Topics. By continuing to use this site, you agree to our Privacy Policy. Always start with your research goals When analyzing data whether from questionnaires, interviews, focus groups, or whatever , always start from review of your research goals, i. Basic analysis of "quantitative" information for information other than commentary, e. Use the copy for making edits, cutting and pasting, etc. Tabulate the information, i. For ratings and rankings, consider computing a mean, or average, for each question. For example, "For question 1, the average ranking was 2. This is more meaningful than indicating, e. Consider conveying the range of answers, e. Basic analysis of "qualitative" information respondents' verbal answers in interviews, focus groups, or written commentary on questionnaires : Read through all the data. Organize comments into similar categories, e. Label the categories or themes, e. Attempt to identify patterns, or associations and causal relationships in the themes, e. Keep all commentary for several years after completion in case needed for future reference. About Analysis And Presentation Of Experimental Results: With Examples, Problems And Programs Writer Они обернулись. Создатель последнего шифра, как это выглядит. Беккер понимал, ваша интуиция на сей раз вас обманула.