UNIT-V
RESEARCH REPORT
A report is a detailed description of what has been done and how it has been done with respect to particular area or topic. The purpose of the written report is to present the results of your research, but more importantly to provide a persuasive argument to readers of what you have found. It is the end product of a research activity. It is highly skilled work it is the final stage of the research work.
Need For Research Report
knowledge.
principle.
or thoughts.
action.
The research ability of a candidate is revealed through the final report he presents.
and others.
Functions of Research Report
r presenting the problem studied, methods and techniques used, findings, conclusions and recommendation in an organized manner.
related area.
f the research project.
research ability.
analyzed.
Qualities of a Good Report
• Clarity
• Continuity
• Consistency
• Brevity
• Readability
• Interest and Appeal
• Judicious Selection of Materials
• Avoiding personal opinion
• Concentrate on Central Ideas
• Proper Reference Steps in Report Writing
They are to be completed through a number of steps.
• Plan the project in advance; fix the target and final date of completing the report.
• The time of report writing should be planned in advance.
• Arrange the data, documents, bibliography etc. in conformity with the structure of the report.
• The outline should be based on all main points and sub points.
• Prepare a rough report of what one has done in his studies. He has to write down the procedure adopted by him in collecting the material, the technique or analysis adopted by him, the broad findings and generalizations and his suggestions.
• Keep the rough report for few days for careful reading and then revising it on the basis of thinking and discussing with others. It is appropriate to get help of some experienced and knowledgeable person at this stage.
• Rewrite the report on the basis of the revision made and corrections effected on the report.
• Prepare final bibliography. Bibliography may contain two parts, first containing name of the books and pamphlets, second containing the names of magazines and newspaper articles.
• The last step in report writing is the writing of a final draft of the report. The final draft should be written in a concise and objective style and in simple language.
Parts/Components of A Research Report 1. Prefatory Items
2. Introductory Part
3. Body/Text/Content/Results of the Work 4. Concluding Part/End Items/Terminal Items I Prefatory Items
1. Title Page
2. Researcher’ s Declaration
3. Certificate of the Research Guide (and Head of the Dept. in the case of Project) 4. Acknowledgements
5. Contents
6. List of Abbreviations 7. List of Tables
8. List of Figures
9. List of Appendices/Appendixes Test of Hypotheses
‘z’ Test
Z-test is a type of hypothesis test—a way for you to figure out if results from a test are valid or repeatable. Several different types of tests are used in statistics (i.e. f test, chi square test, t test).
You would use a Z test if:
Your sample size is greater than 30. Otherwise, use a t test.
Data points should be independent from each other. In other words, one data point isn’t related or doesn’t affect another data point.
Your data should be normally distributed. However, for large sample sizes (over 30) this doesn’t always matter.
Your data should be randomly selected from a population, where each item has an equal chance of being selected.
Sample sizes should be equal if at all possible.
‘t’ Test
A t-test is a statistical test that is used to compare the means of two groups. It is often used in hypothesis testing to determine whether a process or treatment actually has an effect on the population of interest, or whether two groups are different from one another.
When to use a t-test
A t-test can only be used when comparing the means of two groups. If you want to compare more than two groups, or if you want to do multiple pairwise comparisons, use an ANOVA test or a post-hoc test. The t-test is a parametric test of difference, meaning that it makes the same assumptions about your data as other parametric tests. The t-test assumes your data:
1. are independent
2. are (approximately) normally distributed.
3. have a similar amount of variance within each group being compared.
‘f’ Test
An “F Test” is a catch-all term for any test that uses the F-distribution. In most cases, when people talk about the F-Test, what they are actually talking about is The F-Test to Compare Two Variances. However, the f-statistic is used in a variety of tests including regression analysis, the Chow test and the Scheffe Test (a post-hoc ANOVA test).
General Steps for an F Test
1. State the null hypothesis and the alternate hypothesis.
2. Calculate the F value. The F Value is calculated using the formula F = (SSE1 – SSE2 / m) / SSE2 / n-k, where SSE = residual sum of squares, m = number of restrictions and k = number of independent variables.
3. Find the F Statistic (the critical value for this test). The F statistic formula is:
F Statistic = variance of the group means / mean of the within group variances.
You can find the F Statistic in the F-Table.
4. Support or Reject the Null Hypothesis.
F Test to Compare Two Variances
A Statistical F Test uses an F Statistic to compare two variances, s1 and s2, by dividing them. The result is always a positive number (because variances are always positive). The equation for
comparing two variances with the f-test is:
F = s21 / s22
If the variances are equal, the ratio of the variances will equal 1. For example, if you had two data sets with a sample 1 (variance of 10) and a sample 2 (variance of 10), the ratio would be 10/10 = 1.
ANOVA
In some decision-making situations, the sample data may be divided into various groups i.e. the sample may be supposed to have consisted of k-sub samples. There are interest lies in examining whether the total sample can be considered as homogenous or there is some indication that sub- samples have been drawn from different populations. So, in these situations, we have to compare the mean values of various groups, with respect to one or more criteria.
The total variation present in a set of data may be partitioned into a number of non-overlapping components as per the nature of the classification. The systematic procedure to achieve this is called Analysis of Variance (ANOVA). With the help of such a partitioning, some testing of hypothesis may be performed.
Initially, Analysis of Variance (ANOVA) had been employed only for the experimental data from the Randomized Designs but later they have been used for analyzing survey and secondary data from the Descriptive Research.
Analysis of Variance may also be visualized as a technique to examine a dependence relationship where the response (dependence) variable is metric (measured on interval or ratio scale) and the factors (independent variables) are categorical in nature with a number of categories more than two.
Chi-Square test
A chi-square (χ2)statistic is a test that measures how a model compares to actual observed data.
The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample. For example, the results of tossing a fair coin meet these criteria.
Chi-square tests are often used in hypothesis testing. The chi-square statistic compares the size any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship. For these tests, degrees of freedom are utilized to determine if a certain null hypothesis can be rejected based on the total number of variables and samples within the experiment. As with any statistic, the larger the sample size, the more reliable the results.
A chi-square (χ2)statistic is a measure of the difference between the observed and expected frequencies of the outcomes of a set of events or variables.
χ2 depends on the size of the difference between actual and observed values, the degrees of freedom, and the samples size.
χ2 can be used to test whether two variables are related or independent from one another or to test the goodness-of-fit between an observed distribution and a theoretical distribution of frequencies.
************************************
