MBA610 CU- MBA-Sem 2- MBA610 -Business Research Methods-converted-converted

Parameter Key words Type of Data p1−p2p1−p2 Difference between proportions, percentages, fractions, or rates; Qualitative compare proportions σ21/σ22σ12/σ22 Ratio of variances; difference in variability or spread; compare Quantitative variation Population Mean between Two Matched Samples Two data samples are matched if they come from repeated observations of the same subject. Here, we assume that the data populations follow the normal distribution. Using the paired t- test, we can obtain an interval estimate of the difference of the population means. Paired samples: The sample selected from the first population is related to the corresponding sample from the second population. It is important to distinguish independent samples and paired samples. Some examples are given as follows. Compare the time that males and females spend watching TV. Example 199 CU IDOL SELF LEARNING MATERIAL (SLM)

• We randomly select 20 males and 20 females and compare the average time they spend watching TV. Is this an independent sample or paired sample? • Independent -We randomly select 20 couples and compare the time the husbands and wives spend watching TV. Is this an independent sample or paired sample? • Paired Example: Drinking Water Trace metals in drinking water affect the flavor and an unusually high concentration can pose a health hazard. Ten pairs of data were taken measuring zinc concentration in bottom water and surface water Comparing Two Population Means: Independent Sampling In this section we develop both large-sample and small-sample methodologies for comparing two population means. • In the small-sample case we use the t-statistic. Population Mean Between Two Independent Samples Two data samples are independent if they come from unrelated populations and the samples does not affect each other. Here, we assume that the data populations follow the normal distribution. Using the unpaired t-test, we can obtain an interval estimate of the difference between two population means. COMPARISON OF TWO POPULATION PROPORTIONS A survey conducted in two distinct populations will produce different results. It is often necessary to compare the survey response proportion between the two populations. Here, we assume that the data populations follow the normal distribution. HYPOTHESIS TESTING FOR DIFFERENCE BETWEEN TWO MEANS USING Z-STATISTIC AND T-STATISTIC Let’s take an example to learn the concept of Hypothesis Testing. A person is on trial for a criminal offense and the judge needs to provide a verdict on his case. Now, there are four 200 CU IDOL SELF LEARNING MATERIAL (SLM)

possible combinations in such a case: • First Case: The person is innocent and the judge identifies the person as innocent • Second Case: The person is innocent and the judge identifies the person as guilty • Third Case: The person is guilty and the judge identifies the person as innocent • Fourth Case: The person is guilty and the judge identifies the person as guilty Figure 11.8 As you can clearly see, there can be two types of error in the judgment – Type 1 error, when the verdict is against the person while he was innocent and Type 2 error, when the verdict is in favor of Person while he was guilty According to the Presumption of Innocence, the person is considered innocent until proven guilty. That means the judge must find the evidence which convinces him “beyond a reasonable doubt”. This phenomenon of “Beyond a reasonable doubt” can be understood as Probability (Judge Decided Guilty | Person is Innocent) should be small. The basic concepts of Hypothesis Testing are actually quite analogous to this situation. We consider the Null Hypothesis to be true until we find strong evidence against it. Then. we accept the Alternate Hypothesis. We also determine the Significance Level (⍺) which can be understood as the Probability of (Judge Decided Guilty | Person is Innocent) in the previous example. Thus, if ⍺ is smaller, it will require more evidence to reject the Null Hypothesis. Don’t worry, we’ll cover all of this using a case study later. 201 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.9 What is the Z Test? z tests are a statistical way of testing a hypothesis when either: • We know the population variance, or • We do not know the population variance but our sample size is large n ≥ 30 If we have a sample size of less than 30 and do not know the population variance, then we must use a t-test. One-Sample Z test We perform the One-Sample Z test when we want to compare a sample mean with the population 202 CU IDOL SELF LEARNING MATERIAL (SLM)

mean. Figure 11.10 Here’s an Example to Learn a One Sample Z Test Let’s say we need to determine if girls on average score higher than 600 in the exam. We have the information that the standard deviation for girls’ scores is 100. So, we collect the data of 20 girls by using random samples and record their marks. Finally, we also set our ⍺ value (significance level) to be 0.05. 203 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.11 In this example: • Mean Score for Girls is 641 • The size of the sample is 20 • The population mean is 600 • Standard Deviation for Population is 100 204 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.12 Since the P-value is less than 0.05, we can reject the null hypothesis and conclude based on our result that Girls on average scored higher than 600. What is the t-Test? t-tests are a statistical way of testing a hypothesis when: • We do not know the population variance • Our sample size is small, n < 30 One-Sample t-Test We perform a One-Sample t-test when we want to compare a sample mean with the population mean. The difference from the Z Test is that we do not have the information on Population Variance here. We use the sample standard deviation instead of population standard deviation in this case. 205 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 11.13 Here’s an Example to Learn a One Sample t-Test Let’s say we want to determine if on average girls score more than 600 in the exam. We do not have the information related to variance (or standard deviation) for girls’ scores. To a perform t-test, we randomly collect the data of 10 girls with their marks and choose our ⍺ value (significance level) to be 0.05 for Hypothesis Testing. Figure 11.14 206 CU IDOL SELF LEARNING MATERIAL (SLM)

In this example: • Mean Score for Girls is 606.8 • The size of the sample is 10 • The population mean is 600 • Standard Deviation for the sample is 13.14 Figure 11.15 Our P-value is greater than 0.05 thus we fail to reject the null hypothesis and don’t have enough evidence to support the hypothesis that on average, girls score more than 600 in the exam. ANOVA What is Analysis of Variance (ANOVA)? Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study. The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918, when Ronald Fisher created the analysis of variance method. ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, \"Statistical Methods for 207 CU IDOL SELF LEARNING MATERIAL (SLM)

Research Workers.\" It was employed in experimental psychology and later expanded to subjects that were more complex. The Formula for ANOVA is: F=MSE/MSTwhere: F=ANOVA coefficient MST=Mean sum of squares due to treatment MSE=Mean sum of squares due to error What Does the Analysis of Variance Reveal? The ANOVA test is the initial step in analyzing factors that affect a given data set. Once the test is finished, an analyst performs additional testing on the methodical factors that measurably contribute to the data set's inconsistency. The analyst utilizes the ANOVA test results in an f-test to generate additional data that aligns with the proposed regression models. The ANOVA test allows a comparison of more than two groups at the same time to determine whether a relationship exists between them. The result of the ANOVA formula, the F statistic (also called the F-ratio), allows for the analysis of multiple groups of data to determine the variability between samples and within samples. If no real difference exists between the tested groups, which is called the null hypothesis, the result of the ANOVA's F-ratio statistic will be close to 1. Fluctuations in its sampling will likely follow the Fisher F distribution. This is actually a group of distribution functions, with two characteristic numbers, called the numerator degrees of freedom and the denominator degrees of freedom. Example of How to Use ANOVA A researcher might, for example, test students from multiple colleges to see if students from one of the colleges consistently outperform students from the other colleges. In a business application, an R&D researcher might test two different processes of creating a product to see if one process is better than the other in terms of cost efficiency. The type of ANOVA test used depends on a number of factors. It is applied when data needs to be experimental. Analysis of variance is employed if there is no access to statistical software resulting in computing ANOVA by hand. It is simple to use and best suited for 208 CU IDOL SELF LEARNING MATERIAL (SLM)

small samples. With many experimental designs, the sample sizes have to be the same for the various factor level combinations. ANOVA is helpful for testing three or more variables. It is similar to multiple two-sample t- tests. However, it results in fewer type I errors and is appropriate for a range of issues. ANOVA groups differences by comparing the means of each group and includes spreading out the variance into diverse sources. It is employed with subjects, test groups, between groups and within groups. One-Way ANOVA Versus Two-Way ANOVA There are two types of ANOVA: one-way (or unidirectional) and two-way. One-way or two- way refers to the number of independent variables in your analysis of variance test. A one- way ANOVA evaluates the impact of a sole factor on a sole response variable. It determines whether all the samples are the same. The one-way ANOVA is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. A two-way ANOVA is an extension of the one-way ANOVA. With a one-way, you have one independent variable affecting a dependent variable. With a two-way ANOVA, there are two independents. For example, a two-way ANOVA allows a company to compare worker productivity based on two independent variables, such as salary and skill set. It is utilized to observe the interaction between the two factors and tests the effect of two factors at the same time. SUMMARY A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and analyzing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently non-parametric statistics. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of 209 CU IDOL SELF LEARNING MATERIAL (SLM)

each sex, the proportion of subjects with related co-morbidities, etc. Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness. KEY WORDS/ABBREVIATIONS • Control Group- In an experiment, the control group does not receive the intervention or treatment under investigation. This group may also be referred to as the comparison group. • Control Variable- A variable that is not of interest to the researcher, but which interferes with the statistical analysis. In statistical analyses, control variables are held constant or their impact is removed to better analyze the relationship between the outcome variable and other variables of interest. • Controlled Experiment- A form of scientific investigation in which one variable, termed the independent variable, is manipulated to reveal the effect on another variable, termed the dependent or responding variable, while all other variables inthe system are held fixed. • Convenience Sampling- A sampling strategy that uses the most easily accessible people (or objects) to participate in a study. This is not a random sample, and the results cannot be generalized to individuals who did not participate in the research. • Cooperation Rate- In survey research, this is the percentage of persons who answer a survey or complete an interview out of all persons who were contacted and askedto complete the survey or interview. LEARNING ACTIVITY 1. Detail the concept of One-Way ANOVA Versus Two-Way ANOVA. 2. Differentiate between z-statistic and t-statistic Hypothesis testing 210 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Types Questions 1. Explain, what is Statistics in Research? 2. Define Univariate analysis, Bivariate analysis. 3. Discuss and define Chi-square test including testing hypothesis of association. 4. Discuss advantages and disadvantages of Chi square and T test. 5. Define ANOVA. B. Multiple Choice Questions 1. What is the difference between interval/ratio and ordinal variables? a. The distance between categories is equal across the range of interval/ratio data b. Ordinal data can be rank ordered, but interval/ratio data cannot c. Interval/ratio variables contain only two categories d. Ordinal variables have a fixed zero point, whereas interval/ratio variables do not 2. What is the difference between a bar chart and a histogram? a. A histogram does not show the entire range of scores in a distribution b. Bar charts are circular, whereas histograms are square c. There are no gaps between the bars on a histogram d. Bar charts represents numbers, whereas histograms represent percentages 3. What is meant by a \"spurious\" relationship between two variables? a. One that is so ridiculously illogical it cannot possibly be true b. An apparent relationship that is so curious it demands further attention 211 CU IDOL SELF LEARNING MATERIAL (SLM)

c. A relationship that appears to be true because each variable is related to a third one d. One that produces a perfect negative correlation on a scatter diagram 4. A test of statistical significance indicates how confident the researcher is about: a. The inter-coder reliability of their structured interview schedule b. Passing their driving test c. Learning the difference between bivariate and multivariate analysis d. Generalizing their findings from the sample to the population 5. Setting the p level at 0.01 increases the chances of making a: a. Type I error b. Type II error c. Type III error d. All of the above Answer 1. a 2. c 3. c 4. a 5. b REFERENCES • Earl R. Babbie, The Practice of Social Research, 12th edition,Wadsworth Publishing, 2009, ISBN 0-495-59841-0, pp. 436–440 • Bivariate Analysis, Sociology Index> • Chatterjee, Samprit (2012). Regression analysis by example. Hoboken, New Jersey: Wiley. ISBN 978-0470905845. • M. Haghighat, M. Abdel-Mottaleb, & W. Alhalabi (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996. 212 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 12: INTERPRETATION OF DATA ANREPORT WRITING Structure Learning Objectives Introduction Meaning of Interpretation Significance of Report Writing Layout of a Research Report Preliminary Pages Main Text End Matter Ethical issues related to publishing Ethical Issues Plagiarism and Self-Plagiarism What is Self-Plagiarism? Definitions of Plagiarism Summary Key Words/Abbreviations Learning Activity Unit End Questions (MCQ and Descriptive) References LEARNING OBJECTIVES After studying this unit, you will be able to: 213 CU IDOL SELF LEARNING MATERIAL (SLM)

• Define interpretation • State significance of Report writing • Discuss layout of Research Report • List Ethical issues related topublishing • Explain Plagiarism andSelf-Plagiarism INTRODUCTION The following definitions can explain the meaning of interpretation. l “The task of drawing conclusions or inferences and of explaining their significance after a careful analysis of selected data is known as interpretation”. l “It is an inductive process, in which you make generalizations based on the connections and common aspects among the categories and patterns”. l “Scientific interpretation seeks relationship between the data of a study and between the study findings and other scientific knowledge”. l “Interpretation in a simple way means the translation of a statistical result into an intelligible description”. Thus, analysis and interpretation are central steps in the research process. The purpose of analysis is to summarize the collected data, whereas interpretation is the search for the broader meaning of research findings. In interpretation, the researcher goes beyond the descriptive data to extract meaning and insights from the data MEANING OF INTERPRETATION The following definitions can explain the meaning of interpretation. “The task of drawing conclusions or inferences and of explaining their significance after a careful analysis of selected data is known as interpretation”. “It is an inductive process, in which you make generalizations based on the connections and common aspects among the categories and patterns”. l “Scientific interpretation seeks relationship between the data of a study and between the study findings and other scientific knowledge”. “Interpretation in a simple way means the translation of a statistical result into an intelligible description”. Thus, analysis and interpretation are central steps in the research process. The purpose of analysis is to summarize the collected data, whereas interpretation is the search for the broader meaning of research findings. In interpretation, the researcher goes beyond the descriptive data to extract meaning and insights from the data A researcher/ statistician is expected not only to collect and analyze the data but also to interpret the results of his/ her findings. Interpretation is essential for the simple reason that the usefulness and utility of research findings lie in proper interpretation. It is only through interpretation that the researcher can expose relations and patterns that underlie his findings. 214 CU IDOL SELF LEARNING MATERIAL (SLM)

In case of hypothesis testing studies the researcher may arrive at generalizations. In case the researcher had no hypothesis to start with, he would try to explain his findings on the basis of some theory. It is only through interpretation that the researcher can appreciate why his findings are what they are, and can make others learn the real significance of his research findings. Interpretation is not a mechanical process. It calls for a critical examination of the results of one’s analysis in the light of all the limitations of data gathering. For drawing conclusions you need a basis. Some of the common and important bases of interpretation are: relationships, ratios, rates and percentages, averages and other measures ofcomparison. SIGNIFICANCE OF REPORT WRITING A researcher/ statistician is expected not only to collect and analyze the data but also to interpret the results of his/ her findings. Interpretation is essential for the simple reason that the usefulness and utility of research findings lie in proper interpretation. It is only through interpretation that the researcher can expose relations and patterns that underlie his findings. In case of hypothesis testing studies the researcher may arrive at generalizations. In case the researcher had no hypothesis to start with, he would try to explain his findings on the basis of some theory. It is only through interpretation that the researcher can appreciate why his findings are what they are, and can make others learn the real significance of his research findings. Interpretation is not a mechanical process. It calls for a critical examination of the results of one’s analysis in the light of all the limitations of data gathering. For drawing conclusions you need a basis. Some of the common and important bases of interpretation are: relationships, ratios, rates and percentages, averages and other measures of comparison LAYOUT OF A RESEARCH REPORT Research report layout in Research Methodology The Research report layout must necessarily be conveyed enough about the study so that he can place it in its general scientific context, judge the adequacy of its methods and thus form an opinion of how seriously the findings are to be taken. For this purpose there is the need of proper layout of the report. The layout of the report means as to what the research report should contain. A comprehensive layout of the research report should comprise preliminary pages, the main text and the end matter. Let us deal with them separately. Preliminary Pages In its preliminary pages the report should carry a title and date, followed by acknowledgements in the form of ‘Preface’ or ‘Foreword’. Then there should be a table of 215 CU IDOL SELF LEARNING MATERIAL (SLM)

contents followed by list of tables and illustrations so that the decision-maker or anybody interested in reading the report can easily locate the required information in the report. Main Text The main text provides the complete outline of the research report along with all details. Title of the research study is repeated at the top of the first page of the main text and then follows the other details on pages numbered consecutively, beginning with the second page. Each main section of the report should begin on a new page. The main text of the report should have the following sections: 1. Introduction 2. Statement of findings and recommendations 3. The results 4. The implications drawn from the results; and 5. The summary. 1. Introduction: The purpose of introduction is to introduce the research project to the readers. It should contain a clear statement of the objectives of research i.e., enough background should be given to make clear to the reader why the problem was considered worth investigating. A brief summary of other relevant research may also be stated so that the present study can be seen in that context. The hypotheses of study, if any, and the definitions of the major concepts employed in the study should be explicitly stated in the introduction of the report. 2. The methodology adopted in conducting the study must be fully explained. The scientific reader would like to know in detail about such thing: How was the study carried out? What was its basic design? If the study was an experimental one, then what were the experimental manipulations? If the data were collected by means of questionnaires or interviews, then exactly what questions were asked (The questionnaire or interview schedule is usually given in an appendix)? If measurements were based on observation, then what instructions were given to the observers? Regarding the sample used in the study the reader should be told: Who were the subjects? How many were there? How were they selected? All these questions are crucial for estimating the probable limits of generalizability of the findings. The statistical analysis adopted must also be clearly stated. In addition to all this, the scope of the study should be stated and 216 CU IDOL SELF LEARNING MATERIAL (SLM)

the boundary lines be demarcated. The various limitations, under which the research project was completed, must also be narrated. 3. Statement of findings and recommendations: After introduction, the research report must contain a statement of findings and recommendations in non-technical language so that it can be easily understood by all concerned. If the findings happen to be extensive, at this point they should be put in the summarized form. 4. Results: A detailed presentation of the findings of the study, with supporting data in the form of tables and charts together with a validation of results, is the next step in writing the main text of the report. This generally comprises the main body of the report, extending over several chapters. The result section of the report should contain statistical summaries and reductions of the data rather than the raw data. All the results should be presented in logical sequence and splitter into readily identifiable sections. All relevant results must find a place in the report. But how one is to decide about what is relevant is the basic question. Quite often guidance comes primarily from the research problem and from the hypotheses, if any, with which the study was concerned. But ultimately the researcher must rely on his own judgement in deciding the outline of his report. “Nevertheless, it is still necessary that he states clearly the problem with which he was concerned, the procedure by which he worked on the problem, the conclusions at which he arrived, and the bases for his conclusions. 5. Implications of the results: Toward the end of the main text, the researcher should again put down the results of his research clearly and precisely. He should, state the implications that flow from the results of the study, for the general reader is interested in the implications for learning the human behavior. Such implications may have three aspects as stated below: o A statement of the inferences drawn from the present study which may be expected to apply in similar circumstances. o The conditions of the present study which may limit the extent of legitimate generalizations of the inferences drawn from the study. o The relevant questions that still remain unanswered or new questions raised by the study along with suggestions for the kind of research that would provide answers for them. It is considered a good practice to finish the report with a short conclusion which summarizes and recapitulates the main points of the study. The conclusion drawn from the study should be clearly related to the hypotheses that were stated in 217 CU IDOL SELF LEARNING MATERIAL (SLM)

the introductory section. At the same time, a forecast of the probable future of the subject and an indication of the kind of research which needs to be done in that particular field is useful and desirable. 6. Summary: It has become customary to conclude the research report with a very brief summary, resting in brief the research problem, the methodology, the major findings and the major conclusions drawn from the research results. End Matter At the end of the report, appendices should be enlisted in respect of all technical data such as questionnaires, sample information, mathematical derivations and the like ones. Bibliography of sources consulted should also be given. Index (an alphabetical listing of names, places and topics along with the numbers of the pages in a book or report on which they are mentioned or discussed) should invariably be given at the end of the report. The value of index lies in the fact that it works as a guide to the reader for the contents in the report. ETHICAL ISSUES RELATED TO PUBLISHING We are going through a time of profound change in our learning of the ethics of applied social research. From the time immediately after World War II until the early 1990s, there was a gradually developing consensus about the key ethical principles that should underlie the research endeavor. Two marker events stand out (among many others) as symbolic of this consensus. The Nuremberg War Crimes Trial following World War II brought to public view the ways German scientists had used captive human subjects as subjects in oftentimes gruesome experiments. In the 1950s and 1960s, the Tuskegee Syphilis Study involved the withholding of known effective treatment for syphilis from African-American participants who were infected. Events like these forced the reexamination of ethical standards and the gradual development of a consensus that potential human subjects needed to be protected from being used as ‘guinea pigs’ in scientific research. By the 1990s, the dynamics of the situation changed. Cancer patients and persons with AIDS fought publicly with the medical research establishment about the long time needed to get approval for and complete research into potential cures for fatal diseases. In many cases, it is the ethical assumptions of the previous thirty years that drive this ‘go-slow’ mentality. After all, we would rather risk denying treatment for a while until we achieve enough confidence in a treatment, rather than run the risk of harming innocent people (as in the Nuremberg and Tuskegee events). But now, those who were threatened with fatal illness were saying to the 218 CU IDOL SELF LEARNING MATERIAL (SLM)

research establishment that they wanted to be test subjects, even under experimental conditions of considerable risk. You had several very vocal and articulate patient groups who wanted to be experimented on coming up against an ethical review system that was designed to protect them from being experimented on. Although the last few years in the ethics of research have been tumultuous ones, it is beginning to appear that a new consensus is evolving that involves the stakeholder groups most affected by a problem participating more actively in the formulation of guidelines for research. While it’s not entirely clear, at present, what the new consensus will be, it is almost certain that it will not fall at either extreme: protecting against human experimentation at all costs vs. allowing anyone who is willing to be experimented on. 12.5.1 Ethical Issues There are a number of key phrases that describe the system of ethical protections that the contemporary social and medical research establishment have created to try to protect better the rights of their research participants. The principle of voluntary participation requires that people not be coerced into participating in research. This is especially relevant where researchers had previously relied on ‘captive audiences’ for their subjects – prisons, universities, and places like that. Closely related to the notion of voluntary participation is the requirement of informed consent. Essentially, this means that prospective research participants must be fully informed about the procedures and risks involved in research and must give their consent to participate. Ethical standards also require that researchers not put participants in a situation where they might be at risk of harm as a result of their participation. Harm can be defined as both physical and psychological. There are two standards that are applied in order to help protect the privacy of research participants. Almost all research guarantees the participants confidentiality – they are assured that identifying information will not be made available to anyone who is not directly involved in the study. The stricter standard is the principle of anonymity which essentially means that the participant will remain anonymous throughout the study – even to the researchers themselves. Clearly, the anonymity standard is a stronger guarantee of privacy, but it is sometimes difficult to accomplish, especially in situations where participants have to be measured at multiple time points (e.g., a pre-post study). Increasingly, researchers have had to deal with the ethical issue of a person’s right to service. Good research practice often requires the use of a no-treatment control group – a group of participants who do not get the treatment or program that is being studied. But when that treatment or program may have beneficial effects, persons assigned to the no-treatment control may feel their rights to equal access to services are being curtailed. 219 CU IDOL SELF LEARNING MATERIAL (SLM)

Even when clear ethical standards and principles exist, there will be times when the need to do accurate research runs up against the rights of potential participants. No set of standards can possibly anticipate every ethical circumstance. Furthermore, there needs to be a procedure that assures that researchers will consider all relevant ethical issues in formulating research plans. To address such needs most institutions and organizations have formulated an Institutional Review Board (IRB), a panel of persons who reviews grant proposals with respect to ethical implications and decides whether additional actions need to be taken to assure the safety and rights of participants. By reviewing proposals for research, IRBs also help to protect both the organization and the researcher against potential legal implications of neglecting to address important ethical issues of participants. PLAGIARISM AND SELF-PLAGIARISM Writers often claim that because they are the authors, they can reuse their work, either in full or in excerpts, over and over again. How can republishing one’s own work be defined as plagiarism if the author has only used his or her own words and ideas? This white paper explores the definition of self-plagiarism, how it crosses into copyright laws and ethical issues, and the different ways an author can avoid this increasingly controversial act of scholarly misconduct What is Self-Plagiarism? Let's look at one scenario: Leslie is an assistant professor going through tenure review with significant pressure to publish. An article she is writing for a journal piggybacks on a recent conference presentation that was also published by the conference sponsor. Leslie would like to integrate the writing from the conference presentation into the article. She faces an ethical dilemma: to repurpose her own writing from one text and use it for another, thereby increasing her number of publications for tenure, but from the same work. Doing so, Leslie might commit what Scanlon (2007) calls “academic fraud,” a form of self-plagiarism. Self-Plagiarism is defined as a type of plagiarism in which the writer republishes a work in its entirety or reuses portions of a previously written text while authoring a new work. Writers often maintain that because they are the authors, they can use the work again as they wish; they can’t really plagiarize themselves because they are not taking any words or ideas from someone else. But while the discussion continues on whether self-plagiarism is possible, the ethical issue of self-plagiarism is significant, especially because self-plagiarism can infringe upon a publisher’s copyright. Traditional definitions of plagiarism do not account for self-plagiarism, so writers may be unaware of the ethics and laws involved in reusing or repurposing texts. The American Psychological Association (2010) explains how 220 CU IDOL SELF LEARNING MATERIAL (SLM)

plagiarism differs from self-plagiarism: “Whereas plagiarism refers to the practice of claiming credit for the words, ideas, and concepts of others, self-plagiarism refers to the practice of presenting one’s own previously published work as though it were new”. As Roig (2006) suggests, self-plagiarism occurs “when authors reuse their own previously written work or data in a ‘new’ written product without letting the reader know that this material has appeared elsewhere” (pg. 16). Roig identifies a few types of self-plagiarism: • Republishing the same paper that is published elsewhere without notifying the reader nor publisher of the journal • Publishing a significant study as smaller studies to increase the number of publications rather than publishing one large study • Reusing portions of a previously written (published or unpublished text) Definitions of Plagiarism The question of whether self-plagiarism exists or not—is it possible to plagiarize oneself?— is rooted in the definition of plagiarism. Plagiarism is typically defined as stealing the work of another and presenting it as if it were one’s own. The Oxford English Dictionary (2011) defines plagiarism as taking the work of another as “literary theft.” The verb to “plagiarize” is defined as: • “To take and use as one's own (the thoughts, writings, or inventions of another person);” • “To copy (literary work or ideas) improperly or without acknowledgement; (occas.) to pass off as one's own the thoughts or work of (another)” According to the OED definition, in the strict sense recycling papers would not be plagiarism. However, Merriam-Webster Dictionary (2011) defines to “plagiarize” similarly with the addition description in the second definition below: • To steal and pass off (the ideas or words of another) as one's own: use (another's production) without crediting the source • To commit literary theft: present as new and original an idea or product derived from an existing source So, in the Webster definition, recycling one’s own papers would fall under “to present as new and original an idea or product derived from an existing source” and is, therefore, considered plagiarism. But what is more important than the definition of plagiarism, and whether it is possible to “self-plagiarize,” is the ethics behind self- plagiarism. 4.0 Ethical Issues of Self-Plagiarism Publications manuals have a set standard regarding self-plagiarism. When an author publishes in a journal, the author often signs over rights to the publisher; thus, copyright infringement is possible if an author reuses portions of a previously published work. Copyright law “protects original works of authorship” (www. copyright.gov). The Chicago Manual of Style (2010) provides the author’s responsibilities in guaranteeing authorship: “In signing a contract with a publisher an author guarantees that the work is original, that the author owns it, that no part of it has been previously published, and that no other agreement to publish it or part of it is outstanding” 221 CU IDOL SELF LEARNING MATERIAL (SLM)

(pg. 142). Authors can quote from portions of other works with proper citations, but large portions of text, even quoted and cited can infringe on copyright and would not fall under copyright exceptions or “fair use” guidelines. The amount of text one can borrow under “fair use” is not specified, but the Chicago Manual of Style (2010) gives as a “rule of thumb, one should never quote more than a few contiguous paragraphs or stanzas at a time or let the quotations, even scattered, begin to overshadow the quoter’s own material” (pg. 146). In addition to following fair use guidelines, authors need to recognize that copyright is not merely for published text. According to the U.S. Copyright Office (2010), a “work is under copyright protection the moment it is created and fixed in a tangible form that is perceptible either directly or with the aid of a machine or device.” SUMMARY The issue of self-plagiarism is becoming more and more prevalent, and some fields, particularly in STM organizations, such as biomedicine, have seen a rising trend in self- plagiarism. The APA publication manual has no discussion of self-plagiarism in its fifth edition, but addresses it twice in the sixth edition, perhaps to prevent such practices. Organizations and individual authors and researchers can take preventative measures in their writing practices and editing processes, including the use of technology that helps detect potential self-plagiarism before submitting their work for publication. The task of interpretation is not an easy job. It requires skill and dexterity on the part of the researcher. Interpretation is an art that one learns through practice and experience. The researcher may seek the guidance of experts for accomplishing the task of interpretation. The element of comparison is fundamental to all research interpretations. Comparison of one’s findings with a criterion, or with results of other comparable investigations or with normal (ideal) conditions, or with existing theories or with the opinions of a panel of judges/ experts forms an important aspect of interpretation. Interpretation and Reporting 8 The researcher must accomplish the task of interpretation only after considering all relevant factors affecting the problem to avoid false generalizations. He/she should not conclude without evidence. He/she should not draw hasty conclusions. He/she should take all possible precautions for proper interpretation of the data KEY WORDS/ABBREVIATIONS • Fallacy: Misconception, misjudgment, mis conclusion • Finding: A decision upon a fact reached as a result of observation or investigation. 222 CU IDOL SELF LEARNING MATERIAL (SLM)

• Generalization: Abstraction. It is a statement extended to the entire class of objects. • Inference: A logical conclusion / deduction arising from certain facts. • Interpretation: It is the task of drawing conclusions or inferences and of explaining their significance. LEARNING ACTIVITY 1. Draw a Layout of a Research Report as student of Postgraduate course. 2. How Ethical issues related to publishing plays role in Project work? UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Types Questions 1. Explain, what is meant by interpretation of statistical data? 2. Discuss precautions should be taken while interpreting the data? 3. Discuss what do you learn by interpretation of data? 4. Illustrate the types of mistakes which frequently occur in interpretation. 5. Explain the need, meaning and essentials of interpretation B. Multiple Choice Questions 1. Good research reports will always: a. provide results that may be irrelevant. b. focus on the Harvard style. c. provide respondent names and addresses. d. focus on addressing the research objectives. 2. Report writer should always remember that people have expectations about what information they will find and where it will be. It is unusual for final reports to have a 223 CU IDOL SELF LEARNING MATERIAL (SLM)

section with: a. method. b. executive summary. c. research costs. d. appendices. 3. Which report section is intended to describe the purpose with a full statement of the research question? a. Results. b. Appendices. c. Method. d. Objectives. 4. Which of these would NOT help your confidence in the context of a presentation? a. Comfort with the situation. b. Good preparation. c. Presentation software. d. Looking good. 5. The method section for includes detailed information on the sampling frame; sample size; variables selected for measurement; questionnaire, sampling procedure; response rates. a. a qualitative study b. a desk research study c. a quantitative study d. None of these Answer 1. d 2. c 3. d 4. c 5. a REFERENCES • B.N.Gupta. Statistics. Sahitya Bhavan, Agra. S.P. Gupta. Statistical Methods, Sultan Chand & Sons, New Delhi. B.N.Agarwal. 224 CU IDOL SELF LEARNING MATERIAL (SLM)

• Basic Statistics, Wiley Eastern Ltd. P. Saravanavel. Research Methodology, Kitab Mahal, Allahabad. C.R. Kothari. • Research Methodology (Methods and Techniques), New Age International Pvt. Ltd, New Delhi. 225 CU IDOL SELF LEARNING MATERIAL (SLM)

cu UflIVERSITT MBA BUSINESS RESEARCH METHODS CHAhDIGARH MBA610 IJNIVERSIW Discover. Learn. E mpower. www.cuidoI.in 1800-1213-88800 INSTITUTE OFBISTANCE &ONLINELEARNING NH-95, Cbandigarb-Ludbiana Highway, Gbaruan,Mobali (Punjab) Phone:- /5 2700963 J | Email: [email protected] FOLLOW US ON: $ V in Tate ,•