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

Figure 4.1 Another way of managing sources and arguments presented in them is to use a literature review matrix (also called synthesis matrix). Literature review matrix is a table in which you can represent the views, ideas, or data according to thematic categories that correspond to your research project. 51 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 4.2 As you fill out your matrix, you will begin to get a clearer view of how different sources are related, and recognize patterns that may not have been immediately visible before. For example, you may see a correlation between sample sizes and types of conclusions, or between specific kinds of aims and the methods chosen to address them. Because information is arranged in thematic columns, you can get a useful overview of all aims, or all methods at a glance. You can add new columns as your learning improves. Thus, the review matrix can also be a powerful tool for synthesizing the patterns you identify across literature, and for formulating your own observations. 4.3 DIFFERENT TYPES OF LITERATURE REVIEW Literature reviews exist within different types of scholarly works with varying foci and emphases. Short or miniature literature reviews can be presented in journal articles, book chapters, or coursework assignments to set the background for the research work and provide a general learning of the research topic. However, the focus of a literature review in a graduate research thesis is to identify gaps and argue for the need for further research. Depending on the purpose of the writer and the context in which the literature review will be presented, a selective or comprehensive approach may be taken. In the selective approach, a single or limited number of sources are reviewed (e.g. as in an annotated bibliography assignment, or the introduction of a journal article). A comprehensive approach requires the review of numerous books and articles (e.g. as in a review article), which can be presented as a substantial chapter in a research thesis or published on its own as a scholarly review article. 52 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 4.3 (Adapted from Literature Reviews: An Overview for Graduate Students under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States license.) Within a thesis, a literature review may appear in a single chapter – often being the first independent chapter after the introduction. However, reviews of literature may also be dispersed across several chapters, each of which may focus on a different theme, concept, theory or method. As a result, a thesis can contain multiple reviews based upon thematic, conceptual, theoretical and methodological considerations. 4.4 THE FUNCTION OF LITERATURE REVIEWS A literature review functions as a tool to:  provide a background to your work by summarizing the previously published work  classify the research into different categories and demonstrate how the research in a particular area has changed over time by indicating historical background (early research findings in an area) as well as explaining recent developments in an area 53 CU IDOL SELF LEARNING MATERIAL (SLM)

 clarify areas of controversy and agreement between experts in the area as well as identify dominant views  evaluate the previous research and identify gaps (i.e. unexplored areas)  help justify your research by indicating how it is different from other works in the same area Writing a literature review Depending on the discipline, a review of the literature may be:  a single chapter  segmented into a series of chapters on several topics  embedded in successive thematic chapters  a section of the introduction (in most theses there will be a short review here to justify the research, even when there is a longer review elsewhere). This tutorial focuses on writing a chapter-length literature review. However, the five key points presented here will be useful for other forms of literature review: Selection The review must be shaped by a focus on key areas of interest, and include research which provides a background to the topic. It should also be selective. A common mistake is to comment on everything you have read regardless of its relevance. How to decide what's in and what's out is always a hard question. You need to work on developing your own criteria for the bodies of literature - and the scholars - you end up including in the literature review and those you exclude. Your criteria should always include:  relevance to your study  importance to the field. A useful way of thinking about the literature review is to picture it as a dinner party (Kamler & Thomson, 2006). You are the host, and you decide who comes and who sits where, depending on how much they can contribute to the conversation about your topic. Don't forget that you are in charge: if someone's talk becomes irrelevant, throw them out! 54 CU IDOL SELF LEARNING MATERIAL (SLM)

Another way of looking at the process, particularly if you are examining several topics (or variables) is to think of yourself as rather like a film director (Rudestam & Newton, 1992). You can think of providing your audience with:  long shots to provide a solid sense of the background  middle distance shots where the key figures and elements to be examined are brought clearly into view  close-up shots where the precise focus of your work is pinpointed. Argument  When you review the literature in your field you are conducting research about the research others have done, so the written review is a report of your findings.  In the same way that you construct an argument to present the findings from your data, you need to make an argument in your literature review. That argument will establish what has already been done, what still needs to be done, and how your study contributes to meeting that need. Structure Much advice about literature review writing warns against the ‘laundry list’ (e.g., “Brown (2008) studied A and found Y. Roberts (2012) investigated B and found X and Y. Peterson (2007, 2013) conducted a long-term investigation into C and found X, Y and Z.”). For your argument about the literature to come through clearly, the review must have a structure. It must make connections between the works you have read, and between them and your own study. This diagram represents the elements that feed into the structure of your literature review. There is no single “correct” structure, since every review is shaped by the nature of the field being reviewed and the particular needs of the study the review is supporting. Some common organizing patterns are:  themes or concepts  approaches to a question  debates  historical development. 55 CU IDOL SELF LEARNING MATERIAL (SLM)

They may be used in combination. Usually there is a move from general overview to specific studies within the sections of a literature review, however they are determined. The more important works are to your own research, the more detailed your analysis will be. Process Writing a literature review is a very complex task. It will take many drafts to get right. And even before you begin the first formal draft, there are writing activities you can use to develop your thinking. Work towards a structure and argument starts at the reading stage. It will be easier to design a structure for the written review if you start grouping works as you read and take notes. The building blocks of your argument will come from your notes if you record your own thoughts, and the connections you are making between readings, as you go. Some techniques for grouping are mind maps and tables: Mind maps Mind maps are useful for constructing an overview of the field. They are also good for classifying authors, and organizing your own ideas. Figure 4.4 56 CU IDOL SELF LEARNING MATERIAL (SLM)

Tables A table, or matrix, is useful for analyzing research articles in a consistent way that allows you to see patterns. Voice When people talk about ‘voice’ in academic writing, they usually mean that the reader can sense the presence of a writer controlling the message in the text. As the writer of your literature review, you should be controlling:  the overall structure of the review  the way in which you integrate and comment upon the work in the field. Allowing your voice to be heard equates to making your argument come through clearly. 4.5 USES OF LITERATURE REVIEW A literature review may constitute an essential chapter of a thesis or dissertation, or may be a self-contained review of writings on a subject. In either case, its purpose is to:  Place each work in the context of its contribution to the learning of the subject under review  Describe the relationship of each work to the others under consideration  Identify new ways to interpret, and shed light on any gaps in, previous research  Resolve conflicts amongst seemingly contradictory previous studies  Identify areas of prior scholarship to prevent duplication of effort  Point the way forward for further research  Place one's original work (in the case of theses or dissertations) in the context of existing literature  Provide foundation of knowledge on topic  Identify areas of prior scholarship to prevent duplication and give credit to other researchers 57 CU IDOL SELF LEARNING MATERIAL (SLM)

 Identify inconstancies: gaps in research, conflicts in previous studies, open questions left from other research  Identify need for additional research (justifying your research)  Identify the relationship of works in context of its contribution to the topic and to other works  Place your own research within the context of existing literature making a case for why further study is needed. 4.6 SOURCES OF INFORMATION WHAT ARE INFORMATION SOURCES? The sources from where we get information are called information sources and these comprise documents, humans, institutions as well as mass media like newspaper, radio and television. All of us have seen and used many of these sources. In this Unit we shall study these information sources, categorize them based on their type, information contents and physical form. We shall also study the historical development of these sources. TYPES OF INFORMATION SOURCES You have seen in the school library that books are arranged on the shelves according to their class numbers (each subject is given a class number), so that all the books on the same subject can be placed together. Fiction books are arranged alphabetically by the names of the authors, so that all the books by the same author can be placed together for easy browsing. Similarly current issues of the journals and magazines are displayed on the display racks and old issues are shelved behind. Apart from these, there are other sets of books like dictionaries, encyclopedias, maps, atlases, guidebooks, etc. which are kept on separate shelves. These are called reference books. These books are always available in the library for consultation and are not issued to the library members. As a library organizes its collection for the better use of its material by the users, similarly, information sources are also organized according to their contents, type, media or form to cater to the different needs of the users. We can group information sources into two broad categories as follows: i) Documentary Sources ii) Non-documentary Sources Documentary Sources All recorded sources of information irrespective of their contents 58 CU IDOL SELF LEARNING MATERIAL (SLM)

and forms come under documentary sources. These may be published or unpublished, in print or in electronic form. These may be books, periodicals, magazines, and others. Documentary sources can further be categorized based on their contents and form (or media). By Contents Based on the information contents and organizational level these sources can be grouped into: i) Primary, ii) Secondary, and iii) Tertiary sources of information. By Form Based on the physical form the documentary sources can be grouped into: i) Paper-based documentary sources; and ii) Documentary sources on other media which cover the following: a) Sound or audio recording: Audio cassettes, audio tapes, etc. b) Visual Images: Still: slides, filmstrips, transparencies, photographs. c) Visual Images: Moving: Films, videotapes, video discs; etc. d) Artifacts and Realia: Globes, relief models, etc. e) Electronic Media: Magnetic tapes, discs, drums, etc. f) Optical Media: CD-ROM, DVD, etc. g) Microforms; Microfilms, microfiche, etc. Non-documentary Sources Non-documentary sources of information are those sources which are not recorded in any form. Under this category come: i) Humans, ii) Organizations, iii) Mass media other than print media, and iv) Cyber media. As you conduct research, you will consult different sources of information. You will encounter primary, secondary, and tertiary sources. What do these terms mean? IMPORTANT NOTE: The types of information that can be considered primary sources may vary depending on the subject discipline, and also on how you are using the material. For example: o A government study about steroid use in college sports would be a primary source. o A magazine article reporting on a government study about steroid use in college sports would be a secondary source. o However, if you were researching how use of steroids is portrayed in the popular media, the magazine article could be considered a primary 59 CU IDOL SELF LEARNING MATERIAL (SLM)

source. PRIMARY SOURCES Primary sources are original materials. They are from the time period involved and have not been filtered through interpretation or evaluation. Primary sources are original materials on which other research is based. They are usually the first formal appearance of results in physical, print or electronic format. They present original thinking, report a discovery, or share new information. Note: The definition of a primary source may vary depending upon the discipline or context. • Artifacts (e.g. coins, plant specimens, fossils, furniture, tools, clothing, all from the time under study) • Audio recordings (e.g. radio or internet broadcasts) • Diaries, Journals, Notes, Autobiographies & Memoirs • Internet Communications (e.g. email, chat transcripts) • Interviews (e.g., oral histories, telephone, e-mail); • Journal articles describing original research or containing original analysis • Letters, Postcards, & other forms of correspondence • Newspaper and Magazine articles with eyewitness accounts, original reporting or analysis • Original Documents (i.e. birth certificates, wills, marriage licenses, trial transcripts) • Photographs • Records of organizations, government agencies, and businesses (e.g. corporate reports, treaties, constitutions, census data, government documents) • Speeches • Survey Results and Analysis (e.g., market surveys, public opinion polls) • User Manuals • Video recordings (e.g. television or internet broadcasts) • Works of art, architecture, literature, film, and music (e.g., paintings, sculptures, musical scores, movies, buildings, novels, poems) 60 CU IDOL SELF LEARNING MATERIAL (SLM)

SECONDARY SOURCES Secondary sources are less easily defined than primary sources. Generally, they are accounts written after the fact with the benefit of hindsight. They are interpretations and evaluations of primary sources. Secondary sources are not evidence, but rather commentary on and discussion of evidence. However, what some define as a secondary source, others define as a tertiary source. Context is everything. Note: The definition of a secondary source may vary depending upon the discipline or context. • Biographical works • Commentaries, criticisms • Histories • Magazine and newspaper articles (except eyewitness accounts, original reporting or analysis) • Books, other than fiction and autobiography TERTIARY SOURCES Tertiary sources consist of information that is a distillation and collection of primary and secondary sources. • Almanacs or Fact Books • Chronologies or Timelines • Dictionaries and Encyclopedias • Directories • Databases, Indexes, Abstracts, Bibliographies used to locate primary and secondary sources • Textbooks 4.7 SUMMARY Before we can create new knowledge, we must first know the current state of knowledge about our research subject .Effective researchers will use knowledge and insights of others 61 CU IDOL SELF LEARNING MATERIAL (SLM)

and draw on elements of prior research. In the literature review, the task is to learn as much as you can from the efforts and work of others – which is published in the “scientific literature”. The central purpose of the Literature Review is to provide the researcher (and the reader) with a learning of literature about the proposed research. • This includes the strengths and weaknesses! • The research problem is the focus of the literature review But the literature may be related to the research project in several ways – through the problem, the objectives, the conceptual framework, and methods and procedures In scientific and technical disciplines, including medicine and health sciences, the literature review is often more narrowly framed around a specific discipline or research area than in the humanities. A successful scientific literature review will not only identify the current gap in knowledge, but also position your own research project as a viable way of addressing it. You thus need to build a solid argument to convince the reader that your theoretical and methodological approach is likely to result in a worthwhile contribution to knowledge. In writing the review, it is important to identify the overarching themes that show you have a thorough grasp of the big picture, and to ensure your observations are supported by sufficient evidence. When reviewing and critiquing existing trends and methods, consider their design, scale and scope, and point out where findings are not comparable or are difficult to compare. 4.8 KEY WORDS/ABBREVIATIONS  Peer Reviewed Journal - A type of scholarly journal. Peer review is a process where fellow experts look over an article before it is published so that errors, bias, irrelevancies and poor writing don't make it into print.  Periodical - A publication that is issued at regular (periodic) intervals. Includes journals, magazines, newspapers, etc.  Plagiarism - A form of academic dishonesty that involves quoting, paraphrasing or otherwise using another author’s work without properly documenting the source of the information. It is plagiarism even if it is unintentional.  Popular Magazine - A type of non-scholarly periodical, intended for a general 62 CU IDOL SELF LEARNING MATERIAL (SLM)

audience. Magazines make their profit from advertisements. Content is produced by staff writers and freelance writers, not subject experts.  Precision - In searching a database or search engine, precision is a measure of how many of your results were actually on topic.  Proximity Search - Searching for search terms near each other in the text. Most commonly, quotation marks (“”) are used to surround a phrase so that all the words in the phrase are found together, in that order 4.9 LEARNING ACTIVITY 1. What is the purpose of conducting a literature review? What function does a literature review serve within a thesis? __________________________________________________________________________ _____________________________________________________________ 2. Create the draft with the methods to accumulate the sources of information __________________________________________________________________________ _____________________________________________________________ 4.10 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Types Questions 1. Discuss the Meaning of Literature review? 2. Discuss various uses of literature review 3. Discuss the various source of information? 4. Explain how the literature review is helpful in project reports? 5. Explain Mind maps? B. Multiple Choice Questions 1. A research paper is a brief report of research work based on a. Primary Data only b. Secondary Data only c. Both Primary and Secondary Data 63 CU IDOL SELF LEARNING MATERIAL (SLM)

d. None of these 2. Conference proceedings are considered as..................documents. a. Conventional b. Primary c. Secondary d. Tertiary 3. Questionnaire is a: a. Research method b. Measurement technique c. Tool for data collection d. Data analysis technique 4. Controlled Group” is a term used in............... a. Survey research b. Historical research c. Experimental research d. Descriptive research 5. ‘Noise’ in Information Retrieval is due to.............. a. Precision b. Recall c. Relevant information d. Redundant information Answer 1. c 2. b 3. c 4. c 5. d 4.11 REFERENCES  Baglione, L. (2012). Writing a Research Paper in Political Science. Thousand Oaks, California: CQ Press. 64 CU IDOL SELF LEARNING MATERIAL (SLM)

 Adams, John; Khan, Hafiz T A; Raeside, Robert (2007). Research methods for graduate business and social science students. New Delhi: SAGE Publications. p. 56. ISBN 9780761935896.  Bolderston, Amanda (June 2008). \"Writing an Effective Literature Review\". Journal of Medical Imaging and Radiation Sciences. 39 (2): 86–92. doi:10.1016/j.jmir.2008.04.009. PMID 31051808.  Torraco, Richard J. (December 2016). \"Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future\". Human Resource Development Review. 15 (4): 404–428. doi:10.1177/1534484316671606. ISSN 1534-4843. S2CID 152155091.  Shields, Patricia; Rangarjan, Nandhini (2013). A Playbook for Research Methods: Integrating Conceptual Frameworks and Project Management. Stillwater, Oklahoma: New Forums Press. ISBN 978-1-58107-247-1.  Baker, P. (2000). \"Writing a Literature Review\". The Marketing Review. 1 (2): 219– 247. doi:10.1362/1469347002529189.  Granello, D. H. (2001). \"Promoting cognitive complexity in graduate written work: Using Bloom's taxonomy as a pedagogical tool to improve Literature Reviews\". Counselor Education & Supervision. 40 (4): 292–307. doi:10.1002/j.1556- 6978.2001.tb01261.x. 65 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 5: FORMULATION OF HYPOTHESIS Structure 5.0. Learning Objectives 5.1. Introduction 5.2. Qualities of a good Hypothesis 5.3. Null Hypothesis & Alternative Hypothesis; 5.4. Hypothesis Testing – Logic & Importance 5.5. Summary 5.6. Key Words/Abbreviations 5.7. Learning Activity 5.8. Unit End Questions (MCQ and Descriptive) 5.9. References 5.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  List qualities of good hypothesis  Explain Null and Alternative hypothesis  Discuss Hypothesis testing 5.1 INTRODUCTION Hypothesis is an assumption that is made on the basis of some evidence. This is the initial point of any investigation that translates the research questions into a prediction. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables. Sources of Hypothesis Following are the sources of hypothesis: 66 CU IDOL SELF LEARNING MATERIAL (SLM)

 The resemblance between the phenomenon.  Observations from past studies, present-day experiences and from the competitors.  Scientific theories.  General patterns that influence the thinking process of people. Examples of Hypothesis Following are the examples of hypothesis based on their types:  Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.  All lilies have the same number of petals is an example of a null hypothesis.  If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. Types of Hypothesis First, we must take a moment to define independent and dependent variables. Simply put, an independent variable is the cause and the dependent variable is the effect. The independent variable can be changed whereas the dependent variable is what you're watching for change. For example: How does the amount of makeup one applies affect how clear their skin is? Here, the independent variable is the makeup and the dependent variable is the skin. The six most common forms of hypotheses are:  Simple Hypothesis  Complex Hypothesis  Empirical Hypothesis  Null Hypothesis (Denoted by \"HO\")  Alternative Hypothesis (Denoted by \"H1\")  Logical Hypothesis  Statistical Hypothesis A simple hypothesis is a prediction of the relationship between two variables: the independent variable and the dependent variable.  Drinking sugary drinks daily leads to obesity. A complex hypothesis examines the relationship between two or more independent variables and two or more dependent variables. 67 CU IDOL SELF LEARNING MATERIAL (SLM)

 Overweight adults who 1) value longevity and 2) seek happiness are more likely than other adults to 1) lose their excess weight and 2) feel a more regular sense of joy. A null hypothesis (H0) exists when a researcher believes there is no relationship between the two variables, or there is a lack of information to state a scientific hypothesis. This is something to attempt to disprove or discredit.  There is no significant change in my health during the times when I drink green tea only or root beer only. This is where the alternative hypothesis (H1) enters the scene. In an attempt to disprove a null hypothesis, researchers will seek to discover an alternative hypothesis.  My health improves during the times when I drink green tea only, as opposed to root beer only. A logical hypothesis is a proposed explanation possessing limited evidence. Generally, you want to turn a logical hypothesis into an empirical hypothesis, putting your theories or postulations to the test.  Cacti experience more successful growth rates than tulips on Mars. (Until we're able to test plant growth in Mars' ground for an extended period of time, the evidence for this claim will be limited and the hypothesis will only remain logical.) An empirical hypothesis, or working hypothesis, comes to life when a theory is being put to the test, using observation and experiment. It's no longer just an idea or notion. It's actually going through some trial and error, and perhaps changing around those independent variables.  Roses watered with liquid Vitamin B grow faster than roses watered with liquid Vitamin E. (Here, trial and error is leading to a series of findings.) A statistical hypothesis is an examination of a portion of a population.  If you wanted to conduct a study on the life expectancy of Savannians, you would want to examine every single resident of Savannah. This is not practical. Therefore, you would conduct your research using a statistical hypothesis, or a sample of the Savannian population. Functions of Hypothesis Following are the functions performed by the hypothesis:  Hypothesis helps in making an observation and experiments possible.  It becomes the start point for the investigation.  Hypothesis helps in verifying the observations.  It helps in directing the inquiries in the right directions. 68 CU IDOL SELF LEARNING MATERIAL (SLM)

How will Hypothesis help in Scientific Method? Researchers use hypothesis to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:  Formation of question  Doing background research  Creation of hypothesis  Designing an experiment  Collection of data  Result analysis  Summarizing the experiment  Communicating the results 5.2 QUALITIES OF A GOOD HYPOTHESIS Characteristics of Hypothesis Following are the characteristics of hypothesis:  The hypothesis should be clear and precise to consider it to be reliable.  If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.  The hypothesis must be specific and should have scope for conducting more tests.  The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.  Hypothesis should be related to the existing body or theory and impact;  Hypothesis should have logical unity and comprehensiveness;  Hypothesis should be capable of verification; and  Hypothesis should be operation able. 5.3 NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS In everyday life, we often have to make decisions based on incomplete information. These may be decisions that are important to us such as, \"Will I improve my biology grades if I 69 CU IDOL SELF LEARNING MATERIAL (SLM)

spend more time studying vocabulary?\" or \"Should I become a chemistry major to increase my chances of getting into med school?\" This section is about the use of hypothesis testing to help us with these decisions. Hypothesis testing is a kind of statistical inference that involves asking a question, collecting data, and then examining what the data tells us about how to proceed. In a formal hypothesis test, hypotheses are always statements about the population. The hypothesis tests we will examine in this chapter involve statements about the average values (means) of some variable in the population. For example, we may want to know if the average time that college freshmen spend studying each week is really 20 hours per week. We may want to compare this average time spent studying for freshmen that earned a GPA of 3.0 or higher and those that did not Developing Null and Alternative Hypotheses In statistical hypothesis testing, there are always two hypotheses. The hypothesis to be tested is called the null hypothesis and given the symbol H0. The null hypothesis states that there is no difference between a hypothesized population mean and a sample mean. It is the status quo hypothesis. For example, if we were to test the hypothesis that college freshmen study 20 hours per week, we would express our null hypothesis as: H0: µ = 20 We test the null hypothesis against an alternative hypothesis, which is given the symbol Ha. The alternative hypothesis is often the hypothesis that you believe yourself! It includes the outcomes not covered by the null hypothesis. In this example, our alternative hypothesis would express that freshmen do not study 20 hours per week: Ha: µ 6= 20 Deciding Whether to Reject the Null Hypothesis: One and Two-Tailed Hypothesis Tests The alternative hypothesis can be supported only by rejecting the null hypothesis. To reject the null hypothesis means to find a large enough difference between your sample mean and the hypothesized (null) mean that it raises real doubt that the true population mean is 20. If the difference between the hypothesized mean and the sample mean is very large, we reject the null hypothesis. If the difference is very small, we do not. In each hypothesis test, we have to decide in advance what the magnitude of that difference must be to allow us to reject the null hypothesis. Below is an overview of this process. Notice that if we fail to find a large enough difference to reject, we fail to reject the null hypothesis. Those are your only two alternatives. When a hypothesis is tested, a statistician must decide on how much of a difference between means is necessary in order to reject the null hypothesis. Statisticians first choose a level of significance or alpha (α)level for their hypothesis test. Similar to the significance level you used in constructing confidence intervals, this alpha level tells us how improbable a sample mean must be for it 70 CU IDOL SELF LEARNING MATERIAL (SLM)

to be deemed \"significantly different\" from the hypothesized mean. The most frequently used levels of significance are 0.05 and 0.01. An alpha level of 0.05 means that we will consider our sample mean to be significantly different from the hypothesized mean if the chances of observing that sample mean are less than 5%. Similarly, an alpha level of 0.01 means that we will consider our sample mean to be significantly different from the hypothesized mean if the chances of observing that sample mean are less than 1%. Two-tailed Hypothesis Tests A hypothesis test can be one-tailed or two-tailed. The examples above are all two-tailed hypothesis tests. We indicate that the average study time is either 20 hours per week, or it is not. Computer use averages 3.2 hours per week, or it does not. We do not specify whether we believe the true mean to be higher or lower than the hypothesized mean. We just believe it must be different. In a two-tailed test, you will reject the null hypothesis if your sample mean falls in either tail of the distribution. For this reason, the alpha level (let’s assume .05) is split across the two tails. The curve below shows the critical regions for a two-tailed test. These are the regions under the normal curve that, together, sum to a probability of 0.05. Each tail has a probability of 0.025. The z-scores that designate the start of the critical region are called the critical values. If the sample mean taken from the population falls within these critical regions, or \"rejection regions,\" we would conclude that there was too much of a difference and we would reject the null hypothesis. However, if the mean from the sample falls in the middle of the distribution (in between the critical regions) we would fail to reject the null hypothesis One-Tailed Hypothesis Test We would use a single-tail hypothesis test when the direction of the results is anticipated or we are only interested in one direction of the results. For example, a single-tail hypothesis test may be used when evaluating whether or not to adopt a new textbook. We would only decide to adopt the textbook if it improved student achievement relative to the old textbook. When performing a single-tail hypothesis test, our alternative hypothesis looks a bit different. We use the symbols of greater than or less than. For example, let’s say we were claiming that the average SAT score of graduating seniors was GREATER than 1,110. Remember, our own personal hypothesis is the alternative hypothesis. Then our null and alternative hypothesis could look something like: H0: µ ≤ 1100 Ha: µ > 1100 In this scenario, our null hypothesis states that the mean SAT scores would be less than or equal to 1,100 while the alternate hypothesis states that the SAT scores would be greater than 1,100. A single-tail hypothesis test also means that we have only one critical region because we put the entire critical region into just one side of the distribution. When the alternative 71 CU IDOL SELF LEARNING MATERIAL (SLM)

hypothesis is that the sample mean is greater, the critical region is on the right side of the distribution (see below). When the alternative hypothesis is that the sample is smaller, the critical region is on the left side of the distribution. To calculate the critical regions We must first find the critical values or the cut-offs where the critical regions start. This will be covered in the next section. Type I and Type II Errors Remember that there will be some sample means that are extremes – that is going to happen about 5% of the time, since 95% of all sample means fall within about two standard deviations of the mean. What happens if we run a hypothesis test and we get an extreme sample mean? It won’t look like our hypothesized mean, even if it comes from that distribution. We would be likely to reject the null hypothesis. But we would be wrong. When we decide to reject or not reject the null hypothesis, we have four possible scenarios: a. A true hypothesis is rejected. b. A true hypothesis is not rejected. c. A false hypothesis is not rejected d. A false hypothesis is rejected. If a hypothesis is true and we do not reject it (Option 2) or if a false hypothesis is rejected (Option 4), we have made the correct decision. But if we reject a true hypothesis (Option 1) or a false hypothesis is not rejected (Option 3) we have made an error. Overall, one type of error is not necessarily more serious than the other. Which type is more serious depends on the specific research situation, but ideally both types of errors should be minimized during the analysis. The general approach to hypothesis testing focuses on the Type I error: rejecting the null hypothesis when it may be true. Guess what? The level of significance, also known as the alpha level, IS the probability of making a Type I error. At the 0.05 level, the decision to reject the hypothesis may be incorrect 5% of the time. Calculating the probability of making a Type II error is not as straightforward as calculating a Type I error, and we won’t discuss that here. You should be able to recognize what each type of error looks like in a particular hypothesis test. For example, suppose you are means that testing whether listening to rock music helps you improve your memory of 30 random objects. Assume further that it doesn’t. A Type I error would be concluding that listening to rock music did help memory (but you are wrong). A Type I error will only occur when your null hypothesis is false. Let’s assume 72 CU IDOL SELF LEARNING MATERIAL (SLM)

that listening to rock music does improve memory. In this scenario, if you concluded that it didn’t, you would be wrong again. But this time you would be making a Type II error — failing to find a significant difference when one in fact exists. It is also important that you realize that the chance of making a Type I error is under our direct control. Often we establish the alpha level based on the severity of the consequences of making a Type I error. If the consequences are not that serious, we could set an alpha level at 0.10 or 0.20. In other words, we are comfortable making a decision where we could falsely reject the null hypothesis 10 to 20% of the time. However, in a field like medical research, we would set the alpha level very low (at 0.001 for example) if there was potential bodily harm to patients. 5.4 HYPOTHESIS TESTING – LOGIC & IMPORTANCE The Logic of Hypothesis Testing As just stated, the logic of hypothesis testing in statistics involves four steps. 1. State the Hypothesis: We state a hypothesis (guess) about a population. Usually the hypothesis concerns the value of a population parameter. 2. Define the Decision Method: We define a method to make a decision about the hypothesis. The method involves sample data. 3. Gather Data: We obtain a random sample from the population. 4. Make a Decision: We compare the sample data with the hypothesis about the population. Usually we compare the value of a statistic computed from the sample data with the hypothesized value of the population parameter. o If the data are consistent with the hypothesis we conclude that the hypothesis is reasonable. NOTE: We do not conclude it is right, but reasonable! AND: We actually do this by rejecting the opposite hypothesis (called the NULL hypothesis). More on this later. o If there is a big discrepancy between the data and the hypothesis, we conclude that the hypothesis was wrong. We expand on those steps in this section: First Step: State the Hypothesis Stating the hypothesis actually involves stating two opposing hypotheses about the value of a population parameter. 73 CU IDOL SELF LEARNING MATERIAL (SLM)

Example: Suppose we have are interested in the effect of prenatal exposure of alcohol on the birth weight of rats. Also, suppose that we know that the mean birth weight of the population of untreated lab rats is 18 grams. Here are the two opposing hypotheses:  The Null Hypothesis (Ho). This hypothesis states that the treatment has no effect. For our example, we formally state: The null hypothesis (Ho) is that prenatal exposure to alcohol has no effect on the birth weight for the population of lab rats. The birth weight will be equal to 18 grams. This is denoted  The Alternative Hypothesis (H1). This hypothesis states that the treatment does have an effect. For our example, we formally state: The alternative hypothesis (H1) is that prenatal exposure to alcohol has an effect on the birth weight for the population of lab rats. The birth weight will be different than 18 grams. This is denoted Second Step: Define the Decision Method We must define a method that lets us decide whether the sample mean is different from the hypothesized population mean. The method will let us conclude whether (reject null hypothesis) or not (accept null hypothesis) the treatment (prenatal alcohol) has an effect (on birth weight). We will go into details later. Third Step: Gather Data. Now we gather data. We do this by obtaining a random sample from the population. Example: A random sample of rats receives daily doses of alcohol during pregnancy. At birth, we measure the weight of the sample of newborn rats. The weights, in grams, are shown in the table. We calculate the mean birth weight. 74 CU IDOL SELF LEARNING MATERIAL (SLM)

Experiment 1 Sample Mean = 13 Fourth Step: Make a Decision We make a decision about whether the mean of the sample is consistent with our null hypothesis about the population mean.  If the data are consistent with the null hypothesis we conclude that the null hypothesis is reasonable. Formally: we do not reject the null hypothesis.  If there is a big discrepancy between the data and the null hypothesis, we conclude that the null hypothesis was wrong. Formally: we reject the null hypothesis. Example: We compare the observed mean birth weight with the hypothesized value, under the null hypothesis, of 18 grams.  If a sample of rat pups which were exposed to prenatal alcohol has a birth weight \"near\" 18 grams we conclude that the treatment does not have an effect. Formally: We do not reject the null hypothesis that prenatal exposure to alcohol has no effect on the birth weight for the population of lab rats.  If our sample of rat pups has a birth weight \"far\" from 18 grams we conclude that the treatment does have an effect. Formally: We reject the null hypothesis that prenatal exposure to alcohol has no effect on the birth weight for the population of lab rats. For this example, we would probably decide that the observed mean birth weight of 13 grams is \"different\" than the value of 18 grams hypothesized under the null hypothesis. 75 CU IDOL SELF LEARNING MATERIAL (SLM)

Formally: We reject the null hypothesis that prenatal exposure to alcohol has no effect on the birth weight for the population of lab rats. 5.5 SUMMARY Scientific process or all empirical sciences are recognized by two inter-related concepts, namely; (a) context of discovery (getting an idea) and (b) context of justification (testing and results). Hypotheses are the mechanism and container of knowledge moving from the unknown to known. These elements form techniques and testing ground for scientific discovery. Hypotheses are tentative explanation and potential answer to a problem. Hypothesis gives the direction and helps the researcher interpret data. In this unit, you will be familiarized with the term hypothesis and its characteristics. It is, then, followed by the hypothesis formulation and types of hypothesis. Errors in hypothesis testing are also highlighted. Further, In order to test the hypothesis, researcher rarely collects data on entire population owing to high cost and dynamic nature of the individual in population. Therefore, they collect data from a subset of individual – a sample - and make the inferences about entire population. This leads us to what we should know about the population and sample. So, researcher plans sample design and uses Formulation and Sampling various method of sampling. In this unit you have learnt about hypothesis formulation. A hypothesis is a speculative statement that is subjected to verification through a research study. In formulating a hypothesis it is important to ensure that it is simple, specific and conceptually clear; is able to be verified; is rooted in an existing body of knowledge; and able to be operational zed. There are two broad types of hypothesis: a null hypothesis and an alternate hypothesis. Hypothesis testing involves making educated guesses about a population based on a sample drawn from the population. We generate null and alternative hypotheses based on the mean of the population to test these guesses. We establish critical regions based on level of significance or alpha (α) levels. If the value of the test statistic falls in these critical regions, we are able to reject it. When we make a decision about a hypothesis, there are four different outcome and possibilities and two different types of errors. A Type I error is when we reject the null hypothesis when it is true and a Type II error is when we do not reject the null hypothesis, even when it is false. 5.6 KEY WORDS/ABBREVIATIONS  Hypothesis: A tentative and testable statement of a potential relationship between two or 76 CU IDOL SELF LEARNING MATERIAL (SLM)

more variables.  Null hypothesis: The hypothesis that is of no scientific interest; sometimes the hypothesis of no difference.  Alternative hypothesis: Statistical term for research hypothesis that specifies values that researcher believes to hold true.  Population: It is the aggregate from which a sample is drawn. In statistics, it refers to any specified collection of objects, people, organization etc.  Population size: It is the total number of units present in the population. 5.7 LEARNING ACTIVITY 1. Write a hypothesis which incorporates each pair of concepts listed below: a) academic achievement and teaching methods b) education and social prestige c) frustration and need for achievement __________________________________________________________________________ __________________________________________________________________________ 2. Study the following research questions and state the possible hypothesis with specify their types specify their types.  Is physical attractiveness related to friendship?  Does meaningful of material affect the rate of learning?  Does reinforcement improve the learning for solving simple discrimination task?  Does onset of fatigue reduce the efficiency of the worker? __________________________________________________________________________ __________________________________________________________________________ 5.8 UNIT END QUESTIONS (MCQ AND DESCRIPTIVE) A. Descriptive Types Questions 1. Define hypothesis and explain its characteristics. 2. Explain briefly-Formulation of hypothesis 3. Differentiate between Null hypothesis and Alternative hypothesis 77 CU IDOL SELF LEARNING MATERIAL (SLM)

4. State few examples of good hypothesis 5. Discuss the Hypothesis testing with the help of examples B. Multiple Choice Questions 1. Hypothesis is considered as ............... and ................. statement of the possible relationship between two or more variables. a. tentative, testable b. Admit, Prior c. Late, Delay d. Null, Check 2. Hypothesis can be put in the form of an if …... statement. a. Then b. Later c. Process d. Delay 3. Hypothesis is formulated………. to review of literature. a. Admit b. Prior c. Late d. Delay 4. ………… of knowledge of a theoretical framework is a major difficulty in formulating a hypothesis. a. Presence b. Absence c. Valuation d. None of these 5. Formulation of a hypothesis enhances ....................... in the study a. Objectivity b. Relativity c. Subjectivity d. Absolute 78 CU IDOL SELF LEARNING MATERIAL (SLM)

Answer 1. a 2. a 3. b 4. a 5. a 5.9 REFERENCES  Adder, H.J., Mellenbergh, G.J., & Hand, D.J. (2008). Advising on Research Methods: A Consultant’s Companion. Huizen,  The Netherlands: Johannes van kessel Publishing. Blalock, HM (1960), Social Statistics, NY: McGraw-Hill. Goode, WJ & Hatt, PK (1981). Methods in Social Research. Tokyo: McGraw hill Book Company. References Kerlinger, FN (1973).  Foundations of Behavioral Research. New York: Rinehart and Winston. McGuigan, FJ (1990). E  experimental Psychology: A Methodological Approach, Englewood Cliffs, New Jersey: Prentice-hall. Reichenbach, H (1938).  Experience and Prediction. Chicago: University of Chicago press. Reichenbach, H (1947).  Elements of Symbolic Logic, New York: Macmillan. Young, PV (1992). Scientific Social Survey and Research, New Delhi: Prentice hall of India. 79 CU IDOL SELF LEARNING MATERIAL (SLM)

UNIT 6: RESEARCH DESIGN Structure 6.0. Learning Objectives 6.1. Introduction 6.2. Concept and Importance in Research 6.3. Features of a good research design 6.4. Types of Research Designs 6.4.1 Based on Nature of Investigation 6.4.2 Based on Reference Period 6.5. Principles of Experimental Research Designs 6.6. Formal and Informal Experimental Designs 6.7. Summary 6.8. Key Words/Abbreviations 6.9. Learning Activity 6.10. Unit End Questions (MCQ and Descriptive) 6.11. References 6.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  State importance of Research  Explain features of Good Design Research  Discuss types of Research Design 6.1 INTRODUCTION Having decided what you want to study about, the next question comes up as to how are you 80 CU IDOL SELF LEARNING MATERIAL (SLM)

going to conduct your study? What procedures will you adopt to obtain answers to research questions? How will you carry out the tasks needed to complete the different components of the research process? What should you do and what should you not do in the process of undertaking the study? These are some of the questions that need to be answered before we proceed to conduct the study. Basically, answers to these questions constitute the core of a research design. This unit therefore begins with the definition and the description of the research design. Then the purpose of the research design is highlighted in which you will study how a research can maximize the systematic variance, control extraneous variance through the various controlling techniques i.e. randomization, matching, elimination and statistical control. Further you will find how a researcher can minimize the error variance. Moreover, research cannot ignore the criteria of good design. This unit acquaints you with the basic criteria of research through which you can distinguish good design from weak design. Finally, the qualities of research design are indicated and described. Psychologists make decisions about hypothesized relationships between independent and dependent variables based upon observations of behavior. One way to organize the observational process is to employ an experimental design. This unit tries to acquaint you with control group design and two factor design (factorial design) which are used as true experimental design in psychological researches. It begins with the nature and basic elements of experimental design and focuses on the terminology of experimental design. Further, you will find the description of control group design and three types of control group one as posttest only, one as control group design, and one as pretest posttest control group design and one as Solomon four group design with relevant examples. Moreover this unit continues with the discussion of the factorial design in which you will study the nature of factorial design. This will provide answers to the questions like how can you sketch a layout of factorial design, how basic terminology of experimental design are worked out, how can you study the main effect and interaction effect of the different variables and how can you interpret your result with the help of graphical presentation. Finally, advantage and disadvantage of factorial design are described. 6.2 CONCEPT AND IMPORTANCE IN RESEARCH Winner (1971) compared the research design to an architect’s plan for the structure of a building. The designer of researcher performs a role similar to that of the architect. The owner of the building gives his basic requirements to the architect, who then exercising his expertise, prepares a plan or a blue print outlining the final shape of the structure. Similarly, researcher has to do planning or prepare a structure before starting data collection and analysis. According to Myers (1980), the research design is the general structure of the 81 CU IDOL SELF LEARNING MATERIAL (SLM)

experiment, not its specific content. In fact, the research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data. According to Thyer (1993) a traditional research design is a blueprint or detailed plan for how to conduct a research study and how to complete the same. Planning such a research design involves, (i) operationalizing variables so that they can be measured, (ii) selecting a sample of interest to study, (iii) Collecting data to be used as a basis for testing hypothesis, and (iv) analyzing the results. According to Matheson (1970) a research design is a basic plan for research, including the assignment of subjects to the levels of the independent variable and the manipulation of the independent variable. According to Kerlinger (1986) research design is the plan, structure, and strategy of investigation conceived so as to obtain answers to research questions and to control variance. The definition of Kerlinger reveals three important components, which are (i) research design is a plan (ii) research design is the structure (iii) research design is the strategy. Let us see what these are: i) Research Design is the Plan: The plan is the overall scheme or program of the research. It includes an outline of what the investigator will do from writing the hypotheses and their operational implications to the final analysis of data. ii) Research Design is the Structure: The structure of the research is more specific. It is the outline, the scheme, the paradigm, of the operation of the variables. When we draw diagrams that outline the variables and their relation and juxtaposition, we build structural schemes for accomplishing operational research purposes. iii) Research Design is the Strategy: Strategy as used here is also more specific than plan. It includes the methods to be used to gather and analyze the data. In other words strategy implies how the research objectives will be reached and how the problems encountered in the research will be tackled. 6.3 FEATURES OF A GOOD RESEARCH DESIGN A good design is characterized by flexible; appropriate, efficient, economical and so on. The design which minimizes bias and maximizes the reliability of the data collected and 82 CU IDOL SELF LEARNING MATERIAL (SLM)

analyzed is considered a good design. The design which gives the smallest experimental error is supposed to be the best design in many investigations. Similarly, a design which yields maximal information and provides an opportunity for considering many different aspects of a problem is considered the most appropriate and efficient design. Thus, the question of good design is related to the purpose or objective of the research problem and also with the nature of the problem to be studied. One single design cannot serve the purpose of all types of research problem. Throughout the design construction task, it is important to have in mind some endpoint, some criteria which are to be achieved before accepting a design strategy. The criteria below are only meant to be suggestive of the characteristics found in good research design. Theory base: Good research strategies reflect the theories which are being investigated. Where specific theoretical expectations can be hypothesized these are incorporated into the design. For example, where theory predicts a specific treatment effect on one measure but not on another, the inclusion of both in the design improves discriminant validity and demonstrates the predictive power of the theory. Situational: Good research designs reflect the settings of the investigation. This was illustrated above where a particular need of teachers and administrators was explicitly addressed in the design strategy. Similarly, intergroup rivalry, demoralization, and competition might be assessed through the use of additional comparison groups who are not in direct contact with the original group. Feasible: Good designs can be implemented. The sequence and timing of events are carefully thought out. Potential problems in measurement, adherence to assignment, database construction and the like, are anticipated. Where needed, additional groups or measurements are included in the design to explicitly correct for such problems. Redundant: Good research designs have some flexibility built into them. Often, this flexibility results from duplication of essential design features. For example, multiple replication of a treatment helps to insure that failure to implement the treatment in one setting will not invalidate the entire study. Efficient: Good designs strike a balance between redundancy and the tendency to overdesign. Where it is reasonable, other, less costly, strategies for ruling out potential threats to validity are utilized. This is by no means an exhaustive list of the criteria by which we can judge good research design. Nevertheless, goals of this sort help to guide the researcher toward a final design choice and emphasize important components which should be included. 83 CU IDOL SELF LEARNING MATERIAL (SLM)

6.4 TYPES OF RESEARCH DESIGNS Which one is the best for your study, depends on the purpose and scope of your study. While taking decisions on the research design, a number of factors have to be taken into consideration. These include: Nature of investigation, Data collection methods, Number of contacts made with the subjects, and the Period of reference of your study. Nature of Investigation: Exploratory; Descriptive; Experimental; Semi or Quasi- experimental; Non- experimental; and Field research. Data Collection Methods: Survey; Case studies; and Content analysis; Number of Contacts made with the Subjects: Cross- sectional; Before- and- after; and Longitudinal Retrospective; Prospective; and Retrospective - Prospective. 6.4.1 Based on Nature of Investigation The nature of investigation can be exploratory, descriptive, causal/experimental, semi or quasi- experimental, non-experimental, and field research. Exploration is an important characteristic of research. Any research begins with it when the researcher dives into the unknown and unsolved terrains. He/She starts with a quest for knowing more through exploration. It is an initial foray into the densities of the unknown. Exploration starts with a vague idea of what is intended to be researched. It forms the basis of research. It is not very systematic to the order of research to be undertaken otherwise. It is a flexible approach to undertaking research where the sampling is generally non-probability and the data collection methods are unstructured. It involves a study and analysis of the literature and discussions with peers and fellow colleagues to know their views on the topic. Descriptive research is carried out to provide information about a person, thing or process. It describes the characteristics of an individual, group, organization, or phenomena, conditions, or a situation. The characteristics are described in terms of the dependent variables. Description may be limited to events of past or present but not of the future. In that case it becomes experimental research. Most of the research in social sciences is descriptive in nature. Some examples of descriptive research in LIS are: Services, collection, and infrastructure of a particular library Status of libraries in a geographical area Impact of library services on the performance of students Impact of IT on library services Attitude of users towards staff in libraries in descriptive studies data collection is done through structured methods. Samples are selected by random sampling. The nature of investigation moves systematically from exploratory towards experimental. The degree of investigation goes on increasing as we move ahead. 84 CU IDOL SELF LEARNING MATERIAL (SLM)

Casual investigation in exploration, to description and finally causal investigation in experimental research. It aims to find cause and effect relations between the dependent and independent variables. Experimental research studies the effect of independent variables on dependent variables. The researcher identifies the two different kinds of variables and the relationship between them. For this, he/she reviews the literature on the subject and also related studies done by others. Discussions with peers and other professionals also help in finding out the relationship. Hypotheses are framed for verifying the relationships. The research is conducted under controlled conditions so that the changes in the dependent variables can be attributed solely to the changes in the independent variables. Semi - experimental studies are different from experimental studies in that the sampling in experimental studies is random sampling compared to non - random sampling in semi - experimental or quasi-experimental studies. Non - experimental studies also find out causal relations but they follow the reverse approach. Experimental studies explain the cause - effect relation by identifying the independent variables and later inducing changes in them to find out the resultant effect on the dependent variables. Non- experimental studies ascribe the changes that have taken place in the dependent variables to some independent variables. They do not induce changes in the independent variables to see the effect on dependent variables. This is generally done in the social sciences and the reason for doing so is the population that are human beings compared to physical and chemical entities in sciences. Let us consider an example to clarify the difference. We want to see the effect of use of IT in the classroom on the performance of students. In experimental studies, we would take use of IT in the classroom as the independent variable and the results of students as dependent variable. We would compare the scores of students after introduction of IT to the scores obtained by them earlier and find out the relation. In non-experimental study, we would check the scores of students after IT has been introduced and find out the relation between them by studying the coefficient of correlation. Let us take another example to learn the difference, where we are studying the effect of OPAC on the use of catalogue. We would divide the users randomly into two groups. One group of users is provided the facilities of a traditional catalogue for access to the literature. The other group is provided the facility of an OPAC to access the literature. We would measure the use of catalogue in the two cases and ascribe the difference to OPAC. Field research is done in the natural surroundings in real life situations. Here the main criterion is doing research in social settings rather than on the techniques of research. Let us discuss some observations on field research: “Field research is the design, planning and management of scientific investigations in real-life settings” (Fielder) Kaplan comments that Field research involves direct or indirect observation of behavior in the circumstances in which it occurs without any significant 85 CU IDOL SELF LEARNING MATERIAL (SLM)

intervention on the part of the observer. We can conclude that field research is conducted in real life settings without any modifications done to the settings. There is little stress that the techniques applied are scientific. Importance is given to the fact that the observer collects data while being on the site along with those observed. He is trained to be part of the observed group and objective in recording the observations. Such research is carried out particularly in subjects like sociology or social work. Field studies have been divided into field research and field experiments. Field experiments are different from field research in that the former involve studying the effect of varying independent variables on dependent variables in real life natural settings. The difference between experimental research and field experiment is that the former are conducted in laboratory settings whereas the latter are conducted in natural settings. Thus, the control in the observations is not possible in field experiments, which is possible in laboratory experiments. Based on Data Collection Methods Research Design Research design based on data collection methods is of the following types: survey, case studies, and content analysis. Survey approach is used on a large population. But instead of studying the whole of population, a sample is studied. The sample is generally large in size. It is generally used in descriptive studies; however, it can also be used in experimental studies. The techniques of data collection used are questionnaire and interviews. Questionnaire can be self- administered or mailed. It can be structured or otherwise. In social science research, survey method is generally used. Case study involves studying few cases in contrast to the large sample in survey. But the level of study in case study is intensive which is not true of survey. Example of a case study can be “Automation in University Libraries of North India: A Case Study of University Libraries of Kurukshetra, Punjab, and Jammu”. The study involves taking a sample and studying that in detail. This would enable the researcher to study automation in detail in these libraries than if the study would have taken all University libraries of North India. But the question is whether we can generalize the results of the study and conclude for the whole of North India. Content analysis is another type of data collection method where the data is collected from documentary sources. In this method the contents of documents are analyzed to arrive at a conclusion. This is a method used and particularly useful in historical research. It enables to study the events in present that have taken place in the past. It is only documents like diaries, autobiographies, archival documents that can act as the source of data. Content analysis can be done quantitatively as well as qualitatively. Quantitative analysis involves counting of words or phrases. Qualitative analysis involves analyzing documents to find out the ideas 86 CU IDOL SELF LEARNING MATERIAL (SLM)

behind words. Based on Number of Contacts made with the Subjects Based on the number of contacts made with the subjects, research studies are: cross-sectional, before- and- after, and, longitudinal. Cross- sectional studies are case studies that involve studying a phenomenon at one point in time. These are also called one - shot studies. Examples of these could be: Attitudes of users towards use of IT in libraries Status of LIS education in India Continuing education for librarians in India Research Process These are simple to undertake as they involve contact only once with the population. They have a drawback that they are not suitable for measuring change. Before-and- after study design tries to overcome the disadvantage of cross-sectional studies by taking the observation twice. The observation is done before introducing a change in the independent variable and after introducing a change in the independent variable. Thus, we are able to measure change in the variables over a period of time. Examples of the before and after design are: Impact on users of the use of IT in libraries. Attitudes of users towards use of IT in libraries before and after automation. In the above two examples, observations are made twice, before and after introduction of IT in libraries. It enables to measure change, which is not possible in cross- sectional studies. Longitudinal design tries to overcome the disadvantage of the before-and- after study design. It is able to measure the pattern of change in the dependent variables over a period of time. A number of observations are taken over a population after regular intervals of time, which may vary from a week to even more than a year. In the above two examples, if observations are made over a period of time to know the pattern of impact on users at different stages of automation, the design is called longitudinal design. 6.4.2 Based on Reference Period Based on the period of reference of study, research design has been divided into: retrospective, prospective, and, retrospective- prospective. Retrospective studies study a phenomenon, event, or situation that has happened in the past. Data is collected on the basis of documentary evidence or the respondents’ recall of the situation. Some examples of retrospective studies are: Libraries in Ancient India. Devastation caused by floods to libraries in the 20th century. Employment scenario among LIS Professionals in 1990s. Prospective studies try to predict situations that have yet to take place. They attempt to foresee the future. The studies are concerned with studying the future of a concept, object, organization, or even attitudes of people. These are experimental in nature. Examples of some such studies are: Libraries of the future: How will they exist. Prospects of digital libraries in India. Effect of RFID technology on pilferage of books in college libraries. 87 CU IDOL SELF LEARNING MATERIAL (SLM)

Image of IT savvy librarians. Retrospective- Prospective studies are concerned with the events or phenomena that has happened in the past and predict it for the future. These are like before-after studies with the difference that there is no control group here. The dependent variable is observed before and after variation in independent variable on the same population. Some examples of such studies are: Impact of automation on the use of libraries. Change in attitude of users towards staff after library orientation. Rate of use of helmets by people after heavy fines were imposed by traffic police. Effect of advertisement on billboards on the sale of cars in metropolitan cities of India 6.5 PRINCIPLES OF EXPERIMENTAL RESEARCH DESIGNS The term “experimental design” may be used in two different ways (Kirk, 1968). i) It may be used to refer to the sequence of steps necessary to conduct an experiment (stating the hypothesis, detailing the data collection process, and so on). ii) It may be used to refer to the plan by which subjects are assigned to experimental conditions. The experimental design is relatively simple, as for example, when one group of subjects is exposed to an independent variable and another is not. On the other hand, it may be much more complex, involving two or more than two independent variables and repeated measurements of the dependent variables. The overall blueprint of the experiment is called experimental design. It contains the specification of the plan and structure of the entire experiment. For the sake of precision, the variables and their measures are defined and specific instructions for the experimental conditions are clearly written. A good experimental design minimizes the influence of extraneous or uncontrolled variation and increases the likelihood that an experiment will produce valid and consistent results. BASIC ELEMENTS OF VALID EXPERIMENTAL DESIGN a) Factor: The independent variables of an experiment are often called the factors of experiment. An experiment has always one factor, or independent variables, otherwise it would not be an experiment. It is possible for an experiment to have more than one independent variables. To have an experiment, it is necessary to vary some independent variable, or some factors. b) Level: a level is a particular value of an independent variable. Level refers to the degree or intensity of a factor. Any factor may be presented in one or more of several levels, including a zero level. c) Condition is the broadest term used to discuss independent variables. It refers to a particular way in which subjects are treated. 88 CU IDOL SELF LEARNING MATERIAL (SLM)

D) MAIN EFFECT: Main effect is the effect of one independent variable, averaged over all levels of another independent variable. E) INTERACTION; when the effect of one independent variable depends on the level of another independent variable. F) TREATMENT: the treatment is used to refer to a particular set of experimental condition. For example 2X2 factorial experiment, the subjects are assigned to for the treatment. In experiments, a treatment is something that researchers administer to experimental units. Two particular elements of a design provide control over so many different threats to validity that they are basic to good experimental designs; (1) the existence of a control group or a control condition and, (2) the random allocation of subjects to groups. Random allocation ensures that the groups will be equal in all respects, except as they may differ by chance and control over the internal threats to validity; allows one to conclude that dependent variable is associated with independent variable and not with any other variables. In discussing experimental design, Campbell & Stanley (1963) have used some symbols with which a student/reader is expected to be acquainted. R: Random selection of subjects or random assignment of treatment to experimental groups. X: Treatment or experimental variable which is manipulated. When treatments are compared they are levelled as X1, X2, X3 and so on. O: Observation or measurement or test. Where there is more than one O, an arbitrary subscript O1, O2, O3 and so on, is used. Professor Fisher has enumerated three principles of experimental designs. (1) The Principle of Replication (2) The Principle of Randomization (3) The principle of Local Control. According to the Principle of Replication, the experiment should be repeated more than once. Thus, each treatment is applied in many experimental units instead of one. By doing so the statistical accuracy of the experiments is increased. However, it should be remembered that replication is introduced in order to increase the precision of a study; to say, to increase the accuracy with which the main effects and interactions can be estimated. The Principle of Randomization provides protection, when we conduct an experiment, against the effects of extraneous factors by randomization. 89 CU IDOL SELF LEARNING MATERIAL (SLM)

The Principle of Local Control shows the extraneous factor, the known source of variability, is made to vary deliberately over as wide a range as necessary and this needs to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error. (Kothari, C.R.- Research Methodology) 6.6 FORMAL AND INFORMAL EXPERIMENTAL DESIGNS Kothari has given important experimental designs. According to him Experimental design refers to the framework or structure of an experiment. They can be classified into two broad categories i.e. 1. informal experimental design. 2. formal experimental design. Informal experimental designs are those designs that normally use a less sophisticated form of analysis based on differences in magnitudes, whereas formal experimental designs offer relatively more control and use precise statistical procedures for analysis- (i) Informal Experimental designs:- (a) Before and after without control design. (b) After only with control design. (c) Before and after with control design (ii) Formal Experimental Designs:- (a) Completely randomized design (C.R. design) (b) Randomized block design (R.B. design) 49 (c) Latin Square design (L.S. design) (d) Factorial design We may briefly deal with each of the above stated informal as well as formal experimental designs. 1. Before-and-after without control design: In such a design a single test group or area is selected and the dependent variable is measured before the introduction of the treatment. The treatment is then introduced and the dependent variable is measured again after the treatment has been introduced. The effect of the treatment would be equal to the level of the phenomenon after the treatment minus the level of the phenomenon before the treatment. The design can be represented thus: Figure 6.1 90 CU IDOL SELF LEARNING MATERIAL (SLM)

2. The main difficulty of such a design is that with the passage of time considerable extraneous variations may be there in its treatment effect. 3. After-only with control design: In this design two groups or areas (test area and control area) are selected and the treatment is introduced into the test area only. The dependent variable is then measured in both the areas at the same time. Treatment impact is assessed by subtracting the value of the dependent variable in the control area from its value in the test area. This can be exhibited in the following form: Figure 6.2 The basic assumption in such a design is that the two areas are identical with respect to their behavior towards the phenomenon considered. If this assumption is not true, there is the possibility of extraneous variation entering into the treatment effect. However, data can be collected in such a design without the introduction of problems with the passage of time. In this respect the design is superior to before-and-after without control design. 4. Before-and-after with control design: In this design two areas are selected and the dependent variable is measured in both the areas for an identical time-period before the treatment. The treatment is then introduced into the test area only, and the dependent variable is measured in both for an identical time-period after the introduction of the treatment. The treatment effect is determined by subtracting the change in the dependent variable in the control area from the change in the dependent variable in test area. This design can be shown in this way: Figure 6.3 91 CU IDOL SELF LEARNING MATERIAL (SLM)

This design is superior to the above two designs for the simple reason that it avoids extraneous variation resulting both from the passage of time and from non-comparability of the test and control areas. But at times, due to lack of historical data, time or a comparable control area, we should prefer to select one of the first two informal designs stated above. 5. Completely randomized design (C.R. design): Involves only two principles viz., the principle of replication and the principle of randomization of experimental designs. It is the simplest possible design and its procedure of analysis is also easier. The essential characteristic of the design is that subjects are randomly assigned to experimental treatments (or vice-versa). For instance, if we have 10 subjects and if we wish to test 5 under treatment A and 5 under treatment B, the randomization process gives every possible group of 5 subjects selected from a set of 10 an equal opportunity of being assigned to treatment A and treatment B. One-way analysis of variance (or one-way ANOVA) is used to analyze such a design. Even unequal replications can also work in this design. It provides maximum number of degrees of freedom to the error. Such a design is generally used when experimental areas happen to be homogeneous. Technically, when all the variations due to uncontrolled extraneous factors are included under the heading of chance variation, we refer to the design of experiment as C.R. design. We can present a brief description of the two forms of such a design as given above figure.  Two-group simple randomized design: In a two-group simple randomized design, first of all the population is defined and then from the population a sample is selected randomly. Further, requirement of this design is that items, after being selected randomly from the population, be randomly assigned to the experimental and control groups (Such random assignment of items to two groups is technically described as principle of randomization). Thus, this design yields two groups as representatives of the population. In a diagram form this design can be shown in this way: Two-group simple randomized experimental design (in diagram form) 92 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 6.4  Since in the sample randomized design the elements constituting the sample are randomly drawn from the same population and randomly assigned to the experimental and control groups, it becomes possible to draw conclusions on the basis of samples applicable for the population. The two groups (experimental and control groups) of such a design are given different treatments of the independent variable. This design of experiment is quite common in research studies concerning behavioral sciences. The merit of such a design is that it is simple and randomizes the differences among the sample items. But the limitation of it is that the individual differences among those conducting the treatments are not eliminated, i.e., it does not control the extraneous variable and as such the result of the experiment may not depict a correct picture. This can be illustrated by taking an example. Suppose the researcher wants to compare two groups of students who have been randomly selected and randomly assigned. Two different treatments viz., the usual training and the specialized training are being given to the two groups. The researcher hypothesizes greater gains for the group receiving specialized training. To determine this, he tests each group before and after the training, and then compares the amount of gain for the two groups to accept or reject his hypothesis. This is an illustration of the two-groups randomized design, wherein individual differences among students are being randomized. But this does not control the differential effects of the extraneous independent variables (in this case, the individual differences among those conducting the training programme). Random replication design (in diagram form) 93 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 6.5  Random replications design: The limitation of the two-group randomized design is usually eliminated within the random replications design. In the illustration just cited above, the teacher differences on the dependent variable were ignored, i.e., the extraneous variable was not controlled. But in a random replications design, the effect of such differences is minimized (or reduced) by providing a number of repetitions for each treatment. Each repetition is technically called a ‘replication’. Random replication design serves two purposes viz., it provides controls for the differential effects of the extraneous independent variables and secondly, it randomizes any individual differences among those conducting the treatments. Diagrammatically we can illustrate the random replications design thus: Above From the diagram it is clear that there are two populations in the replication design. The sample is taken randomly from the population available for study and is randomly assigned to, say, four experimental and four control groups. Similarly, sample 94 CU IDOL SELF LEARNING MATERIAL (SLM)

is taken randomly from the population available to conduct experiments (because of the eight groups eight such individuals be selected) and the eight individuals so selected should be randomly assigned to the eight groups. Generally, equal number of items are put in each group so that the size of the group is not likely to affect the result of the study. Variables relating to both population characteristics are assumed to be randomly distributed among the two groups. Thus, this random replication design is, in fact, an extension of the two-group simple randomized design. 6. Randomized block design (R.B. design) is an improvement over the C.R. design. In the R.B. design the principle of local control can be applied along with the other two principles of experimental designs. In the R.B. design, subjects are first divided into groups, known as blocks, such that within each group the subjects are relatively homogeneous in respect to some selected variable. The variable selected for grouping the subjects is one that is believed to be related to the measures to be obtained in respect of the dependent variable. The number of subjects in a given block would be equal to the number of treatments and one subject in each block would be randomly assigned to each treatment. In general, blocks are the levels at which we hold the extraneous factor fixed, so that its contribution to the total variability of data can be measured. The main feature of the R.B. design is that in this each treatment appears the same number of times in each block. The R.B. design is analyzed by the two-way analysis of variance (two-way ANOVA) * technique. Let us illustrate the R.B. design with the help of an example. Suppose four different forms of a standardized test in statistics were given to each of five students (selected one from each of the five I.Q. blocks) and following are the scores which they obtained. Figure 6.6 If each student separately randomized the order in which he or she took the four tests (by using random numbers or some similar device), we refer to the design of this experiment as a R.B. design. The purpose of this randomization is to take care of such possible 95 CU IDOL SELF LEARNING MATERIAL (SLM)

extraneous factors (say as fatigue) or perhaps the experience gained from repeatedly taking the test. 7. Latin square design (L.S. design) is an experimental design very frequently used in agricultural research. The conditions under which agricultural investigations are carried out are different from those in other studies for nature plays an important role in agriculture. For instance, an experiment has to be made through which the effects of five different varieties of fertilizers on the yield of a certain crop, say wheat, it to be judged. In such a case the varying fertility of the soil in different blocks in which the experiment has to be performed must be taken into consideration; otherwise the results obtained may not be very dependable because the output happens to be the effect not only of fertilizers, but it may also be the effect of fertility of soil. Similarly, there may be impact of varying seeds on the yield. To overcome such difficulties, the L.S. design is used when there are two major extraneous factors such as the varying soil fertility and varying seeds. The Latin-square design is one wherein each fertilizer, in our example, appears five times but is used only once in each row and in each column of the design. In other words, the treatments in a L.S. design are so allocated among the plots that no treatment occurs more than once in any one row or any one column. The two blocking factors may be represented through rows and columns (one through rows and the other through columns). The following is a diagrammatic form of such a design in respect of, say, five types of fertilizers, viz., A, B, C, D and E and the two blocking factor viz., the varying soil fertility and the varying seeds: Figure 6.7 The above diagram clearly shows that in a L.S. design the field is divided into as many blocks as there are varieties of fertilizers and then each block is again divided into as many parts as there are varieties of fertilizers in such a way that each of the fertilizer variety is used in each of the block (whether column-wise or row-wise) only once. The 96 CU IDOL SELF LEARNING MATERIAL (SLM)

analysis of the L.S. design is very similar to the two-way ANOVA technique. The merit of this experimental design is that it enables differences in fertility gradients in the field to be eliminated in comparison to the effects of different varieties of fertilizers on the yield of the crop. But this design suffers from one limitation, and it is that although each row and each column represents equally all fertilizer varieties, there may be considerable difference in the row and column means both up and across the field. This, in other words, means that in L.S. design we must assume that there is no interaction between treatments and blocking factors. This defect can, however, be removed by taking the means of rows and columns equal to the field mean by adjusting the results. Another limitation of this design is that it requires number of rows, columns and treatments to be equal. This reduces the utility of this design. In case of (2 × 2) L.S. design, there are no degrees of freedom available for the mean square error and hence the design cannot be used. If treatments are 10 or more, than each row and each column will be larger in size so that rows and columns may not be homogeneous. This may make the application of the principle of local control ineffective. Therefore, L.S. design of orders (5 × 5) to (9 × 9) are generally used. 8. Factorial designs: Factorial designs are used in experiments where the effects of varying more than one factor are to be determined. They are especially important in several economic and social phenomena where usually a large number of factors affect a particular problem. Factorial designs can be of two types: simple factorial designs and complex factorial designs. We take them separately Simple factorial designs: In case of simple factorial designs, we consider the effects of varying two factors on the dependent variable, but when an experiment is done with more than two factors, we use complex factorial designs. Simple factorial design is also termed as a ‘two-factor-factorial design’, whereas complex factorial design is known as ‘multifactor- factorial design.’ Simple factorial design may either be a 2 × 2 simple factorial design, or it may be, say, 3 × 4 or 5 × 3 or the like type of simple factorial design. We illustrate some simple factorial designs as under: Illustration: (2 × 2 simple factorial design). 97 CU IDOL SELF LEARNING MATERIAL (SLM)

A 2 × 2 simple factorial design can graphically be depicted as follows: Figure 6.8 In this design the extraneous variable to be controlled by homogeneity is called the control variable and the independent variable, which is manipulated, is called the experimental variable. Then there are two treatments of the experimental variable and two levels of the control variable. As such there are four cells into which the sample is divided. Each of the four combinations would provide one treatment or experimental condition. Subjects are assigned at random to each treatment in the same manner as in a randomized group design. The means for different cells may be obtained along with the means for different rows and columns. Means of different cells represent the mean scores for the dependent variable and the column means in the given design are termed the main effect for treatments without taking into account any differential effect that is due to the level of the control variable. Similarly, the row means in the said design are termed the main effects for levels without regard to treatment. Thus, through this design we can study the main effects of treatments as well as the main effects of levels. An additional merit of this design is that one can examine the interaction between treatments and levels, through which one may say whether the treatment and levels are independent of each other or they are not so. The following examples make clear the interaction effect between treatments and levels. The data obtained in case of two (2 × 2) simple factorial studies may be as given in below. 98 CU IDOL SELF LEARNING MATERIAL (SLM)

Figure 6.9 All the above figures (the study I data and the study II data) represent the respective means. Graphically, these can be represented as shown in below. Figure 6.9 The graph relating to Study I indicates that there is an interaction between the treatment and the level which, in other words, means that the treatment and the level are not independent of each other. The graph relating to Study II shows that there is no interaction effect which means that treatment and level in this study are relatively independent of each other. 99 CU IDOL SELF LEARNING MATERIAL (SLM)

The 2 × 2 design need not be restricted in the manner as explained above i.e., having one experimental variable and one control variable, but it may also be of the type having two experimental variables or two control variables. For example, a college teacher compared the effect of the class size as well as the introduction of the new instruction technique on the learning of research methodology. For this purpose he conducted a study using a 2 × 2 simple factorial design. His design in the graphic form would be as follows: Figure 6.10 But if the teacher uses a design for comparing males and females and the senior and junior students in the college as they relate to the knowledge of research methodology, in that case we will have a 2 × 2 simple factorial design wherein both the variables are control variables as no manipulation is involved in respect of both the variables. Illustration: (4 × 3 simple factorial design). The 4 × 3 simple factorial design will usually include four treatments of the experimental variable and three levels of the control variable. Graphically it may take the following form: Figure 6.11 This model of a simple factorial design includes four treatments viz., A, B, C, and D of the experimental variable and three levels viz., I, II, and III of the control variable and has 12 100 CU IDOL SELF LEARNING MATERIAL (SLM)