CU-BBA-SEM IV- RESEARCH METHODOLOGY-SECOND DRAFT

6.2.1 Primary research methods Primary research involves gathering information directly from the subject. Whether it is done by a group of people or even by a single person, such research can be carried out directly by the researcher himself, or the researcher can hire a third party to carry out the research on his or her behalf. Primary research is conducted specifically to investigate a specific problem that necessitates a more in-depth investigation. Surveys/polls: Information is gathered from a predefined group of respondents using surveys and polls, which are used to gather information from a predefined group of respondents. It is considered to be one of the most important quantitative techniques. Various types of surveys or polls can be used to gather information about people's opinions, trends, and other topics. Because of technological advancements, surveys can now be sent online and made very accessible to those who wish to participate. For example, the use of a survey app on tablets, laptops, or even mobile phones is acceptable. As well as being accessible in real time, this information is also available to researchers. In order to achieve higher response rates, most organisations now offer short-form surveys with incentives for participants. For example, a survey may be sent to a specific group of people in order to learn their thoughts on the size of mobile phones before they purchase one. An organisation can dig deeper into a topic and make business-related decisions based on the information provided by such sources. Interviews: A qualitative research method is used in this type of investigation. When you conduct an interview with a subject matter expert, you will gain valuable insights that you will not be able to get from a generalised public source. Interviews are conducted in person or over the phone, and open-ended questions are asked in order to obtain meaningful information about the subject matter. For example, An interview with an employee can provide you with additional insights into the level of job satisfaction that the employee is experiencing, and an interview with a subject matter expert in quantum theory can provide you with in-depth information on that topic. Focus groups: In such a method, a group of people is selected and given the opportunity to express their thoughts on the subject matter under investigation. However, when selecting participants for a focus group, it is critical to ensure that they all come from a similar background and have had similar life experiences. Example: A study of this nature can assist the researcher in better understanding what consumers value when purchasing a phone. It could be a matter of screen size, brand value, 101

or even the physical dimensions. The organisation can gain an understanding of consumer purchasing attitudes, consumer opinions, and so on based on this information. Observations: Observational research can be classified as either quantitative or quantitative observation. Such research is carried out in order to observe a person and derive conclusions from their responses to various parameters. There is no direct interaction with the subject in this type of research. For example: In the case of an FMCG company, it would like to know how its customers react to a new shape of their product. The researcher observes the customer's initial reaction and collects the information, which is then used to draw inferences from the collected information, as explained above. 6.2.2 Secondary research methods Secondary research is the process of gathering information from primary research that has already been published. In this type of research, you gather information from a variety of sources, such as case studies, magazines, newspapers, and books. Online research: A great deal of information is readily available on the internet, and the researcher can download it whenever he or she needs it. An important consideration for such a study is the genuineness and authenticity of the source websites from which the information is being gathered by the researcher. As an illustration: A researcher is trying to figure out what percentage of people prefer a specific brand of phone over another brand. Using a search engine, the researcher simply types in the information he requires and receives multiple links containing related information and statistics. Literature research: In this section, we will discuss literature research, which is one of the most cost-effective methods of discovering a hypothesis. Library catalogues, online sources, and even commercial databases contain a vast amount of information that can be accessed by anyone. Newspapers, magazines, books from the library, documents from government agencies, specific topic-related articles, literature, Annual reports, published statistics from research organisations, and so on are all examples of sources to draw upon for research. While researching from these sources, there are a few considerations to keep in mind. Government agencies have reliable information, but it may be necessary to pay a small fee in order to obtain it. Furthermore, research conducted by educational institutions is generally overlooked, despite the fact that educational institutions conduct more research than any other entity. 102

Furthermore, commercial sources provide information on major topics such as political agendas, demographics, financial information, market trends and information, and so on. Commercial sources also provide information on minor topics such as sports and entertainment. For example, a company's sales are below average. When a problem is related to the market or an organisation, it can be easily investigated using available statistics and market literature; however, when the topic being studied is related to the financial situation of the country, research data can be accessed through government documents or commercial sources. Case study research: Case study research can assist a researcher in obtaining additional information by carefully analysing existing cases that have experienced a similar problem to the one being investigated. As a result, such analyses are extremely important and critical, particularly in today's business environment. The researcher only needs to make certain that he thoroughly examines the case in comparison to his own case, taking into consideration all of the variables present in the previous case. The use of this term is very common in business organisations and the social sciences sector, as well as in the health care sector. To give an example, a specific orthopaedic surgeon has the highest success rate when it comes to performing knee surgeries. More than a few other hospitals or doctors have taken up this case in order to better understand and benchmark the method in which this surgeon performs the procedure in order to improve their own success rates. 6.3 STEPS TO CONDUCT AN EXPLORATORY RESEARCH In this section, we'll go over five steps to take when conducting exploratory research. The steps are very similar to the steps that would be taken in any marketing research study in practice. 1) Determine the nature of the problem. Determine the nature of the problem you wish to solve. 2) Construct your hypotheses. Create your hypothesis; it will serve as a guideline for your research efforts. On the basis of the results of your research, you will be able to either refute or prove your hypothesis. 3) Decide on your research methodology. You can conduct your research using one or more of the methods 4) Carry out the investigation Take your time to look through various sources and ensure that you are not overlooking any important aspects of the investigation. 103

5) Examine the outcomes of the experiment For the purpose of analysing market research data, there are numerous methods available. You can use those methods to narrow down the outcome of your hypothesis and determine whether it is correct or incorrect. 6.4 CHARACTERISTICS OF EXPLORATORY RESEARCH  They are not research studies in the traditional sense.  It is typically low-cost, interactive, and open-ended in nature.  It will enable a researcher to answer questions such as \"What is the problem?\" and \"How can I solve it?\" What is the overall goal of the research? And what kinds of subjects could be studied?  In order to conduct exploratory research, there must either be no prior research done or the existing research must not provide a precise enough answer to the problem.  It is a time-consuming research project that necessitates patience while also posing risks to participants.  The researcher will have to go over all of the information that is available for the specific study that he is conducting.  There is no set of rules for conducting the research in and of themselves because the rules are flexible, broad, and dispersed.  It is necessary for the research to be of significance or value. It is ineffective to conduct research if the problem is not important to the industry in which it is conducted.  In addition, the research should include a few theories that can be used to support its findings, as this will make it easier for the researcher to evaluate them and move forward with his research.  Such research typically yields qualitative information, but in some cases quantitative information can be generalised to a larger sample size through the use of surveys and experiments. 6.5 IMPORTANCE OF EXPLORATORY RESEARCH Exploratory research assists a researcher in developing a better understanding of the problem under investigation. The purpose of conducting this research is to learn more about the problem and the surrounding areas, rather than to reach a conclusion about it. This type of research assists a researcher in establishing a strong foundation for his or her research as well as in selecting the most appropriate research design and variables that are critical to the analysis. Exploratory research allows a researcher to save time and resources while also determining the feasibility of continuing the research in the future. 104

6.6 ADVANTAGES OF EXPLORATORY RESEARCH 1. The researcher has a great deal of adaptability and can change his or her mind as the research progresses. 2. It is typically inexpensive. 3. It assists in laying the groundwork for a research project, which may lead to additional research. 4. It enables the researcher to determine at an early stage whether or not the topic is worth investing time and resources in, as well as whether or not it is worth pursuing. 5. It can assist other researchers in identifying possible causes for the problem, which can then be investigated in greater depth to determine which of these causes is the most likely cause of the problem in question. 6.7 DISADVANTAGES OF EXPLORATORY RESEARCH 1. Even though it can point you in the right direction towards what is the answer, it is usually inconclusive. 2. The main disadvantage of exploratory research is that they provide qualitative data. Interpretation of such information can be judgmental and biased. 3. Most of the times, exploratory research involves a smaller sample, hence the results cannot be accurately interpreted for a generalised population. 4. Many a times, if the data is being collected through secondary research, then there is a chance of that data being old and is not updated. 6.8 MEANING OF DESCRIPTIVE RESEARCH Descriptive research is a type of research that provides an in-depth description of the phenomenon or population under study. Descriptive research is neither in the category of qualitative research nor in the class of quantitative research, but it uses the features of both types of research. Descriptive research is defined as a research method that aims to describe the characteristics of the population or phenomenon under investigation. When it comes to the research subject, this methodology is more concerned with the \"what\" of the research subject than it is with the \"why.\" The descriptive research method is primarily concerned with describing the characteristics of a demographic segment, rather than addressing the question of \"why\" a particular phenomenon takes place. In other words, it \"describes\" the subject of the research without addressing the question of \"why\" something occurs. In order to better understand the fashion purchasing trends among New York buyers, an apparel company might first conduct a demographic survey of the region, gather population 105

data, and then conduct descriptive research on this particular demographic segment. The study will then reveal specifics about \"what is the purchasing pattern of New York buyers,\" but it will not include any investigative information about \"why\" the patterns are no longer in place. In order for the apparel brand attempting to break into this market to be successful, it is necessary to first understand the nature of their target market. 6.9 TYPES/METHODS OF DESCRIPTIVE RESEARCH The following are three types of descriptive research. 1. Case study method In the case study method, the subject, person, or case that is being studied is thoroughly and meticulously investigated and documented. A case study is a formal research method that is used to carry out the investigation. Research hypothesis can be established based on the findings of the case study, which can then be used to broaden the scope of the research and expand the research horizons. Case study research, on the other hand, is not appropriate for determining the relationship between cause and effect because it does not produce accurate results. Furthermore, the outcome of the case study method is specific to the case in question and similar cases, and it cannot be applied to other cases. Typically, case studies are centred on interesting and unusual cases that are both complex and challenging, and they serve to provide additional information about a specific case. Researchers may conduct a medical case study, for example, in order to gain more knowledge about a rare medical condition. In a similar vein, case study methods are employed by scientists to gain insight into unusual phenomena. 2. Observational Method A type of non-experimental research, observational research can be defined as follows: As defined by the National Institutes of Health, observational research is a type of research in which a researcher keeps track of the ongoing behaviours of the subject being studied. Observational research is widely used in marketing and social science fields, among other disciplines. In observational research, the researcher observes the actions of the subjects under investigation while they are in their natural environment. Observational methods are distinct from experimental research methods in that experimental research methods involve the creation of an artificial environment for the subjects under investigation to be studied. Naturalistic observation and participant observation are the two types of observational studies that can be conducted. A naturalistic observation study is one in which subjects are observed while they are acting in their natural environment. Participants in a research study who are aware that they are being observed are referred to as participants in participant observation. They are asked to take part in the observation study 106

that is being conducted. Observational research methods are well suited for studying the behaviour of subjects who are the subjects of the research. This research, on the other hand, is unable to provide information about the actual causes of the behaviours of the subjects who are being studied. 3. Survey Research Survey research is one of the most popular and straightforward methods of obtaining information or collecting data available today. Questionnaires containing questions related to the research problem are prepared on paper or in any digital format, and they are distributed to participants. These questionnaires are distributed to a random sample of people in the hopes of obtaining an accurate representation of their opinions. When conducting university research or business research, the survey research method is widely used. Survey research, also known as primary research, can be used in conjunction with other research methods to produce more accurate results. Furthermore, the information gathered through survey research can be used as secondary research data by other researchers. 6.10 CHARACTERISTICS OF DESCRIPTIVE RESEARCH The term descriptive research refers to the questions that were asked, the design of the study, and the data analysis that was done on a particular topic. We refer to it as an observational research method because none of the variables in the research study are influenced in any way by the researcher. The following are some characteristics that distinguish descriptive research from other types of research: Quantitative research: Descriptive research is a quantitative research method that aims to collect quantifiable information from a population sample in order to conduct statistical analysis on the sample. Descriptive research is a subset of quantitative research. It is a widely used market research tool that allows us to collect and describe the characteristics of a demographic segment. Uncontrolled variables: In descriptive research, none of the variables are influenced in any way by the researcher. The research is carried out through the use of observational methods. As a result, the researcher has no control over the nature of the variables or the behaviour of the variables. Cross-sectional studies: Generally speaking, descriptive research is a cross-sectional study in which different sections belonging to the same group are studied in different ways. 107

The basis for further research: The data collected and analysed from descriptive research serve as the foundation for further investigation by researchers who employ a variety of research techniques to further investigate the findings. The information can also be used to determine the types of research methods that will be used in the subsequent research. 6.11 APPLICATIONS OF DESCRIPTIVE RESEARCH A descriptive research method can be applied in a variety of situations and for a variety of reasons. Prior to beginning any survey, it is critical to consider the survey objectives and survey design in order to ensure success. Despite following these steps, there is no way to predict whether or not one will achieve the desired research result. What is descriptive research and how does it work? To better understand the end goal of research goals, consider the following examples of how organisations currently use descriptive research today: Define respondent characteristics: Perhaps there is a requirement to identify patterns, characteristics, and behaviours among the respondents. Also possible is to determine from a respondent's attitude or opinion about the phenomenon. For example, determining from millennials how many hours per week they spend on the internet browsing is important. All of this information assists the organisation conducting the research in making well-informed business decisions. Measure data trends: Using the statistical capabilities of a descriptive research design, researchers can track data trends over time. Consider the case of an apparel company conducting market research on different demographics, such as age groups ranging from 24-35 and 36-45, in preparation for the launch of a new autumn wear collection. If one of those groups does not react positively to the new launch, it will provide valuable insight into what types of clothing are popular and which are not. The brand discontinues the clothing and accessories that customers do not like. Conduct comparisons: Companies also use descriptive research designs to better understand how different groups respond to a specific product or service. Comparative studies are another type of descriptive research design. For example, an apparel company might design a survey that asks general questions about the brand's image and then analyses the results. In the same study, demographic questions such as age, income, gender, geographic location, and so on are asked as well. This consumer research assists the organisation in determining which aspects of the brand are appealing to the general public and which aspects are not appealing to the general public. It also aids in the development of product or marketing improvements, as well as the development of a new product line to cater to high growth potential groups. Validate existing conditions: 108

Descriptive research is widely used by researchers to ascertain the prevailing conditions and underlying patterns of the research object under investigation. Because of the non-invasive nature of the research method, as well as the use of quantitative observation and some aspects of qualitative observation, researchers are able to observe each variable and conduct a thorough analysis. Researchers also use it to confirm the presence of any existing conditions that may be prevalent in a given population, according to the findings. Conduct research at different times: The analysis can be carried out at various times to determine whether there are any similarities or differences between the results. This also allows for the evaluation of an unlimited number of variables. Studies on current conditions can also be repeated to identify trends, which can be used for verification. 6.12 ADVANTAGES OF DESCRIPTIVE RESEARCH The following are some of the most significant advantages of descriptive research: 1. Because descriptive research gathers information from a large number of people, the information obtained from descriptive research is useful in important decision-making. Because the descriptive survey method allows for the collection of statistical information, which can then be used to deduce the desired results through analysis of the data. 2. Different descriptive research methods, such as surveys, observation, and vase study, can be used to collect a diverse range of information. These three research methods provide different types of data that can be used to analyse a research problem depending on which method is used. For example, the case study research method can be used to develop a hypothesis about a specific research problem that is being investigated. 3. One advantage of descriptive research over other research methods is that it is both inexpensive and time-efficient to carry out descriptive studies. You are not required to have a fantastic workspace dedicated solely to research. Natural settings are ideal for descriptive research, such as observation research, and you can distribute surveys to people online or have them answered by random people at your business or other public locations. 4. Descriptive research yields both quantitative and qualitative information about a subject. The variety of data available allows for a more comprehensive understanding of the research problem. 5. Descriptive research can be carried out in a variety of natural settings. A dedicated space is not required for any of the descriptive research methods to be used in order to carry out the investigation. 109

6.13 DISADVANTAGES OF DESCRIPTIVE RESEARCH 1. Descriptive methods only provide answers to the question \"what,\" but they do not provide answers to the questions \"why\" and \"how.\" In order to establish cause and effect relationships, descriptive research methods are not appropriate. 2. Descriptive methods are heavily reliant on the responses of participants. When people are aware that they are being watched, there is a possibility that they will not act in their true selves. In the case of the survey method, there is a possibility that some people will not answer the questions truthfully, resulting in the output of the descriptive research study being deemed ineffective. In order to ensure that the results derived from this type of information are accurate. 3. The halo effect is yet another issue that can arise in descriptive research studies. If a researcher has a personal relationship with a participant, he or she may be biased. Observations made in this manner would be deemed invalid by the scientific community. 4. Participants in descriptive research methods are chosen at random from a pool of candidates. The sample's randomness makes it impossible to accurately represent the entire population. 6.14 SUMMARY  Exploratory research is conducted on problems that have not been investigated clearly and there is not much information available on it. the purpose of conducting exploratory research is to develop more understanding about the problem and there is no surety that the research will provide any conclusive outcomes.  There are two types of research that can be conducted: primary research and secondary research.  Primary research is conducted specifically to investigate a specific problem that necessitates a more in-depth investigation  Secondary research is the process of gathering information from primary research that has already been published  Descriptive research is a type of research that provides an in-depth description of the phenomenon or population under study. Descriptive research is neither in the category of qualitative research nor in the class of quantitative research, but it uses the features of both types of research. 6.15 KEYWORDS  Exploratory research: Exploratory research is defined as research that is used to investigate a problem that has not yet been clearly defined by the researcher  Survey: A survey gathers information from a set of people, with the purpose of generalizing the results to a larger population. 110

 Interview: An interview is a procedure designed to obtain information from a person through oral responses to oral inquiries.  Experimental research: Experimental research is used to establish a relationship between the cause and effect of a situation in a laboratory setting.  Focus groups: a group of people assembled to participate in a discussion about a product before it is launched, or to provide feedback on a political campaign, television series, etc.  Observations: an act or instance of viewing or noting a fact or occurrence for some scientific or other special purpose  Case study: A case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 6.16LEARNING ACTIVITY 1. The observer–participant relationship is an important considerationin the design of observation studies. What kindof relationship would you recommend in each of the followingcases? AObservations of professional conduct in the classroomby the student author of a course evaluation guide. BObservation of retail shoppers by a researcher who isinterested in determining customer purchase time bytype of goods purchased. 6.17 UNIT END QUESTIONS A. Descriptive Questions Short Questions 1. Write short notes on Exploratory Research 2. What do you mean by Descriptive Research? 3. Define Interview 4. What do you mean by Case study method? 5. How will you collect data through Observational research? Long Questions 1. Describe the steps to conduct an Exploratory research 2. State the applications of Exploratory research 3. List the advantages and disadvantages of descriptive research 4. Explain the types of descriptive research. 111

5. Compare the advantages and disadvantages of the survey to those of observation. Under which circumstances could you make acase for using observation? B. Multiple Choice Questions 1. ______ is defined as research that is used to investigate a problem that has not yet been clearly defined by the researcher a. Exploratory Research b. Descriptive Research c. Diagnostic Research d. Casual Research 2. ______ is conducted specifically to investigate a specific problem that necessitates a more in-depth investigation. a. Qualitative Research b. Primary Research c. Secondary Research d. Data verification strategy 3. _________a group of people is selected and given the opportunity to express their thoughts on the subject matter under investigation a. Focus groups b. Observations c. Online research d. Survey method 4. This is a type of research that provides an in-depth description of the phenomenon or population under study. a. Experimental research b. Descriptive research c. Explanatory research d. Diagnostic research 5. Identify the research type which is inexpensive and time efficient. a. Diagnostic research b. Exploratory research c. Descriptive research d. All of these Answers 1-b, 2-b, 3-a, 4-b, 5-c 112

6.18 REFERENCES References book  R1, Business Research Methods – Alan Bryman& Emma Bell, Oxford University Press.  R2, Research Methodology - C.R. Kothari  R2, Statistics for Managers Using Microsoft Excel, Levine Stephan, Krehbiel Berenson Textbook references  T1,SPSSExplained,ISBN:9780415274104,Publisher:TataMcgrawHill  T2, Sancheti&Kapoor,BusinessMathematics,SultanChand,NewDelhi 113

UNIT 7: TYPES OF RESEARCH DESIGN - 2 STRUCTURE 7.0 Learning objectives 7.1 Meaning of Experimental Research 7.2 Methods of Experimental Research 7.2.1 pre-experimental research design 7.2.2 True experimental research design 7.2.3 Quasi-experimental research design 7.3 Characteristics of Experimental Research 7.4 Importance of Experimental Research 7.5 Advantages of Experimental Research 7.6 Disadvantages Experimental Research 7.7 Meaning of Diagnostic research 7.8 Characteristics of Diagnostic research 7.9 Advantages of Diagnostic research 7.10 Disadvantages of Diagnostic research 7.11 Summary 7.12 Keywords 7.13 Learning Activity 7.14 Unit End Questions 7.15 References 7.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  Explain briefly the methods of Experimental Research  Describe in detail about the pros and cons of Experimental Research  Explain the characteristics of Diagnostic Research  List the characteristics of Experimental Research  Explain the diagnostic research process 114

7.1 MEANING OF EXPERIMENTAL RESEARCH Definition: Experimental research is research that is carried out using a scientific approach and involves the use of two sets of variables. The first set serves as a constant, and the second set is used to calculate the differences between the first and second sets. Experimentation is common in quantitative research methods, for example. If you don't have enough data to back up your decisions, you must first establish the truth of the situation. Experimental research collects the information necessary to assist you in making more informed decisions. Experimental methods are used in any research that is conducted under scientifically acceptable conditions. The success of experimental studies is dependent on the researchers' ability to demonstrate that the change in a variable is solely due to the manipulation of a constant variable. The investigation should uncover a significant cause and effect relationship. It is possible to conduct experimental research in the following circumstances: 1. When establishing a causal relationship between two events, the passage of time is critical. 2. The relationship between cause and effect is characterised by constant behaviour. 3. You wish to comprehend the significance of the relationship between cause and effect. 7.2 METHODS OF EXPERIMENTAL RESEARCH Experimentation design can be defined as \"the methods used to collect data in experimental studies,\" according to the classic definition. There are three primary types of experimental design: 1. Pre-experimental research design 2. True experimental research design 3. Quasi-experimental research design The type of design is determined by how you categorize research subjects, whether it is by conditions or by groups of subjects. 7.2.1 pre-experimental research design Following the implementation of factors of cause and effect, a group, or a number of groups, are kept under observation. You will conduct this research in order to determine whether or not further investigation is required for these specific populations. It is possible to further categorise pre-experimental research into three types: 115

 One-shot Case Study Research Design  One-group Pretest-posttest Research Design  Static-group Comparison 7.2.2 True experimental research design In order to prove or disprove a hypothesis, true experimental research must rely on statistical analysis, making it the most accurate type of research. Only true experimental design, out of the various types of experimental design, is capable of establishing a cause-and-effect relationship within a group. In order for an experiment to be valid, three conditions must be met: There will be two groups: a Control Group that will not be subjected to any changes, and an Experimental Group that will be subjected to the altered variables.  A variable which can be manipulated by the researcher  Random distribution In the physical sciences, this type of experimental research method is frequently used. 7.2.3 Quasi-experimental research design A similarity between two things is indicated by the word \"Quasi.\" A quasi-experimental design is similar to an experimental design, but it is not the same thing as an experimental design. The only difference between the two is the assignment of a control group in each case. There is a manipulation of an independent variable in this study, but the participants of a group are not assigned at random. It is used in field settings when random assignment is either irrelevant or not required, such as when collecting data from participants. 7.3 CHARACTERISTICS OF EXPERIMENTAL RESEARCH Following are the characteristics of experimental research Operation: The researcher can exert control over the independent variable through treatment or intervention. Regulator: On the dependent variable, the effect of the control group and extraneous variables was examined. Randomization: There is an equal chance for every subject to be assigned to either the experimental or control groups. 116

7.4 IMPORTANCE OF EXPERIMENTAL RESEARCH  Researchers have a stronger grip on variables, allowing them to achieve the desired results.  The effectiveness of experimental research is not affected by the subject or the industry in which it is conducted. Any industry can use it for research purposes, and it is not limited to pharmaceuticals.  The outcomes are very specific.  After you've finished analysing the results, you can apply what you've learned to similar concepts or situations.  You have the ability to determine the cause and effect of a hypothesis. Researchers can dig deeper into this relationship in order to come up with more in-depth conclusions.  The use of experimental research as a starting point is ideal. The information you gather will serve as a foundation upon which to build additional ideas and conduct additional research. 7.5 ADVANTAGES OF EXPERIMENTAL RESEARCH 1. It allows researchers to exercise a high degree of control. The variables can be manipulated in experimental research so that researchers can create a setting that allows them to observe the phenomena they are interested in. They have the ability to eliminate or control other factors that could have an impact on the overall results, which means they can narrow their focus and concentrate solely on two or three variables. For example, in the pharmaceutical industry, scientists conduct studies in which they administer a new kind of drug to one group of subjects while administering a placebo to another group of subjects. They then provide the subjects with the same type of food and even house them in the same area in order to ensure that they are not exposed to any other factors that could interfere with the drugs' effectiveness. At the conclusion of the study, the researchers conduct an analysis of the data to determine how the new drug affects the subjects and to identify any side effects or negative outcomes. 2. It enables researchers to test a wide range of hypotheses. As previously stated, when conducting experimental research studies, researchers have almost complete control over the outcome of the study. That way, they can experiment with variables and create an environment where they can test their hypotheses with as many (or as few) variations as they want while maintaining the validity of their research design. If, as in the preceding example, the researchers decide to include a third group of subjects (in addition to the new drug group and the placebo group), this group would be given a well-known and widely available drug that has been in widespread use for many years. They will be able to 117

compare the performance of the new drug to that of the placebo drug as well as the widely used drug in this manner. 3. It has the potential to produce excellent results. Researchers can quickly understand the relationships between variables, subjects, and their environment because of the nature of experimental research. They can also quickly determine what is causing or causing what in whatever phenomenon they are studying because of the nature of experimental research. Studies conducted in an experimental setting can be easily replicated, which means that the researchers themselves or other scientists can repeat their studies to confirm the findings or to test for other variables. 4. It can be applied in a variety of fields. A common application of experimental research in the medical and pharmaceutical industries is to evaluate the effects of various treatments and drugs on patients and animals. It is also employed in a variety of other fields, including chemistry, biology, physics, engineering, electronics, agriculture, social science, and economics, among others. 7.6 DISADVANTAGES OF EXPERIMENTAL RESEARCH 1. It has the potential to create artificial situations. The function of drugs, gadgets, treatments, and other new discoveries is studied in many scenarios in which experimental researchers manipulate variables in an attempt to replicate real-world scenarios. Although this method is generally effective, there are instances when researchers over-manipulate their variables and end up creating an artificial environment that is vastly different from the real world. The researchers can also skew the study to fit whatever outcome they desire (either intentionally or unintentionally), which can have a negative impact on the research's findings. 2. It can consume a significant amount of time and money. Experimental research can be expensive and time-consuming, especially if the researchers must conduct a large number of studies to test every possible variable combination. Even if the studies are funded by the government, they would consume millions, if not billions, of dollars in taxpayer funds that could have been used for other community projects such as education and housing, as well as healthcare. Even if the studies are privately funded, they can be a significant financial burden on the companies that are involved, who in turn would pass on the costs to their clients. Customers must therefore spend a significant amount of money in order to benefit from these new treatments, gadgets, and other technological advancements. 3. It is susceptible to being harmed by errors. 118

It is true that experimental research is not always perfect, just as it is with any other type of research. There may be mistakes in the research design or methodology, as well as random mistakes that cannot be controlled or predicted, which can have a negative impact on the study's outcome and force the researchers to start over from the beginning. In addition, there may be human errors; for example, the researchers may allow their personal biases to influence the study's results. The researchers may be made aware of which subjects are in the control group when they are conducting a double-blind study (in which both the researchers and the subjects are unaware of which group they are in). This would invalidate the research because the validity of the research would be compromised. It is possible for the subjects to make mistakes as well. Subjects have been known to give answers that they believe the researchers want to hear rather than speaking truthfully about what is on their minds in some situations (particularly in social experiments). 4. It may not be feasible in certain circumstances. There are times when the variables simply cannot be manipulated, or when the researchers require an impossibly large sum of money in order to carry out the study successfully. There are also instances in which the study would impinge on the subjects' human rights and/or would raise ethical concerns about the subjects. Rather than insisting on using the experimental research method in these situations, it is preferable to use another type of research design (such as a review, meta-analysis, descriptive, or correlational research) instead of insisting on using the experimental research method. 7.7. MEANING OF DIAGNOSTIC RESEARCH In diagnostic design, the researcher is attempting to determine the underlying cause of a specific topic or phenomenon being studied or investigated. This method assists one in learning more about the factors that contribute to the occurrence of problematic situations. This research design is divided into three sections:  Inception of the issue  Diagnosis of the issue  Solution for the issue A diagnostic test is a procedure that is used to determine the existence of a condition or the source of the condition. It is employed in the diagnosis of disease. A diagnostic test that is performed as part of a medical examination may be used to determine the cause of symptoms or to diagnose a medical condition. The use of a diagnostic test to identify specific strengths and weaknesses can be beneficial when used for other purposes. Additionally, diagnostic tests may be used to determine the underlying cause of a particular behaviour or characteristic. In the case of a student having difficulty reading, diagnostic 119

testing may be performed in order to determine whether or not the student has dyslexia or a visual impairment. 7.8. CHARACTERISTICS OF DIAGNOSTIC RESEARCH The diagnostic process is probabilistic, multivariable and sequential a. A diagnosis starts with a patient presenting a complaint symptom and or a sign suggestive of a certain disease to be diagnosed b. the subsequent work up it's a multivariate process. It involves multiple diagnostic determinants that are applied in logical order: from age gender, medical history, and signs and symptoms, tomorrow complicated, invasive, and costly tests. c. Setting or ruling out a diagnosis it's a probabilistic action in which the probability of the presence or absence of the disease is central. The probability is continuously updated based on the subsequent diagnostic test results. d. The true diagnostic value of a test is determined by the extent to which it provides diagnostic information beyond earlier tests, that is, materially changes the probability estimation of disease presents based on the previous test results e. The goal of the diagnostic process is to eventually rule in or out the disease with enough confidence to take clinical decisions. This requires precise estimate of probability of the presence of target diseases. 7.9. DIAGNOSIS VS. PREDICTION Diagnosis: Disease has already occurred, and we are trying to detect its presence Prognosis: Disease has not occurred, and we want to know who is most likely to develop the disease Both are amenable to multivariable approaches and prediction models. They are often mixed up. Sometimes a diagnostic test itself can be used to predict future outcomes (e.g., PSA, Heart failure, Chest pain, Gastrointestinal, Stroke, Lung cancer etc.) 7.10 DIAGNOSTIC PROCESS A diagnostic test is a procedure that is used to determine the presence or cause of a medical condition. It is employed in the diagnosis of diseases and conditions. Diagnoses are made using diagnostic tests that are performed as part of a medical examination. Diagnostic tests are used to determine the cause of symptoms or to diagnose a disease. The use of a diagnostic test to identify specific strengths and weaknesses can be beneficial in other situations as well. 120

Additionally, diagnostic tests can be used to determine the underlying cause of a particular behaviour or characteristic. In the case of a student having difficulty reading, diagnostic testing may be performed in order to determine whether or not the student has dyslexia or a visual deficit. 7.11 SUMMARY  Experimentation design can be defined as \"the methods used to collect data in experimental studies,\" according to the classic definition.  There are three primary types of experimental design:  Pre-experimental research design  True experimental research design  Quasi-experimental research design  Following are the characteristics of experimental research  Operation  Regulator  Randomization  The researcher is attempting to determine the underlying cause of a specific topic or phenomenon being studied or investigated  This research design is divided into three sections:  Inception of the issue  Diagnosis of the issue  Solution for the issue  A diagnostic test is a procedure that is used to determine the presence or cause of a medical condition. 7.12 KEYWORDS  Experimental research: Experimental research is research that is carried out using a scientific approach and involves the use of two sets of variables.  Diagnostic research: A diagnostic test is a procedure that is used to determine the presence or cause of a medical condition.  Diagnosis: Diagnosis is the procedure through which the nature of a phenomenon, such as a disease, is determined. The term diagnosis also refers to the opinion reached through this process.  Prediction: A statement about what will be observed before the actual event, a foretelling of some future happening  Quasi-experimental research design: A quasi-experimental design is similar to an experimental design, but it is not the same thing as an experimental design 121

7.13 LEARNING ACTIVITY 1. A study of the effects of various levels of advertising effort and price reduction on the sale of specific branded grocery products by a retail grocery chain. What type of experimental design would you recommend? 7.14 UNIT END QUESTIONS A. Descriptive Questions Short Questions 1. Define Experimental research. 2. Define Diagnostic research. 3. Where is diagnostic research is much applicable? 4. What are the practical difficulties of experimental research? 5. Distinguish experimental and diagnostic research. Long Questions 1. Describe in detail about the pros and cons of Experimental Research 2. Explain briefly the methods of Experimental Research 3. List the characteristics of Experimental Research 4. Explain the diagnostic research process 5. Explain the characteristics of Diagnostic Research B. Multiple Choice Questions 1. ______ is research that is carried out using a scientific approach. a. Experimental Research b. Descriptive Research c. Diagnostic Research d. Casual Research 2. The researcher can exert control over the independent variable through treatment or intervention. a. Operation b. Regulator c. Randomization d. All the above 3. Experimental research can be applied in a. Only in selected sectors b. Only in Pharmaceutical sectors c. Variety of fields 122

d. None of these 4. ______ is a procedure that is used to determine the presence or cause of a medical condition. a. Diagnostic test b. Medical test c. Experimental test d. Research test 5. There is an equal chance for every subject to be assigned to either the experimental or control groups. a. Operation b. Probability c. Experimentation d. Randomization Answers 1-a, 2-a, 3-c, 4-a, 5-d 7.15 REFERENCES References book  R1, Business Research Methods – Alan Bryman& Emma Bell, Oxford University Press.  R2, Research Methodology - C.R. Kothari  R2, Statistics for Managers Using Microsoft Excel, Levine Stephan, Krehbiel Berenson Textbook references  T1, SPSSExplained,ISBN:9780415274104,Publisher:TataMcgrawHill  T2,Sancheti&Kapoor,BusinessMathematics,SultanChand,NewDelhi 123

UNIT 8: VARIABLES STRUCTURE 8.0 Learning Objective 8.1 Meaning of Variables 8.2 Types of Variables 8.2.1. Independent variables 8.2.2 Dependent variables 8.2.3 Intervening variables 8.2.4 Moderating variables 8.2.5 Control variables 8.2.6 Extraneous variables 8.2.7 Quantitative variables 8.2.8Qualitative variables 8.2.9 Confounding variables 8.2.10 Composite variables 8.3 Examples of Independent and Dependent variable 8.4 Independent and Dependent variable in experiments 8.5 Confounding variables 8.5.1 Understanding confounding variable 8.5.2 Effects of Confounding variables in research 8.5.3 How to reduce the impact of confounding variables 8.6 Summary 8.7 Keywords 8.8 Learning Activity 8.9 Unit End Questions 8.10 References 124

8.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  Explain about the variables in research.  Describe the types of variables  Explain the independent and dependent variable  Describe the Confounding Variables.  Identify how to approach the independent and dependent variable in real time. 8.1 MEANING OF VARIABLES “Variable is a property that takes on different values''. It is also a logical grouping of attributes. A variable is a measurable concept such as height, age, income etc. it takes quantitative values. It may vary from individuals to individuals or groups to groups. When there are two variables in a study such that the values of one variable change in response to the change in the values of the other variable, then the former is said to be depending on variable and latter is said to be independent variable. A variable may be discrete or continuous. When a variable assumes only certain specified values in an interval, it is called discrete variable. But a continuous variable is one which can assume any number of values in an interval. A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, \"The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable].\" Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write. 8.2 TYPES OF VARIABLES In statistics and research, variables are things that can be measured, manipulated, and controlled. Every study examines a variable, which can be a person, a place, a thing, or an idea, among other things. When comparing variables between groups or over time, the value of a variable can change. For example, if the variable in an experiment is a person's eye colour, the value of the variable can vary from person to person, ranging from brown to blue to green. 125

Below listed are the different types of variables:  Independent variable  Dependent variables  Intervening variables  Moderating variables  Control variables  Extraneous variables  Quantitative variables  Qualitative variables  Confounding variables  Composite variables Let us discuss this is detail. 8.2.1 Independent variables When you have an independent variable, it refers to a characteristic that cannot be changed by the other variables in your experiment. An example of an independent variable is the individual's age. The place where someone lives, the food they eat, and the amount of exercise they get will not change their age. Independent variables, on the other hand, have the ability to change other variables. In studies, researchers often try to find out whether an independent variable causes other variables to change and in what way. 8.2.2 Dependent variables A dependent variable is reliant on and can be influenced by the other components of the system. A grade on an exam is an example of a dependent variable because it is influenced by factors such as how much sleep you got and how much time you spent studying before the exam. Independent variables can have an impact on dependent variables, but dependent variables are unable to have an impact on independently variable. Using the example above, the amount of time you spent studying (dependent) can have an impact on your test grade (independent), but the amount of time you spent studying has no impact on your test grade. When examining relationships between study objects, researchers frequently seek to understand what causes the dependent variable to change and how it changes over time. 8.2.3 Intervening variables In research, an intervening variable, also known as a mediator variable, is a theoretical variable that the researcher uses to explain a cause or connection between other study variables, which are typically dependent and independent variables. Instead of being observations, they are associations. A researcher might hypothesis that access to high-quality healthcare is the intervening variable that links wealth and life span, for example, if wealth is the independent variable and a long-life span is the dependent variable. 126

8.2.4 Moderating variables In a moderating or moderator variable, the relationship between dependent and independent variables is altered by strengthening or weakening the effect of the intervening variable on the relationship. For example, age is a moderating variable in a study looking at the relationship between economic status (independent variable) and the frequency with which people get physical exams from a doctor (dependent variable). It's possible that this relationship is weaker in younger people and stronger in older people than in the general population. 8.2.5 Control variables Control or controlling variables are characteristics that remain constant throughout a study and do not change as a result of the study's findings. They have no effect on any of the other factors. In order to avoid bias in their experiments, researchers may choose to keep a control variable constant throughout the experiment. Control variables in a plant development experiment, for example, might include the amounts of fertilizer and water that each plant receives. It is important that these amounts remain constant so that they do not interfere with the growth of the plants. 8.2.6 Extraneous variables In experimental design, extraneous variables are variables that have an effect on the dependent variable but were not considered by the researcher at the time of experiment design. These unwanted variables have the potential to unintentionally alter the results of a study or the way in which a researcher interprets those results. Let's say you're conducting research to determine whether private tutoring or online courses are more effective at improving students' Spanish test scores. Examples of unintentional variables that could have an impact on the outcome include parental support, prior knowledge of a foreign language, and socioeconomic standing. 8.2.7 Quantitative variables In the context of quantitative variables, any data set that contains numbers or amounts is considered to be quantitative. Height, distance, and the number of items are all examples of quantitative data. In addition, quantitative variables can be subdivided into two types by researchers: Discrete: Any numerical variables that can be counted in a realistic manner, such as the coins in your wallet or the money in your savings account, are acceptable. Continuous: Time, for example, is one of those numerical variables that you could never finish counting. Discrete vs continuous variables 127

Type of variable What does the data Examples represent? Discrete Counts of individual items Number of students in a class variables (or or values. Number of different tree species in a forest integer variables) Continuous Measurements of Distance variables (or ratio continuous or non-finite Volume variables) values. Age 8.2.8 Qualitative Variables Qualitative variables, also known as categorical variables, are non-numerical values or groupings of values. Examples could include the colour of one's eyes or hair. Qualitative variables can be further divided into three types, according to researchers: Binary: Only two categories are represented by variables such as male or female, red or blue, and so on. Nominal: Variables that can be organised into more than two categories but do not have to be organised in a specific order. Consider the following examples of housing types: Single-family home, condominium, or tiny home are all options. Ordinal: If you have more than two categories of variables that follow a specific order, you can group them together. Take, for example, customer satisfaction levels: Unsatisfied, neutral, and satisfied are all possible responses. Type of variable What does the data Examples represent? Binary variables (or Yes/no outcomes. Heads/tails in a coin flip dichotomous variables) Win/lose in a football game Nominal variables Groups with no rank or Species names order between them. Colors Brands 128

Ordinal variables Groups that are ranked in a Finishing place in a race specific order Rating scale responses in a survey 8.2.9 Confounding variables A confounding variable is a variable that you did not account for that has the potential to obscure the effects of another variable. Confounding variables can invalidate your experiment results by causing them to be biased or by implying that a relationship between variables exists when there isn't actually one between them. For example, if you are investigating the relationship between exercise level (independent variable) and body mass index (dependent variable), but do not take into account the effect of age on these variables, age becomes a confounding variable that alters your findings. 8.2.10 Composite variables A composite variable is a combination of two or more variables that results in a more complex variable. An example of a composite variable is when you use a number of other variables, such as body weight, blood pressure, and chronic pain, to determine overall health in your experiment. 8.3 EXAMPLES OF INDEPENDENT AND DEPENDENT VARIABLE Independent Variable The condition that you change in an experiment is referred to as the independent variable. It is the variable that you have control over. This variable is referred to as \"independent\" because its value is not dependent on and is not affected by the state of any other variable in the experiment. This variable is sometimes referred to as the \"controlled variable\" because it is the one that is being changed in the experiment. It should not be confused with a \"control variable,\" which is a variable that is purposefully kept constant so that it cannot influence the outcome of the experiment if it is used. Dependent Variable During an experiment, the dependent variable is the condition that you are attempting to measure. It can be thought of as being dependent on the independent variable because you are evaluating how it responds to a change in the independent variable. The dependent variable is also referred to as the \"responding variable\" in some cases. Independent and Dependent Variable Examples 129

During a study to determine whether or not the amount of time a student sleeps has an effect on test scores, the independent variable is the amount of time spent sleeping, and the dependent variable is the test score. The goal is to compare different brands of paper towels in order to determine which one holds the most liquid. The brand of paper towel used in your experiment would be considered the independent variable. The amount of liquid that is absorbed by the paper towel would be the dependent variable in this case. In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable, and whether or not the light is observed (the response) is the dependent variable. In this experiment, the wavelength of light is the independent variable, and the response is the dependent variable. If you want to know if caffeine has an effect on your appetite, the presence or absence of a specific amount of caffeine would be the independent variable in your experiment. The dependent variable would be how hungry you are at the time. Designing an experiment to determine whether or not a chemical is required for rat nutrition is something you are interested in. This is an independent variable because it does not depend on the presence or absence of the chemical. As the dependent variable, we are interested in the rat's health (whether or not it lives and can reproduce). If you discover that the substance is required for proper nutrition, you may want to conduct a follow-up experiment to determine how much of the chemical is required. The amount of chemical used would be the independent variable in this case, and the health of the rats would be the dependent variable. How to Tell the Difference Between an Independent and a Dependent Variable Identifying which variable is the independent and which variable is the dependent variable can be difficult, but keep in mind that the dependent variable is the one that is affected by a change in the independent variable. Using the variables in a sentence that shows cause and effect, you can see that the independent variable is the one that has a negative impact on the dependent variable. Putting the variables in the wrong order will result in the sentence being incomprehensible. In this case, the independent variable has an impact on the dependent variable. For example,How long you sleep (independent variable) affects your test score (dependent variable). This makes sense, but there is a catch: Example: Your test score affects how long you sleep. Nothing about this makes sense (unless you are having trouble sleeping because you are worried about failing a test, but that would be a completely different experiment). How to Plot Variables on a Graph 130

When graphing the independent and dependent variables, there is a standard method to follow. The independent variable is represented by the x-axis, and the dependent variable is represented by the y-axis. To make it easier to remember how to graph variables, you can use the acronym DRY MIX: Dry mixing applications D = dependent variable R = responding variable Y = graph on the vertical or y-axis M = manipulated variable I = independent variable X = graph on the horizontal or x-axis Independent vs dependent vs control variables Type of variable Definition Examples (salt tolerance experiment) Independent variables Variables you manipulate in The amount of salt added to each (or treatment variables) order to affect the outcome of plant’s water. an experiment. Dependent variables (or Variables that represent the Any measurement of plant health response variables) outcome of the experiment and growth: in this case, plant height and wilting. Control variables Variables that are held constant The temperature and light in the throughout the experiment. room the plants are kept in, and the volume of water given to each plant. 8.4 INDEPENDENT AND DEPENDENT VARIABLE IN EXPERIMENTS In research, variables are any characteristics that can have different values depending on the situation, such as height, age, species, or exam score. In scientific research, it is common to want to look into the relationship between one variable and another. As an example, you might want to see if students who put in more effort in their studies perform better on exams. The independent and dependent variables in a study of a cause-and-effect relationship are the variables that are being investigated. 131

 The independent variable is the cause. Its value is independent of other variables in your study.  The dependent variable is the effect. Its value depends on changes in the independent variable. Examples of independent and dependent variables Research Question Independent variable(s) Dependent variable(s) Do tomatoes grow fastest under The type of light the The rate of growth of the fluorescent, incandescent, or tomato plant is grown tomato plant natural light? under What is the effect of diet and The type of soda you Your blood sugar levels regular soda on blood sugar drink (diet or regular) levels? How does phone use before The amount of phone use Number of hours of sleep bedtime affect sleep? before bed Quality of sleep How well do different plant The amount of salt added Plantgrowth species tolerate salt water? to the plants’ wate Plant wilting Plant survival rate Independent and dependent variables in experiments In experimental research, the independent variable is manipulated or changed by the experimenter in order to determine the effect of this manipulation or change on the dependent variable (the outcome). As an illustration, consider the following scenario: When it comes to patients with hypertension, you're looking into how a new medication will affect their blood pressure. You divide your patients into two groups in order to determine whether or not the medication is beneficial. An experimental medication is administered to one group, while a sugar pill placebo is administered to the other group Treatment, or the treatment that differs between groups, is your independent variable. The type of pill that a patient receives is your independent variable. 132

It is the outcome that you are measuring that is the dependent variable: the blood pressure of the subjects. Most of the time, the independent variable is applied at various levels to see how the outcome varies. When determining whether or not the independent variable has an effect, you can apply only two levels (for example, the new medication and the placebo). Multiple levels (for example, three different doses of the new medication) can be used to investigate the relationship between the dependent and independent variables. Figure 8.1 Independent and dependent variable Variables in other types of research Researchers are frequently unable to directly manipulate or change the independent variable that they are interested in when they are not in an experimental setting. Instead, they must look for examples of the independent variable that already exist and investigate how changes in this variable affect the dependent variable, as described above. Research example You're curious about the impact that a higher minimum wage has on employment rates in your area. You will not be able to control the minimum wage on your own. Instead, you look at a state that increased its minimum wage last year and compare it to a neighbouring state that did not raise its minimum wage.  Your independent variable is the minimum wage. 133

 Your dependent variable is the employment rate. In order to determine whether a change in the minimum wage had an impact on employment rates, you can compare the differences in outcomes between two states (while taking other factors into consideration). In non-experimental research, it’s more difficult to establish a definite cause-and-effect relationship, because other variables that you haven’t measured might be influencing the changes. These are known as confounding variables. In types of research where the exact relationship between variables is less certain, you might use different terms for independent and dependent variables. Sometimes, the variable you think is the cause might not be fully independent – it might be influenced by other variables. In this case, one of these terms is more appropriate:  Explanatory variables (they explain an event or outcome)  Predictor variables (they can be used to predict the value of a dependent variable)  Right-hand-side variables (they appear on the right-hand side of a regression equation). Other names for dependent variables Dependent variables are also known by these terms:  Response variables (they respond to a change in another variable)  Outcome variables (they represent the outcome you want to measure)  Left-hand-side variables (they appear on the left-hand side of a regression equation) 8.5 CONFOUNDING VARIABLES If you are conducting research to determine a potential cause and effect relationship, you may encounter a confounding variable, which is an unmeasured third variable that has an impact on both the supposed cause and the supposed effect. It is critical to take into account potential confounding variables and account for them in your research design in order to ensure that your findings are reliable. In research, confounding variables (also known as confounders or confounding factors) are a type of extraneous variable that is related to both the independent and dependent variables in a research study. A variable must meet two criteria in order to be considered a confounder: It has to be correlated with the independent variable in order to be considered. It is possible that this is a causal relationship, but it is not required to be so. It must be related to the dependent variable in a causal manner. 134

Confounding variable example You keep track of things like sunburns and ice cream consumption. You discover that increased ice cream consumption is associated with a greater likelihood of getting sunburned. Does this imply that ice cream consumption results in sunburns? When it comes to sunburns, the confounding variable is temperature: high temperatures cause people to consume more ice cream while also spending more time outside in the sun, which results in more sunburns. Figure 8.2 Confounding variables 8.5.1 Understanding confounding variable Generally speaking, a confounding variable (also known as a confounder) is a factor other than the one under investigation that is associated with both the disease (dependent variable) and the factor under investigation (independent variable). A confounding variable may cause the effects of another variable on the disease under investigation to be distorted or obscured. For example, a hypothesis that coffee drinkers have more heart disease than non-coffee drinkers may be influenced by another factor as shown below. Due to the fact that coffee drinkers are more likely than non-coffee drinkers to smoke cigarettes, smoking is a confounding variable in studies examining the relationship between coffee drinking and heart disease. It is possible that the increase in heart disease is due to smoking rather than coffee. More recent studies have demonstrated that coffee consumption has a significant beneficial effect on heart health as well as the prevention of dementia. 135

Figure 8.3 Confounding variable example 8.5.2 Effects of Confounding variables in research Confounding variables must be taken into consideration in order to ensure the internal validity of your research. If you do not follow these instructions, your results may not accurately reflect the actual relationship between the variables that you are interested in. Consider the possibility that you will discover a cause-and-effect relationship that does not actually exist, because the effect you measure is caused by the confounding variable (and not by your independent variable). ExampleYou discover that states with higher minimum wages have a higher employment rate than other states. So, does this imply that higher minimum wages result in higher labour force participation rates? This is not always the case. Perhaps states with stronger job markets are more likely to raise their minimum wages than states with weaker job markets, rather than the reverse. It is essential that you consider prior employment trends when analysing the impact of the minimum wage on employment; otherwise, you may discover a causal relationship where none exists. Even if you correctly identify a cause-and-effect relationship, confounding variables can lead to over- or underestimation of the impact of your independent variable on your dependent variable, depending on your research questions. ExampleYou discover that babies born to mothers who smoked during their pregnancies weigh significantly less than babies born to mothers who did not smoke during their pregnancies. It is possible to overestimate the relationship between smoking and low birth weight if you do not take into consideration the fact that smokers are more likely to engage in other unhealthy behaviours, such as drinking or eating less healthy foods. 8.5.3 How to reduce the impact of confounding variables? The accounting for confounding variables can be accomplished in a variety of ways. When studying any type of subject matter, including humans, animals, plants, chemicals, and so on, 136

you can employ the following methods. Each method has its own set of benefits and drawbacks to consider. Restriction In this method, you limit the number of subjects in your treatment group by including only those who have the same values of potential confounding factors. Because these values do not differ among the subjects of your study, they are unable to correlate with your independent variable and, as a result, are unable to confound the cause- and-effect relationship you are investigating. Restriction example You'd like to investigate whether or not a low-carb diet can help you lose weight. As a result, you decide to limit your subject pool to 45-year-old women with bachelor's degrees who exercise at moderate levels of intensity between 100–150 minutes per week. You know that age, gender, level of education, and exercise intensity are all factors that may be associated with weight loss, as well as the diet your subjects choose to follow. Matching In this method, you choose a comparison group that is similar to the treatment group. Everyone in the comparison group should have a counterpart in the treatment group who has the same values of potential confounders but different values of independent variables as the comparison group members. Thus, the possibility that differences in confounding variables are responsible for the variation in outcomes between treatment and comparison groups can be eliminated. Assuming that you have taken into account any potential confounders, you can conclude that any variation in the independent variable must be due to a variation in the dependent variable. Matching example In your study on low-carb diets and weight loss, you pair up your subjects based on their age, gender, level of education, and amount of exercise they do each week.. This enables you to cover a broader range of topics because your treatment group is made up of men and women of various ages and educational backgrounds. Each subject on a low-carb diet is matched with another subject who has the same characteristics but is not on the diet in order to compare results. To compare the weight loss between two subjects, for every 40-year-old highly educated man who follows a low-carb 137

diet, you find another 40-year-old highly educated man who does not follow a low-carb diet. Do the same for all of the other subjects in your treatment sample as you did for the first. Statistical control Alternatively, if you have already collected the data, you can include the potential confounders as control variables in your regression models; in this way, you will be able to account for the impact of the confounding variable on your results. Any effect that the potential confounding variable has on the dependent variable will be reflected in the results of the regression, allowing you to distinguish between the impact of the independent variable and the effect of the potential confounding variable. Statistical control example In your regression model, you include exercise levels, education, age, and gender as control variables, as well as the type of diet each subject follows as the independent variable. After collecting data on weight loss and low-carb diets from a variety of participants, you include the data in your regression model. The impact of the diet you choose can then be distinguished from the influence of the other four variables on weight loss in your regression model as a result. Randomization Randomizing the values of your independent variable is another method of reducing the impact of confounding variables on your results. For example, if some of your participants are assigned to a treatment group while others are assigned to a control group, you can assign participants to each group according to a randomization process. Randomization ensures that, given a sufficiently large sample size, all potential confounding variables—including those that cannot be observed directly in your study—will have the same average value across all groups participating in the study. They cannot confound your study because they do not differ according to group assignment, and they cannot correlate with your independent variable because they do not differ according to group assignment. 138

Because this method allows you to account for all potential confounding variables, which is nearly impossible to do otherwise, it is frequently regarded as the most effective method of reducing the impact of confounding variables in research studies. Randomization example You recruit a large number of subjects to take part in your weight-loss research study. Using a random selection process, you decide which half of them will follow a low-carb diet and which half will continue with their normal eating habits. In your low-carb diet group and in your control group, randomization ensures that both groups have the same average age, education, and level of physical activity, but also the same average values on other characteristics that you have not measured, such as body mass index (BMI). 8.6 SUMMARY  A variable is a measurable concept such as height, age, income etc. it takes quantitative values. It may vary from individuals to individuals or groups to groups.  When there are two variables in a study such that the values of one variable change in response to the change in the values of the other variable, then the former is said to be depending on variable and latter is said to be independent variable.  Below listed are the different types of variables: Independent variable, Dependent variables, Intervening variables, Moderating variables, Control variables, Extraneous variables, Quantitative variables, Qualitative variables, Confounding variables, Composite variables  The independent variable is the cause. Its value is independent of other variables in your study.  The dependent variable is the effect. Its value depends on changes in the independent variable.  In research, confounding variables (also known as confounders or confounding factors) are a type of extraneous variable that is related to both the independent and dependent variables in a research study 139

8.7 KEYWORDS  Independent variable: When you have an independent variable, it refers to a characteristic that cannot be changed by the other variables in your experiment  Dependent variable: A dependent variable is reliant on and can be influenced by the other components of the system  Intervening variable: The researcher uses to explain a cause or connection between other study variables, which are typically dependent and independent variables  Moderating variable: The relationship between dependent and independent variables is altered by strengthening or weakening the effect of the intervening variable on the relationship.  Control variable: Control or controlling variables are characteristics that remain constant throughout a study and do not change as a result of the study's findings  Extraneous variable: Extraneous variables are variables that have an effect on the dependent variable but were not considered by the researcher at the time of experiment design.  Composite variable: A composite variable is a combination of two or more variables that results in a more complex variable  Confounding variable: A confounding variable is a variable that you did not account for that has the potential to obscure the effects of another variable. 8.8LEARNING ACTIVITY 1. In your company’s management development program, there was a heated discussion between some people who claimed, “Theory is impractical and thus no good,” and others who claimed, “Good theory is the most practical approach to problems.” What position would you take and why? 8.9 UNIT END QUESTIONS A. Descriptive Questions Short Questions 1. Define Variable 2. What do you mean by extraneous variable? 3. Define Intervening variable. 4. Where do you apply control variables? 5. How do you identify dependent and independent variable? Long Questions 1. Explain the independent and dependent variable 140

2. Explain about the variables in research. 3. Identify how to approach the independent and dependent variable in real time. 4. Describe the types of variables in detail 5. Describe the Confounding Variables. B. Multiple Choice Questions 1. Any numerical variables that can be counted in a realistic manner, such as the coins in your wallet or the money in your savings account, are acceptable. a. Continuous variable b. Discrete variable c. Binary variable d. Nominal variable 2. Only two categories are represented by variables such as male or female, red or blue, and so on. a. Continuous variable b. Discrete variable c. Binary variable d. Nominal variable 3. Groups that are ranked in a specific order denotes a. Continuous variable b. Discrete variable c. Binary variable d. Ordinal variable 4. The condition that you change in an experiment is referred to as the ______. a. Dependent variable b. Independent variable c. Discrete variable d. Continuous variable 5. They can be used to predict the value of a dependent variable. a. Predictor variable b. Independent variable c. Discrete variable d. Continuous variable Answers 141

1-b, 2-c, 3-d, 4-b, 5-a 8.10 REFERENCES References book  R1, Business Research Methods – Alan Bryman& Emma Bell, Oxford University Press.  R2, Research Methodology - C.R. Kothari  R2, Statistics for Managers Using Microsoft Excel, Levine Stephan, Krehbiel Berenson Textbook references  T1,SPSSExplained,ISBN:9780415274104,Publisher:TataMcgrawHillSancheti&K apoor,BusinessMathematics,SultanChand,NewDelhi 142

UNIT 9: SCALING STRUCTURE 9.0 Learning Objective 9.1 Meaning of Measurement in Research 9.2 Sources of error in measurement 9.3 Tests of Sound Measurement 9.3.1 Test of Validity 9.3.1.1 Content validity 9.3.1.2 Criterion-related validity 9.3.1.3 Construct validity 9.3.2 Test of Reliability 9.3.2.1 Stability aspect 9.3.2.2 Equivalence aspect 9.3.3 Test of Practicality 9.4 Technique of Developing Measurement Tools 9.4.1 Concept development 9.4.2 Specification of concept dimensions 9.4.3 Selection of indicators 9.4.4 Formation of index. 9.5 Selecting a Measurement Scale 9.5.1 Research objectives. 9.5.2 Response types. 9.5.3 Data properties. 9.5.4 Number of dimensions. 9.5.5 Balanced or unbalanced. 9.5.6 Forced or unforced choices. 9.5.7 Number of scale points. 9.5.8 Rater errors. 9.6 Summary 143

9.7 Keywords 9.8 Learning Activity 9.9 Unit End Questions 9.10 References 9.0 LEARNING OBJECTIVES After studying this unit, you will be able to:  Describe the Measurement in Research  List out the sources of error in Measurement.  Explain the selection of measurement scale  Describe the techniques of developing a measurement scale  Explain the tests for sound measurement. 9.1 MEANING OF MEASUREMENT IN RESEARCH A physical object is said to be measured when we use a yardstick to determine the weight, height, or some other characteristic of the object in our everyday lives. We use measurements when deciding how much we enjoy a song, a painting, or the personalities of our friends, among other things. As a result, we take measurements of both physical objects and abstract concepts. Measuring qualitative or abstract phenomena is a difficult and time-consuming task, which is made even more difficult when dealing with qualitative or abstract phenomena. Objects or observations are measured in the process of assigning numerical values to them, with the level of measurement being a function of the rules that govern how the numbers are assigned. It is relatively simple to assign numbers to certain properties of some objects, but it is more difficult to do so with respect to other properties. For example, measuring things like social conformity, intelligence, or marital adjustment is much more difficult and requires much more attention than measuring things like physical weight, biological age, or a person's financial assets are. Thus, while certain characteristics such as weight and height can be measured directly using some standard unit of measurement, others such as motivation to succeed and the ability to withstand stress and the like are more difficult to assess. When measuring the length of a pipe with a yard stick, we can expect high accuracy; however, if the concept is abstract and the measurement tools are not standardised, we can expect lower accuracy in the results of the measurement. Using a correspondence rule, measurements are made by mapping aspects of a domain onto other aspects of a range in a systematic manner. We devise some form of scale in the range (in terms of set theory, range may refer to some set) and then transform or map the properties 144

of objects from the domain (in terms of set theory, domain may refer to some other set) onto this scale in order to perform measurements. 9.2 SOURCES OF ERROR IN MEASUREMENT In an ideal research study, the measurements should be precise and unambiguous at all times. This objective, on the other hand, is frequently not achieved in its entirety. As a result, the researcher must be aware of the sources of error in measurement procedures. The following are some examples of possible sources of measurement error. (a) Respondent: It is possible that the respondent will be reluctant to express strong negative feelings at times, or that he will have very little knowledge but will not admit his lack of knowledge. All of this reluctance is likely to result in a series of 'guesses' being interviewed. Transient factors such as fatigue, boredom, anxiety, and other emotions may impair the respondent's ability to provide accurate and complete responses. (b) Situation: Situational factors can also get in the way of accurate measurement of a condition. Any condition that makes it difficult to conduct an interview can have a negative impact on the rapport between the interviewer and the respondent. For example, if another person is present, he or she can distort responses simply by participating or simply by being present. If the respondent believes that his or her anonymity is in jeopardy, he or she may be reluctant to express certain emotions. (c) Measurer: By rephrasing or rearranging questions, the interviewer can distort the responses received. His demeanour, mannerisms, and appearance may either encourage or discourage certain responses from respondents. The findings may be distorted if the mechanical processing is not done with care. It is also possible for errors to creep in as a result of incorrect coding, faulty tabulation, and/or statistical calculations, which are particularly prevalent during the data-analysis stage (d) Instrument: It is possible that an error will occur as a result of a faulty measuring instrument. Poor printing, insufficient space for responses, response choice omissions, and other factors that cause the measuring instrument to be defective and result in measurement errors are some of the factors that cause the measuring instrument to be defective and result in measurement errors. Inadequate sampling of the universe of items of concern is a second type of instrument deficiency to be aware of. 145

The researcher must understand that accurate measurement is contingent on successfully addressing all of the issues listed above. He must make every effort to eliminate, neutralise, or otherwise deal with all possible sources of error in order to prevent the final results from becoming contaminated. 9.3 TESTS OF SOUND MEASUREMENT Validity, reliability, and practicality are all requirements for sound measurement. In fact, when evaluating a measurement tool, these are the three most important factors to consider. The extent to which a test measures what we actually want to measure is referred to as its validness. The accuracy and precision of a measurement procedure are referred to as reliability in this context. Economy, convenience, and interpretability are all important considerations in the realm of practicality. We will briefly go over the pertinent details of these sound measurement tests in this section. 9.3.1Test of Validity When it comes to measuring validity, the most important criterion is the degree to which an instrument accurately measures what it is intended to measure. Validity and utility are two terms that can be used to describe the same thing. In other words, the extent to which differences discovered with a measuring instrument reflect genuine differences among those being tested is referred to as validity. However, the question arises as to how one can determine validity in the absence of direct confirmation knowledge. If we find other relevant evidence that confirms the answers, we have discovered with our measuring tool, then we may have found the answer. What is relevant evidence and what is not depends on the nature of the research problem and the judgement of the researcher, among other things. However, there are three types of validity that can be considered in this connection.: (i) Content validity. (ii) Criterion-related validity and (iii) Construct validity. 9.3.1.1 Content validity The extent to which a measuring instrument provides adequate coverage of the topic under investigation is referred to as content validity. The content validity of an instrument is good if it contains a sample of the universe that is representative of the universe. It makes its decision primarily on the basis of judgement and intuition. A panel of individuals who will judge how well the measuring instrument meets the standards can also be used to determine it; however, no numerical method can be used to express it. 9.3.1.2 Criterion-related validity It is our ability to predict a specific outcome or estimate the presence of some current condition that is measured by criterion-related validity. This type of validity reflects the 146

success of measures that have been used for some type of empirical estimation. The following characteristics must be present in the criterion in question:  Relevance (A criterion is relevant if it is defined in terms, we judge to be the proper measure.)  Freedom from bias: (Freedom from bias is attained when the criterion gives each subject an equal opportunity to score well.)  Reliability: (A reliable criterion is stable or reproducible.)  Availability: (The information specified by the criterion must be available.) In reality, criterion-related validity is a broad term that refers to both Predictive validity and Concurrent validity, which are two types of validity. It is important to distinguish between the usefulness of a test in forecasting one type of future performance and the usefulness of a test in closely correlating with other measures with established validity. The coefficient of correlation between test scores and some measure of future performance, or between test scores and scores on another measure of known validity, is used to express criterion-related validity. 9.3.1.3 Construct validity Construct validity is the most difficult and abstract concept to grasp. An indicator of construct validity is one that confirms predicted relationships with other theoretical propositions to the extent that the measure demonstrates construct validity. The degree to which the results of a test can be explained by the explanatory constructs of a sound theory is referred to as its construct validity. For the purpose of determining construct validity, we associate a set of other propositions with the results obtained from the application of our measurement instrument. The existence of construct validity can be established if measurements taken on our devised scale correlate in the manner predicted by these other propositions. If the above-mentioned criteria and tests are met, we can conclude that our measuring instrument is valid and will result in accurate measurements; otherwise, we will need to seek additional information and/or resort to exercising judgement. 9.3.2 Test of Reliability One of the most important tests of sound measurement is the test for dependability. A measuring instrument is considered reliable if the results it produces are consistent. Validity is enhanced by the use of a reliable measuring instrument, but a valid instrument does not have to be reliable in order to be valid. To illustrate this point, while a scale that consistently overweighes objects by five kilogrammes is a reliable scale, it does not provide a valid measure of weight. However, the converse is not true, i.e., a valid instrument is not always trustworthy. Two aspects of reliability viz., stability and equivalence deserve special mention 147

a) The stability aspect b) The equivalence aspect 9.3.2.1 Stability aspect The stability aspect is concerned with ensuring consistent results when repeated measurements are taken by the same person and with the same device. Stability is typically determined by comparing results from multiple measurements taken at different intervals. 9.3.2.2 Equivalence aspect The equivalence aspect takes into account the amount of error that may be introduced by different investigators or different samples of the items under investigation. A good way to determine whether two investigators' measurements are equivalent is to compare their observations of the same event. The following are two approaches that can be used to increase reliability: (i) To achieve this, we must standardise the conditions under which the measurement is performed, which means that external sources of variation such as boredom, fatigue, and so on must be reduced to the greatest extent possible. This will help to improve the stability of the system. (ii) By using carefully designed measurement directions that are consistent from group to group, by employing trained and motivated individuals to conduct the research, and by expanding the range of items tested. This will help to improve the equivalence aspect. 9.3.3 Test of Practicality The economics, convenience, and interpretability of a measuring instrument are all factors that can be considered when evaluating its practicality. From the standpoint of operation, the measuring instrument should be practical, that is, it should be cost-effective, convenient, and easy to understand and interpret According to economic considerations, a trade-off between the ideal research project and the research project that the budget can support is required to be made. The length of a measuring instrument is a critical area in which economic pressures are felt quickly and significantly. Although, as previously stated, more items provide greater reliability, we must limit the number of items we use for our study in order to keep the interview or observation time to a minimum in order to achieve our objectives. Similar to this, the data collection methods that will be used are sometimes influenced by economic factors as well. Convenience test is suggested that the measuring instrument should be simple to use and administer using the convenience test. In order to achieve this, it is necessary to pay close attention to the proper layout of the measuring instrument. Consider the following example: a questionnaire with clear instructions (illustrated by examples) is unquestionably more effective and easier to complete than one that does not have these characteristics: 148

Consideration of interpretability is particularly important when the results of a test are to be interpreted by individuals other than the test's designers. In order for the measuring instrument to be interpretable, it must be accompanied by other instruments. (a) detailed instructions for administering the test. (b) scoring keys. (c) evidence about the reliability and (d) guides for using the test and for interpreting results. 9.4 TECHNIQUE OF DEVELOPING MEASUREMENT TOOLS It takes four stages to develop measurement tools, and each stage is comprised of the following components: (a) Concept development. (b) Specification of concept dimensions. (c) Selection of indicators; and (d) Formation of index. 9.4.1 Concept development The first and most important step is concept development, which means that the researcher must come to an understanding of the major concepts that are relevant to his or her study in order to proceed. This stage of concept development is more visible in theoretical studies than in more pragmatic research, where the fundamental concepts are more often already established by the time the research begins. 9.4.2 Specification of concept dimensions The researcher is required to specify the dimensions of the concepts that he developed in the first stage in the second step. In order to accomplish this task, one can either deduce, i.e., use a more or less intuitive approach, or use empirical correlation of the individual dimensions with the total concept and/or the other concepts. When considering the image of a particular company, one may consider a variety of factors such as product reputation, customer treatment, corporate leadership, concern for individuals, a sense of social responsibility, and so on. 9.4.3 Selection of indicators The researcher must then develop indicators for measuring each concept element after the dimensions of the concept have been specified. A specific question or scale or other device by which a respondent's knowledge, opinion, expectation, or other characteristics are measured is referred to as an indicator. Due to the fact that there is rarely a perfect measure of a concept, the researcher should consider a number of different alternatives for the purpose. 149

The use of more than one indicator improves the stability of the scores while also increasing the validity of the results. 9.4.4 Formation of index. The final step is the combination of the various indicators into an index, which is known as the creation of an index. When we have several dimensions of a concept or different measurements of a dimension, it is possible that we will need to combine them into a single index to make things easier. One straightforward method of obtaining an overall index is to assign scale values to the responses and then add the sums of the scores assigned to each scale value. It is believed that an overall index would be a more effective measurement tool than a single indicator because an individual indicator \"has only a probability relation to what we really want to know,\" as stated in the article. In this manner, we must be able to create an overall index for the various concepts related to the research project. 9.5 SELECTING A MEASUREMENT SCALE When selecting and constructing a measurement scale, it is necessary to take into account a number of factors that have an impact on the reliability, validity, and practicality of the scale: 1. Research objectives. 2. Response types. 3. Data properties. 4. Number of dimensions. 5. Balanced or unbalanced. 6. Forced or unforced choices. 7. Number of scale points. 8. Rater errors. 9.5.1 Research objectives. The objectives of researchers are far too numerous to list them all (including, but not limited to, studies of attitude, attitude change, persuasion, awareness, purchase intention, cognition and action, actual and repeat purchase). Researchers, on the other hand, are confronted with two general types of scaling objectives: • To assess the characteristics of the participants who take part in the study. • To use the participants as judges of the objects or indicators that are presented to them during the study. Consider the following scenario: you are conducting a survey of customers to determine their attitudes toward a change in the company's identity (a company logo and peripherals). With regard to the first study objective, your scale would be used to determine whether customers were more or less favourable in their orientation. You could use the responses of each individual to create an indicator of their overall orientation. The emphasis in this first study is 150