MAE612_RESEARCH METHODOLOGY(Draft 1)-converted

• Fink, A., Conducting Research Literature Reviews: From the Internet to Paper. 2009, Sage Publications • Coley, S.M. and Scheinberg, C. A., \"Proposal Writing\", 1990, Sage Publications. • The Craft of Research, Third Edition Book by Gregory G. Colomb, Joseph M. Williams, and Wayne C. Booth • Berg, Bruce L., 2009, Qualitative Research Methods for the Social Sciences. Seventh Edition. Boston MA: Pearson Education Inc. • Creswell, J. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, California: Sage Publications.

UNIT 4: RESEARCH DESIGN STRUCTURE 1. Learning Objectives 2. Introduction 3. Summary Characteristics Of Descriptive R 4. Descriptive Research 5. Types Of Research Design 6. Qualitative Research 7. Qualitative Research: Data Collection And Analysis 8. Keywords 9. Learning Activity 10. Unit End Questions 11. References LEARNING OBJECTIVES While studying this chapter we will learn about the following points: • The purpose of research and its different techniques to write a research. • The various important aspects that are a must for research. • Writing of research as an important factor and its guidelines. • Hypothesis –an important factor. • The different ways in which data can be collected and presented. INTRODUCTION Research design is the framework of research methods and techniques chosen by a researcher. The design allows researchers to hone in on research methods that are suitable for the subject matter and set up their studies up for success.The design of a research topic explains the type of research (experimental, survey, correlational, semi-experimental, review) and also its sub-type (experimental design, research problem, descriptive case-study). There are three main types of research design: Data collection, measurement, and analysis.The type of research problem an organization is facing will determine the research design and not vice- versa. The design phase of a study determines which tools to use and how they are used.An impactful research design usually creates a minimum bias in data and increases trust in the

accuracy of collected data. A design that produces the least margin of error in experimental research is generally considered the desired outcome. DESCRIPTIVE RESEARCH Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why. This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in. What Are The Types of Descriptive Research? Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below: • Descriptive-survey Descriptive-survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects. For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument, and each item on the survey related to qualifications is subjected to a Yes/No answer. This way, the researcher can describe the qualifications possessed by the employed demographics of this community. • Descriptive-normative survey

This is an extension of the descriptive-survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm. For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role. If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory. • Descriptive-status This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance. A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa. • Descriptive-analysis Descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office. A questionnaire is devised to analyze the job role of employees with similar salaries and work in similar positions. • Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly. • Descriptive-comparative In descriptive-comparative research, the researcher considers 2 variables which are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests. A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method. • Correlative Survey Correlative used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables, say X and Y are directly proportional, inversely proportional or are not related to each other. Examples of Descriptive Research There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article. Comparing Student Performance: An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field. Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify student's weak points and work on ways to assist them. Scientific Classification

During major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified. For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, in vertebrae, etc. All these classifications are made a result of descriptive research which describes what they are. Human Behaviour When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use if to conclude. A company willing to sell to its target market needs to first study the behaviour of the market. This may be done by observing how its target reacts to a competitor's product, then use it to determine their behaviour. CHARACTERISTICS OF DESCRIPTIVE RESEARCH The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are: Quantitative-ness Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences. Qualitative-ness It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratoryor experimental. Uncontrolled variables In descriptive research, researchers cannot control the variables like they do in experimental research.

The basis for further research The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used. This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is. Use Descriptive Research Design Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience. Define subject characteristics: It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects. For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community. Measure Data Trends It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision. Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions. Comparison Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do? Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales. Validate existing conditions When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions. Conducted Overtime Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be. What are the Disadvantages of Descriptive Research? Response and Non-response Bias Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched. The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results. A case-study or sample taken from a large population is not representative of the whole population. Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

TYPES OF RESEARCH DESIGN INTO FIVE CATEGORIES 1. Descriptive research design: In a descriptive design, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method which is created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 2. Experimental research design: Experimental research design establishes a relationship between the cause and effect of a situation. It is a causal design where one observes the

impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is a highly practical research design method as it contributes to solving a problem at hand. The independent variables are manipulated to monitor the change it has on the dependent variable. It is often used in social sciences to observe human behaviour by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to gain a better understanding of social psychology. 3. Correlational research design: Correlational research is a non-experimental research design technique that helps researchers establish a relationship between two closely connected variables. This type of research requires two different groups. There is no assumption while evaluating a relationship between two different variables, and statistical analysis techniques calculate the relationship between them. A correlation coefficient determines the correlation between two variables, whose value ranges between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables and -1 means a negative relationship between the two variables. 4. Diagnostic research design: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. This design has three parts of the research: · Inception of the issue · Diagnosis of the issue · Solution for the issue 5. Explanatory research design: Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The research explains unexplored aspects of a subject and details about what, how, and why of research questions. QUALITATIVE RESEARCH

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication. This method is not only about “what” people think but also “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that the number of men visiting this store are more. One good method to determine why women were not visiting the store is to conduct an in-depth interview of potential customers in the category.Example, on successfully interviewing female customers, visiting the nearby stores and malls, and selecting them through random sampling, it was known that the store doesn’t have enough items for women and so there were fewer women visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store, because there were more male products than female ones. Qualitative research is based on the disciplines of social sciences like psychology, sociology, and anthropology. Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses, where the interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience takes decisions can help derive conclusions in market research. Types of qualitative research methods with examples Qualitative research methods are designed in a manner that help reveal the behaviour and perception of a target audience with reference to a particular topic. There are different types of qualitative research methods like an in-depth interview, focus groups, ethnographic research, content analysis, case study research that are usually used. The results of qualitative methods are more descriptive and the inferences can be drawn quite easily from the data that is obtained. Qualitative research methods originated in the social and behavioural sciences. Today our world is more complicated and it is difficult to understand what people think and perceive. Online qualitative research methods make it easier to understand that as it is more communicative and descriptive. The following are the qualitative research methods that are frequently used. Also, read about qualitative research examples:

1. One-on-one interview: Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent. One of the advantages of this method provides a great opportunity to gather precise data about what people believe and what their motivations are. If the researcher is well experienced asking the right questions can help him/her collect meaningful data. If they should need more information the researchers should ask such follow up questions that will help them collect more information. These interviews can be performed face-to-face or on phone and usually can last between half an hour to two hours or even more. When the in-depth interview is conducted face to face it gives a better opportunity to read the body language of the respondents and match the responses. 2. Focus groups: A focus group is also one of the commonly used qualitative research methods, used in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market. The main aim of the focus group is to find answers to the “why” “what” and “how” questions. One advantage of focus groups is, you don’t necessarily need to interact with the group in person. Nowadays focus groups can be sent an online survey on various devices and responses can be collected at the click of a button. Focus groups are an expensive method as compared to the other online qualitative research methods. Typically they are used to explain complex processes. This method is very useful when it comes to market research on new products and testing new concepts. 3. Ethnographic research: Ethnographic research is the most in-depth observational method that studies people in their naturally occurring environment. This method requires the researchers to adapt to the target audiences’ environments which could be anywhere from an organization to a city or any remote location. Here geographical constraints can be an issue while collecting data.

This research design aims to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings first hand. This type of research method can last from a few days to a few years, as it involves in-depth observation and collecting data on those grounds. It’s a challenging and a time-consuming method and solely depends on the expertise of the researcher to be able to analyze, observe and infer the data. 4. Case study research: The case study method has evolved over the past few years and developed into a valuable qual research method. As the name suggests it is used for explaining an organization or an entity. This type of research method is used within a number of areas like education, social sciences and similar. This method may look difficult to operate, however, it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data. 5. Record keeping: This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. This is similar to going to a library. There one can go over books and other reference material to collect relevant data that can likely be used in the research. 6. Process of observation: Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since, the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. Qualitative observation is primarily used to equate quality differences. Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing. This doesn’t involve measurements or numbers but instead characteristics. QUALITATIVE RESEARCH: DATA COLLECTION AND ANALYSIS A. Qualitative data collection

Qualitative data collection allows collecting data that is non-numeric and helps us to explore how decisions are made and provide us with detailed insight. For reaching such conclusions the data that is collected should be holistic, rich, and nuanced and findings to emerge through careful analysis. 1. Whatever method a researcher chooses for collecting qualitative data, one aspect is very clear the process will generate a large amount of data. In addition to the variety of methods available, there are also different methods of collecting and recording the data. For example, if the qualitative data is collected through a focus group or one-to-one discussion, there will be handwritten notes or video recorded tapes. If there are recording they should be transcribed and before the process of data analysis can begin. 2. As a rough guide, it can take a seasoned researcher 8-10 hours to transcribe the recordings of an interview, which can generate roughly 20-30 pages of dialogues. Many researchers also like to maintain separate folders to maintain the recording collected from the different focus group. This helps them compartmentalize the data collected. 3. In case there are running notes taken, which are also known as field notes, they are helpful in maintaining comments, environmental contexts, nonverbal cues etc. These filed notes are helpful and can be compared while transcribing audio recorded data. Such notes are usually informal but should be secured in a similar manner as the video recordings or the audio tapes. B. Qualitative data analysis Qualitative data analysis such as notes, videos, audio recordings images, and text documents. One of the most used methods for qualitative data analysis is text analysis. Text analysis is a data analysis method that is distinctly different from all other qualitative research methods, where researchers analyze the social life of the participants in the research study and decode the words, actions, etc. There are images also that are used in this research study and the researchers analyze the context in which the images are used and draw inferences from them. In the last decade, text analysis through what is shared on social media platforms has gained supreme popularity.

Characteristics of qualitative research methods 1. Qualitative research methods usually collect data at the sight, where the participants are experiencing issues or problems. These are real-time data and rarely bring the participants out of the geographic locations to collect information. 2. Qualitative researchers typically gather multiple forms of data, such as interviews, observations, and documents, rather than rely on a single data source. 3. This type of research method works towards solving complex issues by breaking down into meaningful inferences, that is easily readable and understood by all. 4. Since it’s a more communicative method, people can build their trust on the researcher and the information thus obtained is raw and unadulterated. Qualitative research method case study Let’s take the example of a bookstore owner who is looking for ways to improve their sales and customer outreach. An online community of members who were the loyal patrons of the bookstore were interviewed and related questions were asked and the questions were answered by them.At the end of the interview, it was realized that most of the books in the stores were suitable for adults and there were not enough options for children or teenagers. By conducting this qualitative research, the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research now the bookstore owner can now keep books for different age categories and can improve his sales and customer outreach. Such qualitative research method examples can serve as the basis to indulge in further quantitative research, which provides remedies. When to use qualitative research? Researchers make use of qualitative research techniques when they need to capture accurate, in-depth insights. It is very useful to capture “factual data”. Here are some examples of when to use qualitative research. • Developing a new product or generating an idea.

• Studying your product/brand or service to strengthen your marketing strategy. • To understand your strengths and weaknesses. • Understanding purchase behaviour. • To study the reactions of your audience to marketing campaigns and other communications. • Exploring market demographics, segments, and customer groups. • Gathering perception data of a brand, company, or product. SUMMARY Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why. Descriptive research is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the \"what\" question. A good example of a qualitative research method would be unstructured interviews. This is because these generate qualitative data through the use of open questions allowing a respondent to talk at length, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.Remember that qualitative data isn’t limited to words or text. Photographs, videos, and even sound recordings can be considered qualitative data. KEYWORDS • Quantifiable- able to be expressed or measured as a quantity. • Exploratory- relating to or involving exploration or investigation. • Experimental- relating to scientific experiments. • Conversational- as used in conversation; not formal. • Patronage- the support given by a patron. • Anthropology- the studies of human societies and cultures and their development. • Perception- awareness of something through senses. LEARNING ACTIVITY 1. How do you write a questionnaire in qualitative research?

2. What makes a good qualitative research question? UNIT END QUESTIONS (MCQ’S AND DESCRIPTIVE) A. Descriptive Questions 1. What is descriptive research and examples? 2. What is the purpose of descriptive research? 3. What is qualitative research examples? 4. What is the purpose of qualitative research? 5. What are the 4 types of qualitative research? B. Multiple Choice Questions (Mcq’s) 1. Which of the following is characteristic of qualitative research? a. Generalization to the population b. Random sampling c. Unique case orientation d. Standardized tests and measures 2.Which of the following is a method that is commonly used in qualitative research? a. Self-completion questionnaires b. Surveys c. Ethnography 3. Why do qualitative researchers like to give detailed descriptions of social settings? a. To provide a contextual understanding of social behaviour b. Because once they have left the field, it is difficult to remember what happened c. So that they can compare their observations as a test of reliability d. Because they do not believe in going beyond the level of description

4. The flexibility and limited structure of qualitative research designs is an advantage because: a. The researcher does not impose any predetermined formats on the social world b. Allows for unexpected results to emerge from the data c. The researcher can adapt their theories and methods as the project unfolds d. All of the above 5. which of the following is not a criticism of qualitative research? a. The studies are difficult to replicate b. There is a lack of transparency c. The approach is too rigid and inflexible d. The accounts are too subjective and impressionistic Answer 1. c 2. c 3. b 4. d 5. c REFERENCES • Quantitative Research Methods in the Social Sciences Paul S. Maxim • The SAGE Handbook of Qualitative Research • Kothari, C.R. Research Methodology: Methods and Techniques. 10th Ed. 2012, New Age International. • Sinha, S.C. and Dhiman, A.K., 2002. Research Methodology, EssEss Publications. 2 volumes. • Anthony, M., Graziano, A.M. and Raulin, M.L., Research Methods: A Process of Inquiry, Allyn and Bacon. 2009. • Leedy, P.D. and Ormrod, J.E., 2004 Practical Research: Planning and Design, Prentice Hall. • Fink, A., Conducting Research Literature Reviews: From the Internet to Paper. 2009, Sage Publications

• Coley, S.M. and Scheinberg, C. A., \"Proposal Writing\", 1990, Sage Publications. • The Craft of Research, Third Edition Book by Gregory G. Colomb, Joseph M. Williams, and Wayne C. Booth • Berg, Bruce L., 2009, Qualitative Research Methods for the Social Sciences. Seventh Edition. Boston MA: Pearson Education Inc. • Creswell, J. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, California: Sage Publications.

UNIT 5: SAMPLING AND DATA COLLECTION STRUCTURE 1. Learning Objectives 2. Introduction 3. Data collection methods 4. Types of Sampling 5. Sampling methods 6. Questionnaire : Explanation 7. Interview 8. Data processing 9. Presentation of different types of Data 10. Summary 11. Keywords 12. Learning Activity 13. Unit End Questions 14. References LEARNING OBJECTIVES While studying this chapter we will learn about the following points: • The purpose of research and its different techniques to write a research. • The various important aspects that are a must for research. • Writing of research as an important factor and its guidelines. • Hypothesis –an important factor. • The different ways in which data can be collected and presented. INTRODUCTION Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let’s begin by covering some of the key terms in sampling like “population” and “sampling frame.” Then, because some types of sampling rely upon quantitative models, we’ll talk about some of the statistical terms used in sampling. Samples are parts of a population. For example, you might have a list of

information on 100 people (your “sample”) out of 10,000 people (the “population”). You can use that list to make some assumptions about the entire population’s behaviour. DATA COLLECTION METHODS There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research. • Observational Method The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour. It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research. This process can be said to be either quantitative or qualitative. Quantitative observation involved the objective collection of numerical data, whose results can be analyzed using numerical and statistical methods. Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions. • Case Study Method A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group. This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization. • Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers. Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies. Survey research can be carried out both online and offline using the following methods • Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Form plus has amazing tools and features that will help increase response rates. • Offline Surveys: This includes paper forms, mobile offline forms, and SMS-based forms. TYPES OF SAMPLING: SAMPLING METHODS Sampling in market research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling. 1. Probability sampling: Probability sampling is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. All the members have an equal opportunity to be a part of the sample with this selection parameter. 2. Non-probability sampling: In non-probability sampling, the researcher chooses members for research at random. This sampling method is not a fixed or predefined selection process. This makes it difficult for all elements of a population to have equal opportunities to be included in a sample. In this blog, we discuss the various probability and non-probability sampling methods that you can implement in any market research study. Types of probability sampling with examples: Probability sampling is a sampling technique in which researchers choose samples from a larger population using a method based on the theory of probability. This sampling

method considers every member of the population and forms samples based on a fixed process. For example, in a population of 1000 members, every member will have a 1/1000 chance of being selected to be a part of a sample. Probability sampling eliminates bias in the population and gives all members a fair chance to be included in the sample. There are four types of probability sampling techniques: • Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample. For example, in an organization of 500 employees, if the HR team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected. • Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inference from the feedback. For example, if the United States government wishes to evaluate the number of immigrants living in the Mainland US, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data. • Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. This type of sampling method has a predefined range, and hence this sampling technique is the least time-consuming. For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will

choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10). • Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Egg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results. Uses of probability sampling There are multiple uses of probability sampling. They are: • Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived from a population is negligible to non-existent. The selection of the sample mainly depicts the understanding and the inference of the researcher. Probability sampling leads to higher quality data collection as the sample appropriately represents the population. • Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed towards one demographic. For example, if Square would like to understand the people that could make their point-of- sale devices, a survey conducted from a sample of people across the US from different industries and socio-economic backgrounds helps. • Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data. Types of non-probability sampling with examples The non-probability method is a sampling method that involves a collection of feedback based on a researcher or statistician’s sample selection capabilities and not on a fixed selection process. In most situations, the output of a survey conducted with a non-probable sample leads to skewed results, which may not represent the desired target population. But,

there are situations such as the preliminary stages of research or cost constraints for conducting research, where non-probability sampling will be much more useful than the other type. Four types of non-probability sampling explain the purpose of this sampling method in a better manner: • Convenience sampling: This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling, because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non- probability sampling method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of research, convenience sampling is used. For example, start-ups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly. • Judgmental or purposive sampling: Judgemental or purposive samples are formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample. • Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, it will be extremely challenging to survey shelter less people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method in situations where the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.

• Quota sampling: In Quota sampling, the selection of members in this sampling technique happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples. Uses of non-probability sampling Non-probability sampling is used for the following: • Create a hypothesis: Researchers use the non-probability sampling method to create an assumption when limited to no prior information is available. This method helps with the immediate return of data and builds a base for further research. • Exploratory research: Researchers use this sampling technique widely when conducting qualitative research, pilot studies, or exploratory research. • Budget and time constraints: The non-probability method when there are budget and time constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is easier to pick respondents at random and have them take the survey or questionnaire. How do you decide on the type of sampling to use? For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method. • Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy. • Identify the effective sampling techniques that might potentially achieve the research goals. • Test each of these methods and examine whether they help in achieving your goal. • Select the method that works best for the research. Difference between Probability Sampling and Non-Probability Sampling Methods Probability Sampling Methods Non-Probability Sampling

Methods Probability Sampling is a Non-probability sampling is a sampling technique in which sampling technique in which the Definition samples from a larger population researcher selects samples based on are chosen using a method based the researcher’s subjective judgment on the theory of probability. rather than random selection. Alternatively Random sampling method. Non-random sampling method Known as Population The population is selected The population is selected selection randomly. arbitrarily. Nature The research is conclusive. The research is exploratory. Since there is a method for Since the sampling method is Sample deciding the sample, the arbitrary, the population population demographics are demographics representation is conclusively represented. almost always skewed. Takes longer to conduct since the This type of sampling method is Time Taken research design defines the quick since neither the sample or selection parameters before the selection criteria of the sample are market research study begins. undefined. Results This type of sampling is entirely This type of sampling is entirely unbiased and hence the results are biased and hence the results are unbiased too and conclusive. biased too, rendering the research speculative. Hypothesis In probability sampling, there is In non-probability sampling, the an underlying hypothesis before hypothesis is derived after the study begins and the objective conducting the research study. of this method is to prove the

hypothesis. We have looked at the different types of sampling methods above and their subtypes. To encapsulate the whole discussion, though, the significant differences between probability sampling methods and non-probability sampling methods are as below: Organisational and institutional documents have been a staple in qualitative research formany years. In recent years, there has been an increase in the number of research reportsand journal articles that mention document analysis as part of the methodology. What hasbeen rather glaring is the absence of sufficient detail in most reports found in the reviewedliterature, regarding the procedure followed and the outcomes of the analyses of documents. Moreover, there is some indication that document analysis has not always been used effectivelyin the research process, even by experienced researchers. This article examines the place and function of documents in qualitative research. Writtenmainly for research novices, the article describes the nature and forms of documents, outlinesthe strengths and weaknesses of document analysis, and offers specific examples of the useof documents in the research process. Suggestions for doing document analysis are included. The fundamental purpose of this article is to increase knowledge and understanding ofdocument analysis as a qualitative research method with a view to promoting its effectiveuse Organisational and institutional documents have been a staple in qualitative research formany years. In recent years, there has been an increase in the number of research reportsand journal articles that mention document analysis as part of the methodology. What hasbeen rather glaring is the absence of sufficient detail in most reports found in the reviewedliterature, regarding the procedure followed and the outcomes of the analyses of documents. Moreover, there is some indication that document analysis has not always been used effectivelyin the research process, even by experienced researchers. This article examines the place and function of documents in qualitative research. Writtenmainly for research novices, the article describes the nature and forms of documents, outlinesthe

strengths and weaknesses of document analysis, and offers specific examples of the useof documents in the research process. Suggestions for doing document analysis are included. The fundamental purpose of this article is to increase knowledge and understanding ofdocument analysis as a qualitative research method with a view to promoting its effectiveuse Organisational and institutional documents have been a staple in qualitative research formany years. In recent years, there has been an increase in the number of research reportsand journal articles that mention document analysis as part of the methodology. What hasbeen rather glaring is the absence of sufficient detail in most reports found in the reviewedliterature, regarding the procedure followed and the outcomes of the analyses of documents. Moreover, there is some indication that document analysis has not always been used effectivelyin the research process, even by experienced researchers. This article examines the place and function of documents in qualitative research. Writtenmainly for research novices, the article describes the nature and forms of documents, outlinesthe strengths and weaknesses of document analysis, and offers specific examples of the useof documents in the research process. Suggestions for doing document analysis are included. The fundamental purpose of this article is to increase knowledge and understanding ofdocument analysis as a qualitative research method with a view to promoting its effectiveuse Organisational and institutional documents have been a staple in qualitative research formany years. In recent years, there has been an increase in the number of research reportsand journal articles that mention document analysis as part of the methodology. What hasbeen rather glaring is the absence of sufficient detail in most reports found in the reviewedliterature, regarding the procedure followed and the outcomes of the analyses of documents. Moreover, there is some indication that document analysis has not always been used effectivelyin the research process, even by experienced researchers. This article examines the place and function of documents in qualitative research. Writtenmainly for research novices, the article describes the nature and forms of documents, outlinesthe strengths and weaknesses of document analysis, and offers specific examples of the useof documents in the research process. Suggestions for doing document analysis are included. The fundamental purpose of this article is to increase knowledge and understanding ofdocument analysis as a qualitative research method with a view to promoting its effectiveuse

SAMPLING METHODS When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods: Probability sampling involves random selection, allowing you to make statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data. Population vs sample First, you need to understand the difference between a population and a sample, and identify the target population of your research. The population is the entire group that you want to draw conclusions about. The sample is the specific group of individuals that you will collect data from. The population can be defined in terms of geographical location, age, income, and many other characteristics.

It can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or students in a single school. It is important to carefully define your target population according to the purpose and practicalities of your project. If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. Sampling frame The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population). Example You are doing research on working conditions at Company X. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database which lists the names and contact details of every employee. Sample size The number of individuals in your sample depends on the size of the population, and on how precisely you want the results to represent the population as a whole. You can use a sample size calculator to determine how big your sample should be. In general, the larger the sample size, the more accurately and confidently you can make inferences about the whole population. Probability sampling methods Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, you need to use a probability sampling technique. There are four main types of probability sample.

1. Simple random sampling In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance. Example You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers. 2. Systematic sampling Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals. Example

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people. If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees. 3. Stratified sampling This sampling method is appropriate when the population has mixed characteristics, and you want to ensure that every characteristic is proportionally represented in the sample. You divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role). From the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup. Example The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people. 4. Cluster sampling Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups. If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population. Example The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters. Receive feedback on language, structure and layout Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Grammar Style consistency Non-probability sampling methods In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included. This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias, and you can’t use it to make valid statistical inferences about the whole population. Non-probability sampling techniques are often appropriate for exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling A convenience sample simply includes the individuals who happen to be most accessible to the researcher. This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Example You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university. 2. Voluntary response sampling Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others. Example You send out the survey to all students at your university and a lot of students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students. 3. Purposive sampling This type of sampling involves the researcher using their judgement to select a sample that is most useful to the purposes of the research. It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences. An effective purposive sample must have clear criteria and rationale for inclusion. Example You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services. 4. Snowball sampling If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. Example You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area. QUESTIONNAIRE: DEFINITION, EXAMPLES, DESIGN AND TYPES

A questionnaire is a research instrument consisting of a series of questions for the purpose of gathering information from respondents. Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, computer or post. Questionnaires provide a relatively cheap, quick and efficient way of obtaining large amounts of information from a large sample of people. Data can be collected relatively quickly because the researcher would not need to be present when the questionnaires were completed. This is useful for large populations when interviews would be impractical. However, a problem with questionnaires is that respondents may lie due to social desirability. Most people want to present a positive image of themselves and so may lie or bend the truth to look good, e.g., pupils would exaggerate revision duration.Questionnaires can be an effective means of measuring the behaviour, attitudes, preferences, opinions and, intentions of relatively large numbers of subjects more cheaply and quickly than other methods. An important distinction is between open-ended and closed questions. Often a questionnaire uses both open and closed questions to collect data. This is beneficial as it means both quantitative and qualitative data can be obtained. Closed Questions Closed questions structure the answer by only allowing responses which fit into pre-decided categories. Data that can be placed into a category is called nominal data. The category can be restricted to as few as two options, i.e., dichotomous (e.g., 'yes' or 'no,' 'male' or 'female'), or include quite complex lists of alternatives from which the respondent can choose (e.g., polytomous). Closed questions can also provide ordinal data (which can be ranked). This often involves using a continuous rating scale to measure the strength of attitudes or emotions. For example, strongly agree / agree / neutral / disagree / strongly disagree / unable to answer. Closed questions have been used to research type A personality (e.g., Friedman & Rosenman, 1974), and also to assess life events which may cause stress (Holmes & Rahe, 1967), and attachment (Fraley, Waller, & Brennan, 2000). Strengths

They can be economical. This means they can provide large amounts of research data for relatively low costs. Therefore, a large sample size can be obtained which should be representative of the population, which a researcher can then generalize from. The respondent provides information which can be easily converted into quantitative data (e.g., count the number of 'yes' or 'no' answers), allowing statistical analysis of the responses. The questions are standardized. All respondents are asked exactly the same questions in the same order. This means a questionnaire can be replicated easily to check for reliability. Therefore, a second researcher can use the questionnaire to check that the results are consistent. Limitations They lack detail. Because the responses are fixed, there is less scope for respondents to supply answers which reflect their true feelings on a topic. Open Questions Open questions allow people to express what they think in their own words. Open-ended questions enable the respondent to answer in as much detail as they like in their own words. For example: “can you tell me how happy you feel right now?” If you want to gather more in-depth answers from your respondents, then open questions will work better. These give no pre-set answer options and instead allow the respondents to put down exactly what they like in their own words. Open questions are often used for complex questions that cannot be answered in a few simple categories but require more detail and discussion. Lawrence Kohlberg presented his participants with moral dilemmas. One of the most famous concerns a character called Heinz who is faced with the choice between watching his wife die of cancer or stealing the only drug that could help her. Participants were asked whether Heinz should steal the drug or not and, more importantly, for their reasons why upholding or breaking the law is right. Strengths Rich qualitative data is obtained as open questions allow the respondent to elaborate on their answer. This means the research can find out why a person holds a certain attitude.

Limitations Time-consuming to collect the data. It takes longer for the respondent to complete open questions. This is a problem as a smaller sample size may be obtained. Time-consuming to analyze the data. It takes longer for the researcher to analyze qualitative data as they have to read the answers and try to put them into categories by coding, which is often subjective and difficult. However, Smith (1992) has devoted an entire book to the issues of thematic content analysis the includes 14 different scoring systems for open-ended questions. Not suitable for less educated respondents as open questions require superior writing skills and a better ability to express one's feelings verbally. Questionnaire Design With some questionnaires suffering from a response rate as low as 5%, it is essential that a questionnaire is well designed. There are a number of important factors in questionnaire design. Aims Make sure that all questions asked address the aims of the research. However, use only one feature of the construct you are investigating in per item. Length The longer the questionnaire, the less likely people will complete it. Questions should be short, clear, and be to the point; any unnecessary questions/items should be omitted. Pilot Study Run a small scale practice study to ensure people understand the questions. People will also be able to give detailed honest feedback on the questionnaire design. Question Order Questions should progress logically from the least sensitive to the most sensitive, from the factual and behavioural to the cognitive, and from the more general to the more specific. The researcher should ensure that the answer to a question is not influenced by previous questions.

Terminology There should be a minimum of technical jargon. Questions should be simple, to the point and easy to understand. The language of a questionnaire should be appropriate to the vocabulary of the group of people being studied. Use statements which are interpreted in the same way by members of different subpopulations of the population of interest. For example, the researcher must change the language of questions to match the social background of respondents' age / educational level / social class / ethnicity etc. Presentation Make sure it looks professional, include clear and concise instructions. If sent through the post make sure the envelope does not signify ‘junk mail.’ Ethical Issues The researcher must ensure that the information provided by the respondent is kept confidential, e.g., name, address, etc. This means questionnaires are good for researching sensitive topics as respondents will be more honest when they cannot be identified. Keeping the questionnaire confidential should also reduce the likelihood of any psychological harm, such as embarrassment. Participants must provide informed consent prior to completing the questionnaire, and must be aware that they have the right to withdraw their information at any time during the survey/ study. Problems with Postal Questionnaires The data might not be valid (i.e., truthful) as we can never be sure that the right person actually completed the postal questionnaire. Also, postal questionnaires may not be representative of the population they are studying? This is because some questionnaires may be lost in the post reducing the sample size. The questionnaire may be completed by someone who is not a member of the research population.

Those with strong views on the questionnaire’s subject are more likely to complete it than those with no interest in it. Benefits of a Pilot Study A pilot study is a practice / small-scale study conducted before the main study. It allows the researcher to try out the study with a few participants so that adjustments can be made before the main study, so saving time and money. It is important to conduct a questionnaire pilot study for the following reasons: Check that respondents understand the terminology used in the questionnaire. Check that emotive questions have not been used as they make people defensive and could invalidate their answers. Check that leading questions have not been used as they could bias the respondent's answer. Ensure the questionnaire can be completed in an appropriate time frame (i.e., it's not too long) INTERVIEWS An interview in qualitative research is a conversation where questions are asked to elicit information. The interviewer is usually a professional or paid researcher, sometimes trained, who poses questions to the interviewee, in an alternating series of usually brief questions and answers. Interviews Interviews are a far more personal form of research than questionnaires. In the personal interview, the interviewer works directly with the respondent. Unlike with mail surveys, the interviewer has the opportunity to probe or ask follow-up questions. And, interviews are generally easier for the respondent, especially if what is sought is opinions or impressions. Interviews can be very time consuming and they are resource intensive. The interviewer is considered a part of the measurement instrument and interviewers have to be well trained in how to respond to any contingency. Almost everyone is familiar with the telephone interview. Telephone interviews enable a researcher to gather information rapidly. Most of the major public opinion polls that are reported were based on telephone interviews. Like personal interviews, they allow

for some personal contact between the interviewer and the respondent. And, they allow the interviewer to ask follow-up questions. But they also have some major disadvantages. Many people don’t have publicly-listed telephone numbers. Some don’t have telephones. People often don’t like the intrusion of a call to their homes. And, telephone interviews have to be relatively short or people will feel imposed upon. Unstructured interviews: These are interviews that take place with few, if any, interview questions. They often progress in the manner a normal conversation would, however it concerns the research topic under review. It is a relatively formless interview style that researchers use to establish rapport and comfort with the participant, and is extremely helpful when researchers are discussing sensitive topics. The researcher is expected to probe participants in order to obtain the most rich and in-depth information possible. If you select this interview style, just keep in mind that you may have to conduct several rounds of interviews with your participants in order to gather all the information you need. Since you do not use a standard interview protocol, sometimes participant’s narratives manoeuvre the conversation away from other aspects of the research topic you want to explore; it is a part of the conversational style this interview method requires. Semi structured interviews: These are interviews that use an interview protocol to help guide the researcher through the interview process. While this can incorporate conversational aspects, it is mostly a guided conversation between the researcher and participant. It does maintain some structure (hence the name semi structured), but it also provides the researcher with the ability to probe the participant for additional details. If you decide to choose this interview method, understand that it offers a great deal of flexibility for you as a researcher. You do not have to worry about needing to conduct several rounds of interviews because your interview protocol will keep you focused on gathering all the information that you need to answer your research question. Even though that is the goal with an interview protocol, there may be a need for additional probing so that you can get more details about participants’ thoughts, feelings, and opinions. Structured interviews: These are interviews that strictly adhere to the use of an interview protocol to guide the researcher. It is a more rigid interview style, in that only the questions on the interview protocol are asked. As a result, there are not a lot of opportunities to probe and further explore topics that participants bring up when answering the interview questions.

This method can be advantageous when researchers have a comprehensive list of interview questions, since it helps target the specific phenomenon or experience that the researcher is investigating. It makes for expedient interviewing and will gather the correct information that you need, so there should not be much need for you to do follow-up interviews for missed or forgotten questions. Without data processing, companies limit their access to the very data that can hone their competitive edge and deliver critical business insights. That’s why it’s crucial for all companies to understand the necessity of processing all their data, and how to go about it. DATA PROCESSING Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output. Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organizational load. Six stages of data processing 1. Data collection Collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehouses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality. 2. Data preparation Once the data is collected, it then enters the data preparation stage. Data preparation, often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is diligently checked for any errors. The purpose of this step is to eliminate bad data (redundant, incomplete, or incorrect data) and begin to create high-quality data for the best business intelligence. 3. Data input

The clean data is then entered into its destination (perhaps a CRM like Salesforce or a data warehouse like Redshift), and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information. 4. Processing During this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.). 5. Data output/interpretation The output/interpretation stage is the stage at which data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data for their own data analytics projects. 6. Data storage The final stage of data processing is storage. After all of the data is processed, it is then stored for future use. While some information may be put to use immediately, much of it will serve a purpose later on. Plus, properly stored data is a necessity for compliance with data protection legislation like GDPR. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed. What is Data Presentation and Analysis? Data presentation and analysis forms an integral part of all academic studies, commercial, industrial and marketing activities as well as professional practices. Presentation of data requires skills and understanding of data. It is necessary to make use of collected data which is considered to be raw data. This raw data must be processed to be used or for any application. Data analysis helps in the interpretation of data and help take a decision or answer the research question. This can be done by using various Data processing tools and Software’s. Data analysis starts with the collection of data, followed by data processing. This processing of data can be done by various data processing methods and sorting it. Processed data helps in obtaining information from it, as the raw data is non-comprehensive in nature.

Presenting the data includes the pictorial representation of the data by using graphs, charts, maps and other methods. These methods help in adding the visual aspect to data which makes it much more comfortable and easy to understand. This visual representation of data is called as data visualization. Various methods of data presentation can be used to present data and facts based on available data set. Widely used format and data presentation techniques are mentioned below: As Text – Raw data with proper formatting, categorisation, indentation is most extensively used and is a very effective way of presenting data. Text format is widely found in books, reports, research papers and in this article itself. In Tabular Form – Tabular form is generally used to differentiate, categorise, relate different datasets. It can be a simple pros & cons table, or a data with corresponding value such as annual GDP, a bank statement, monthly expenditure etc. Quantitative data usually require such tabular form. In Graphical Form – Data can further be presented in a simpler and even easier form by means of using graphics. The input for such graphical data can be another type of data itself or some raw data. For example, a bar graph & pie chart takes tabular data as input. The tabular data in such case is processed data itself but provides limited use. Converting such data or raw data into graphical form directly makes it quicker and easier to interpret. The Significance and Importance of Data Presentation Data presentation and analysis plays an essential role in every field. An excellent presentation can be a deal maker or deal breaker. Some people make an incredibly useful presentation with the same set of facts and figures which are available with others. At times people work really hard but fail to present it properly and have lost essential deals. The work which they did was unable to impress the decision makers. So to get the job done, especially while dealing with clients or higher authorities, Presentation Matters! No one is willing to spend hours in understanding what you have to show and this is precisely why presentation matters! It is thus essential to have a clarity on what is data presentation. Some of the factors which directly affects the data presentation include data quality, correlation coefficient, vector images, colour scheme etc.

Data analysis helps people in content analysis and understanding the results of surveys conducted, makes use of already existing studies to obtain new results. Helps to validate the existing research or to add/expand the current research. Data Presentation and Analysis or Data Analysis and Presentation? These two go hand in hand, and it will be difficult to provide a complete differentiation between the two. Adding visual aspect to data or sorting it using grouping and presenting it in the form of table is a part of the presentation. Doing this further helps in analyzing data. During a study with an aim and multiple objectives, data analysis will be required to complete the required objectives. Compiling or presenting the analyzed data will help in overall analysis and concluding the study. A variety of data can be used in presentations. Some of these types include: Time Series Data Bar Charts Combo Charts Pie Charts Tables Geo Map Scorecard Scatter Charts Bullet Charts Area Chart Text & Images Steps for Presenting and Analyzing Data: Frame the objectives of the study and make a list of data to be collected and its format. Collect/obtain data from primary or secondary sources. Change the format of data i.e., table, maps, graphs, etc. in the desired format.

Sort data through grouping, discarding the extra data and deciding the required form to make data comprehensible. Make charts and graphs to help to add visual part and analyze trends. Analyse trends and relate the information to fulfill the objectives. Presentation of Data: A presentation should have a predefined sequence of arguments being made to support the study. Start with stating the Aim of study and the objectives required to reach the aim. Break the objectives in multiple parts and make a list of data to be collected. Noting down the sources of data, form in which data exist and needs to be obtained. Also conducting a primary survey for information which does not exist. Form and explain the methodology adapted to carry out a study. Data collection through primary survey needs to have well thought of sampling methods. This will help in reducing the efforts and increasing efficiency. Sample size should be given importance and correct sampling technique should be applied. Present only the required information and skip the background research to make your point more clear. Do not forget to give credits and references in the end and where ever required. The presentation can be done using software such as Microsoft Power Point, Prezi, Google Analytics and other analytic software. It can also be done by making models, presenting on paper or sheets, on maps or by use of boards. The methods selected depends on the requirement and the resources available. PRESENATION OF THE DIFFERENT TYPE OF DATA – WHICH FORMAT TO CHOOSE? Since there are number of options available while presenting data, careful consideration should be given to the method being used. A basic understanding of the desired result/ form is helpful to choose the correct form of representation. One cannot expect to get liner data from a pie chart, thus basic knowledge and application of different type of presentation methods saves time. Additionally, there should be enough sample available so as to get some meaningful analysis and result. Some of the popular ways of presenting the data includes

Line graph, column chart, box pot, vertical bar, scatter plot. These and other types are explain below with brief information about their application. Secondary surveys form a significant part of data research and primary means of data collection by conducting various studies and making use of existing data from multiple sources. The data thus obtained from multiple sources like Census department, Economics and Statistics Department, Election Commission, Water Board, Municipal Bodies, Economic surveys, Website feedbacks, Scientific research, etc. is compiled and analyzed. Data is also required to forecast and estimate the change in the requirement of various resources and thus provide them accordingly. Phasing and prioritization form another important part for the effective implementation of the proposals. Such presentation of data and information can be either by means of manual hand drawings/ graphs & tables, whereas much effective and accurate way for such presentation is by means of specialised computer software’s. Different types of charts which can be used for data presentation and analysis or data visualisation are explained below: Bar Charts/Bar Graphs: These are one of the most widely used charts for showing the grown of a company over a period. There are multiple options available like stacked bar graphs and the option of displaying a change in numerous entities. These look as shown in the image below: Line Chart: These are best for showing the change in population, i.e., for showing the trends. These also work well for explaining the growth of multiple areas at the same time. Pie Charts: These work best for representing the share of different components from a total 100%. For, e.g. contribution of different sectors to GDP, the population of different states in a country, etc. Combo Chart: As the name suggests it is a combination of more than one chart type. The one shown in the figure below is a combination of line and bar graph. These save space and are at times more effective than using two different charts. There can even be 3 or more charts depending on the requirement. Most Popular and Commonly used Charts in everyday life:

1. Area Chart – It is one of the most popular charts which is used to show continuity across a data set or variable. It is very similar to the line chart and is often used for plotting time series. The area chart is also useful for plotting continuous variables. 2. Correlogram – It is mostly used for testing the level of correlation between the given variable of a particular data set. The matrix cells can be coloured or shaded for showing the correlation value. The cells which are darker as compared to others have a high correlation value. For example, let’s examine the correlation between weight, cost, sales outlet, established year and others. 3. Scatter Plot – Scatter Plot is most commonly used for establishing the relationship between two or more than two variables. In the above dataset, we can create visualizations of items as per their given cost by using a scatter plot with the help of two variables MRP and visibility. 4. Stacked Bar Chart – Stacked Bar chart is also a type of bar chart which is used by combining several categorical variables. From our given database, if we want to get the number of outlets on the basis of different variables such as outlet location type, the stacked bar chart will visualize the data in the most appropriate format. 5. Bar Chart – This type of charts is used you want to use a categorical and continuous variable together. In our given dataset, if we want to know how many stores were developed in a particular year, then a bar chart is the most preferred option. 6. Heat Map– Heatmap is used to find the relationship between two or more variables by using different shades of colour. In a heatmap, the first two dimensions are represented as axis and the other dimension by different shades of colour. If you want to find the cost of each item on every store, you can plot a heatmap using three variable such as the type of item, price of item and outlet identifier. What are Data Interpretation Methods? Data interpretation methods are how analysts help people make sense of numerical data that has been collected, analyzed and presented. Data, when collected in raw form, may be difficult for the layman to understand, which is why analysts need to break down the information gathered so that others can make sense of it.

For example, when founders are pitching to potential investors, they must interpret data (e.g. market size, growth rate, etc.) for better understanding. There are 2 main methods in which this can be done, namely; quantitative methods and qualitative methods. Qualitative Data Interpretation Method The qualitative data interpretation method is used to analyze qualitative data, which is also known as categorical data. This method uses texts, rather than numbers or patterns to describe data. Qualitative data is usually gathered using a wide variety of person-to-person techniques, which may be difficult to analyze compared to the quantitative research method. Unlike the quantitative data which can be analyzed directly after it has been collected and sorted, qualitative data needs to first be coded into numbers before it can be analyzed. This is because texts are usually cumbersome, and will take more time and result in a lot of errors if analyzed in its original state. Coding done by the analyst should also be documented so that it can be reused by others and also analyzed. There are 2 main types of qualitative data, namely; nominal and ordinal data. These 2 data types are both interpreted using the same method, but ordinal data interpretation is quite easier than that of nominal data. In most cases, ordinal data is usually labelled with numbers during the process of data collection, and coding may not be required. This is different from nominal data that still needs to be coded for proper interpretation. Quantitative Data Interpretation Method The quantitative data interpretation method is used to analyze quantitative data, which is also known as numerical data. This data type contains numbers and is therefore analyzed with the use of numbers and not texts. Quantitative data are of 2 main types, namely; discrete and continuous data. Continuous data is further divided into interval data and ratio data, with all the data types being numeric.