INT 301 Great Basin College Types of Data Discussion Questions

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PART 1:  Explain data types!

Use Ch.6, 9 and 10 of Wang and Park.  Be sure to cite your sources in your text.  Your post should be at least 2 pages long (single-spaced).  

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Address the following for this post:

A.  Quantitative Data: locate a peer-reviewed journal article that is based on quantitative data to address the following questions.  Be sure to attach the article to the post and cite the article and book in your answers.  Number each answer please.

1.  What are quantitative data?  Provide examples from the article.

2.  Why did this study require quantitative data?

3  How did researchers obtain quantitative data?  Provide specific examples from the article.

4.  What are some ways these researchers analyzed quantitative data?

B.  Qualitative Data:  locate a peer-reviewed journal article that is based on qualitative data to address the following questions.  Be sure to attach the article to the post and cite the article and book in your answers.  Number each answer please.

1.  What are qualitative data?  Provide examples from the article.

2.  Why did this study require qualitative data?

3  How did researchers obtain qualitative data?  Provide examples from the article.

4.  What are some ways these  researchers  analyze qualitative data?

C.  What are the strengths and weaknesses of each data type?


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Library < Back Go to v Аа ili Q Chapter 6 Steps of Quantitative and Qualitative Research Designs The concept, "gravitation toward social media” is hard to measure itself. You will need to use tangible measures such as "time spent on social media each night.” Likewise, the concept “emotional effects” can be specified into multiple questions gauging how happy the child is, how social the child is, or how energetic and curious the child is, and so on. How Do You Select a Sample to Study from Your Target Population? Since the guiding principles and procedures for quantitative and qualitative research are quite different, the two kinds of research design call for somewhat different knowledge and skills. In this chapter, we are going to illustrate more detailed steps of quantitative and qualitative research designs and some issues to consider at each step. We will first discuss steps of quantitative research designs. Qualitative research designs will be discussed in the second half of the chapter. If you have already determined that your study calls for a qualitative research design and are only interested in qualitative research, skip directly to the section on qualitative research design. Quantitative research designs include various methods including surveys, experiments, and content analysis. Since the most commonly used quantitative research method is questionnaire survey, we will focus on the steps involved in survey research and assume the discussion will help those interested in other quantitative research methods as well. Students frequently ask us these questions when designing their survey research projects: • What are my independent variable and dependent variables? variables are already implied by your research questions. The independent variable in this example would be relationships with parents and the dependent variable would be school performance. Similarly, if your research question is “Are teenagers' grades negatively affected by gravitation toward social media?” then your independent variable is "gravitation toward social media” and your dependent variable is “grades.” Since you are likely to have more than one research questions in your study, you may have multiple independent and dependent variables. Sometimes, you may have several independent variables and one dependent variable, and vice versa. For example, questions such as “Do regular medical check-ups, exercise, and sufficient vegetable intakes reduce the likelihood of cancer?” and “Do cigarette bans in public buildings and higher cigarette taxes encourage smokers to quit smoking?” have multiple independent variables and a single dependent variable. On the other hand, a research question on the academic and emotional effects of bedtime reading during early childhood assumes one independent variable ("bedtime reading") and multiple dependent variables (the various "academic and emotional effects"). It is a good practice to write down your research questions and label your independent and dependent variables. When you identify and label your independent and dependent variables, you should be quite clear in your mind that an independent variable is the cause of the dependent variable and a dependent variable is the effect of the independent variable. A dependent variable must be able to vary or be affected when it is influenced by the independent variable. In another example, if you use education as an independent variable and salary as a dependent variable, then, you are anticipating that the salary of your respondents will change when their level of education changes. If a variable cannot vary or cannot be affected, then it cannot be used as a dependent variable. For example, someone's race and gender cannot be changed by the influence of other variables; thus, they cannot be used as dependent variables. In the examples above, abstract concepts such as “relationship with parents," "gravitation toward social media," and "emotional effects," need to be more specified and operationalized into measurable indicators so that you can quantify them. Operationalization is a step where you identify very specific indicators or measures for your concepts. For example, “relationships with parents” are not something you can directly observe, but you can use some very specific indicators for a good or a bad relationship. Quantifiable indicators, such as “number of arguments a teenager had with his/her parents within a month,” “number of times a teenager received a punishment from parents within a month," and “number of times a teenager violated rules set by parents” are all good ways to measure whether a teen has a good relationship with his/her parents. Or, you can simply ask the teen respondents to rate the quality of their relationship with parents on a scale of one to ten. What group of people or cases is your research about? Do your research questions concern the general population, a particular group of people, countries, schools, or other social organizations? The answer to these questions will be your study population, or target population; the term refers to the group of people or cases about whom you will conduct your study and to whom you will apply the findings of your study (Babbie 2013). Your population is also the pool of cases from which you will select a sample, or a subgroup of the cases you will actually study. As you can imagine, if you select a sample that resembles your population closely, you will be able to use your findings to tell something about your study population. But if your sample does not resemble your study population, your ability to use your study findings to predict the patterns in the study population is limited. Suppose you have selected a group of students from your university whose average grade is an A. You know it is unlikely that this sample will reflect what the average grade is in your university. The extent to which your sample "looks like" your study population is called "representativeness"; study findings from a representative sample can be generalized to the study population. For instance, if a group of spectators selected by random drawing of numbers happens to have the same demographic characteristics as the spectators in the entire stadium, this sample will be representative of the crowd in the stadium. This means that, if there is more rooting for Team A in this sample, you can generalize that there will be more support for Team A among the entire stadium crowd. to draw a particular type of sample, consult some of the references listed in this chapter. In the broadest sense, sampling methods fall within two groups: probability and non-probably sampling. In the above list, all the variety of random sampling and cluster sampling fall in the probability sampling category. Quota sampling, snowball sampling, purposive sampling and availability sampling are non-probability sampling. Probability sampling methods select participants based only on random chance. Sampling theory considers this as the best way to obtain a representative sample. To use probability sampling methods, you need access to the sampling frame, which refers to the roster of all units in your study population (e.g., an approximate list of citizens of a country, a list of residents of a community, a list of all schools, organizations, student roster, and so on), so that everyone is in the pool of available subjects and only random chance can determine whether someone is selected to be included in the sample. Non-probability samples are used when researchers do not have access to the sampling frame, or do not have a clearly identifiable study population (such as undocumented immigrants, homeless population). Research done by students like you often has to resort to non-probability sampling methods simply due to insufficient time and resources. Non-probability sampling methods are likely to introduce sampling biases because factors other than random chance will affect the selection process. It is okay to study a non-probability sample, especially for a small scale exploratory study, or if you are conducting qualitative research. Just keep in mind that your findings will have limited generalizability, and the limitation should be included in the discussion of your findings. When you select your study population, make sure that you can gain access to them. If your study population is minors (such as children or juveniles) or people with limited power (such as prisoners), you may face particular difficulties obtaining informed consents from guardians or getting permission from the heads of the institutions to enter the sites to collect data for your research. Therefore, think carefully about access before you decide to study a particular population. After you decide on your study population, decide on what your unit of analysis is. It may be individuals, universities, organizations, or countries, depending on what is most appropriate for your research. you need a sufficient number of respondents in your sample. As a general guideline, a minimum of 400 cases will be amenable for statistical data analysis. This suggestion is to reduce sampling error due to sample size. If you have a sample size of 400 cases, the standard sampling error will always be 5% or smaller no matter what the variation is in the study population (Babbie 2013). In reality, however, it may be unrealistic for a student researcher to be able to draw a sufficient size sample; you are more likely to work with much smaller size sample due to the time and resource constraints. In deciding your sample size, consult with your project supervisor, or professor, as they may have specific guidelines or requirements for sample size. Generally speaking, three principles are useful in determining sample size. First, the larger the sample size, the smaller your standard sampling error will be. At the same time, your sample is more likely to resemble the characteristics of your population and you will be able to generalize your findings to the target population. Second, if you are conducting a quantitative study, most statistical analysis techniques used in social sciences assume a normal, or the bell-curve distribution of data. If the sample is too small, say less than 100 cases, there is a good chance that you will not meet this assumption of a normal distribution. Our advice is to obtain at least a sample size of 100 respondents, if you plan to use statistical analysis techniques. With that number you will be able to use commonly used descriptive and inferential statistical techniques such as cross-tabulation analyses, chi-square tests, t-tests for comparison of means, and so on. Keep in mind that you may receive invalid answers, which you will exclude from your analysis; to obtain a sample size of a 100 valid cases; you may need to go slightly beyond your targeted sample size when you collect the data. • How do I select a sample to study from my target population? • What is an acceptable sample size for survey research? • How do I turn my concepts into variables in survey questionnaire? • What are levels of measurement and why do they matter? The first half of this chapter responds to these questions and relevant issues. In designing a survey research, the following steps are usually necessary: What Are Your Independent and Dependent Variables? How do we select a representative sample? Social science methods teach us that we can approximate a representative sample by reducing systemic selection biases in sample drawing process. In general, a selection method which only relies on random chance is considered as having no systemic selection biases (Babbie 2013). There are a variety of different ways to draw a sample from the study population: simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, quota sampling, snowball sampling, purposive sampling, and availability sampling. Some of these sampling techniques select participants using random drawing while others do not. The specific steps and details of different sampling strategies are beyond the scope of this book. If you need to refresh your memory on how The term "independent variable” is commonly used in social sciences to refer to the cause, or the variable that affects the other in a hypothesized relationship. The term "dependent variable” refers to the effects or outcomes in a hypothesized relationship. For example, let's consider the research question, “How do relationships with parents affect teenagers' school performance?” Suppose you expect that teenagers who do not have the typical quarrelsome relationships with their parents will do better in school than those who have a lot of conflicts with their parents. The independent and dependent Third, a small sample size may produce insignificant statistical results simply as a function of the sample size. Sometimes, small samples require you to use special statistical measures other than the commonly used measures mentioned above. According to probability theory, when sample size decreases, the standard error increases. For example, if you do a chi-square test with a very small sample, you may find that many of the cells in your cross-tabulation have fewer than five cases and your chi-square value is not statistically significant. If you have more than 25% of the cells with fewer than five counts, your chi-square analysis is not acceptable (George and Mallery 2000). If this is the case, you cannot use chi-squire analysis to test whether two variables are statistically independent of each other. On the other hand, if you only intend to use simpler descriptive statistics such as percentages and graphs to answer your research questions, a sample size smaller than 100 can still work. A “robust" sample, or a sufficiently large and What Is an Acceptable Sample Size for Surveys? Another issue to consider is sample size. Regardless of whether you use probability sampling or non-probability sampling, the size of the sample is an independent issue which requires your attention. If you want to conduct surveys and use computer software to do data analysis, 44% Page 105 of 267 • Location 3037 of 7225 Library < Back Go to v Аа i Q Chapter 9 Quantitative Data Analysis answer your respondent may have checked off. The number 1 stands for those who go to the university library very often, 2 stands for those who go to the university often, 5 stands for those who never visit, and 6 stands for those who do not want to tell you how often they visit or who simply do not want to answer the question. After you have coded your surveys or structured interview questionnaires, you may assign a case number to each of your respondents and start entering the data into the SPSS software. 目 then you may need to read more systematic textbooks on statistics and computer data analysis. This chapter does not attempt to give you a comprehensive training in computer data analyses; instead, it focuses on guiding student researchers, as they try to conduct quantitative data analyses using actual research projects. Students who are interested in learning systematic data analysis may want to consult How to Use SPSS, Eighth Edition: A Step-by-Step Guide to Analysis and Interpretation by Brian Cronk (2014). Another textbook, PASW Statistics 18 Guide to Data Analysis by Marija J. Norusis (2010) may be helpful for students who need more detailed instructions. Quantitative data analyses are very useful in student research. Although you might have been required to take statistics, research methods, or computer data analysis classes, doing quantitative data analysis in your own research may still pose a great challenge. The following are some frequently asked questions by students who are expected to undertake computer data analysis in their empirical research. Let's take a look to see if any of these questions are yours. • I conducted my questionnaire survey; now, how do I do data entry? • Why do I have to know the levels of measurement in my data analysis? variable is gender; to enter this information to SPSS, you can type in gender in the first cell in the second column. For the second row, you may type in the second variable in the survey, which is age. For the third row, you may type in the third variable, which is university status or year of study at a university. Since SPSS only allows you to enter one word for your variable name, you will need to use abbreviations or acronyms for your variable if one word is not appropriate for your variable. As for the third variable, you may use ustatus as your variable name instead of university status. Then later on, you may use a more descriptive variable label to inform you what the variable is. You can continue until you type in all the names of your variables in the second column. The purpose of this survey is to understand your experiences on campus at the university. There is no right or wrong answer to the questions. We would appreciate your giving us the most truthful and accurate answers possible. This questionnaire survey is anonymous and your name is not needed. All data collected from this survey will remain strictly confidential and only be used for computer data analysis. Participation in this survey is voluntary, but any information you can provide will be helpful and appreciated. You may refuse to participate or stop answering questions at any time. It should take approximately 5 minutes to complete the survey. PLEASE DO NOT WRITE YOUR NAME ON THIS QUESTIONNAIRE Starting SPSS Software How Do You Start Entering Data From Your Survey or Interview Questionnaire? After you started the IBM SPSS Statistics (Version 22) software, your computer screen looks like Figure 9.1. Two windows are now available: a Data View and Variable View window. Both are indicated at the lower left-hand corner of the screen. Select either by clicking on the tab. By default, the Data View window is activated and the computer software is ready for data entry. First of all, please tell us something about yourself. 1. What is your gender? Are you o X Male 1 Female? Eye Dructurn • I learned different procedures of data analyses, but which ones are most appropriate for my research? • What do I do, if I just want basic descriptive analyses of my data? 2. What is your age? 26. 3. What is your university status? • Which procedure should I perform to determine whether two variables are related to each other? 1. Freshman 2. Sophomore • Which analysis procedure should I use to see how several variables are related? 3. X Junior Data View Variable View 4. Senior 5. Graduate • Which analysis procedure should I use to compare different groups of people? • How do I use my data to explain the causal relationships between my independent and dependent variables? Figure 9.1 SPSS Window. Source: IBM SPSS Statistics Software (SPSS), version 22. Reproduced with permission of International Business Machines Corporation. 6. Coding After completing your questionnaire survey or structured questionnaire interviews, you are ready to use SPSS (or other software) to do data entry and data analysis. If you did not assign a number to each of the values in your survey questions, you need to code your questions first because the computer software mostly analyzes numbers but not words. What we mean by coding is to change respondents' answers into numbers and enter the numbers into the SPSS program so that you can analyze them. For example, if you asked your respondents the following question about gender without assigning a number to the two possible answers: What is your gender? male female Then, you will need to assign a number to each of the two options, for example: What is your gender? O female In this way, the number o stands for males and the number 1 stands for females. This is the task of coding. In other words, if one respondent checked “male," you will code it as “o” for a male respondent. If the respondent checked “female,” you will use the number 1 for a female respondent. Here is an example of coding that has already been built into the question. You may have asked how frequently your respondents go to the university library: How often do you go to the university library? Other 4. What is your racial background? 1. Caucasian • How do I use several independent variables to explain or predict a dependent variable? male 1 2. African 3. Asian The Data View window allows you to enter the number codes for the answers the respondent selected and the Variable View window allows you to define your variables, including variable names, value labels, missing values, data measurement levels, and other data specifications such as decimals. There is a tutorial, if you need to refresh your memory about how to use the software. The helpful tutorial is usually available when you first start the SPSS program on your computer. If it is not readily available, you may click on Help and then click on Tutorial to start the tutorial program. 4. X Hispanic 5. Native 6. Other 5. What is your current marital status? 1. Married Defining Your Variables 2. Living together as an unmarried couple • How do I interpret the data after my data analysis? • What should I include in my paper when reporting the findings of my data analysis? This chapter answers these questions and shows you how to start your data entry, select appropriate procedures for your specific data analysis, and report findings in your final report or thesis. Although there are different kinds of computer software available for data analysis, this chapter will show you how to use IBM SPSS Statistics software version 22 for computer data analysis. The basics in earlier versions are similar to this version. SPSS stands for Statistical Package for the Social Sciences and it is powerful and the most frequently used computer data analysis software for social science research. This chapter is written with the perspective that you have studied statistics and have done some practice in data analysis but may need to refresh your skills or need more help with your specific research. If you have never learned statistics or how to use SPSS to do computer data analysis, 3 Divorced 1. Very often 4. Separated 2. Often 5. X Never married 3. Sometimes We recommend that you begin your data entry by setting up your variables in the SPSS first. To do this, click on the Variable View tab and switch to the variable view window so that you can start to define and enter information about your variables. In this Variable View screen, each row represents all the information about one variable and each column is for one kind of information about all of the variables. For example, the first column automatically provides you with a serial number for each variable. The second column is to enter the variable names. In the sample survey illustrated below, the first 6. Widowed 4. Seldom 7. Other 5. Never 6. What is your father's education? 6. I do not know In this case, you already have a number code for each 68% Page 173 of 267 . Location 4789 of 7225 Library < Back Go to v Аа i Q Chapter 10 Qualitative Data Analysis meanings implied in the data. Regardless of the type of qualitative data you work with, your analysis is based on the principle of interpretation. Suppose your interviewee stated: become even more complicated. Do not worry, for almost everyone runs into this situation. While staying focused is important, it does not mean that you ignore when you find something unexpected or something new in the process of analysis. Remember that being able to immerse yourself deeply into the data and find emerging stories in them are the benefits of qualitative research. 目 Constructing a Theoretical Story with Your Data If you have conducted a qualitative research, you should have a set of non-numeric data such as texts, images, observational notes, and voice-recording. Summarizing and analyzing them can be challenging because there is no one standard procedure that fits all types of data. When you are ready to analyze and interpret your qualitative data you might ask: • What is the purpose of qualitative data analysis? • Do I need to transcribe all my interviews? I had a lot of difficulty juggling work and the family; finding someone on a short notice when my regular childcare arrangement falls through was a nightmare. You will need to figure out the meaning of this statement (i.e., interpret it). Does it mean that this person wants to spend more time with the child but cannotbecause of work? Why is it so difficult to balance work and childcare? Is it due to particular circumstances, or is it an experience common to most workers? Is childcare the only challenge this person feels as a working parent? What is her “regular childcare arrangement” and why it would not work sometimes? By considering these questions, you are beginning to interpret the meaning of the data. Obviously, thinking about contexts is an important part of interpreting your data. In the end, the goal of qualitative analysis is to tell the story of your data. Regardless of which analytic route you decide to take, you will report the prevailing patterns, claims, and ideas about your topic. It is through the process of constructing concepts and telling the story of your data that you will find answers to your initial research questions. Let's consider some more questions you might have when you analyze qualitative data. Transcribing is straightforward. You will simply play the recorded interviews and type them onto a word processing program. Transcribing is a time-consuming work, taking longer than the interviews themselves. You will probably have to allocate about three to five hours to transcribe a one-hour interview, for instance. Your university libraries may have transcribing machines which you can borrow. This will help you with the tedious task of listening to the recorded interviews, stopping, and typing. The machine allows you to use a convenient pedal to stop the recording and restart as you transcribe. Once fully transcribed, the voice data become text which you can analyze. You may also have taken field notes about the settings, and any non-verbal cues such as gestures, smiles, laughs, and facial expressions during the interviews and any group dynamics you observed during focus group discussions. These written notes should also be included in the transcribed data. If you conducted interviews in languages other than the language in which you will write your analysis, you may need to translate the transcribed interviews. For instance, if you conducted interviews with immigrants in their native languages but wish to write the report in English, you will need translated interview transcripts. follow multiple stages of analysis to find first small units of meaning and then gradually merge and group them into a few broader themes. This is the case with the grounded theory (Glaser and Strauss 1967), a popular technique in sociology, anthropology, and related disciplines for analyzing and theorizing text data. We will explain this technique in greater detail below. When you are working with images, you may treat them as symbols or signs for certain meanings; examining closely each piece of image data, you first assign a code or codes based on your interpretation of the image and, in a later stage, merge and group similar codes to construct broader themes and categories of the meaning embedded in the images. But for visual data, we frequently find studies using pre-constructed coding schemes; in this case, codes are predetermined and the researcher identify and count the images in the data, which correspond to the coding scheme. This is a common strategy in content analysis. For example, if your research investigates racial stereotypes in magazine advertisements, you may first construct, based on previous literature, categories of stereotypes on which you want to focus (e.g., Black athletes, White nuclear families, Asian women in service roles, and so on). Then, you can systematically examine the advertisements in your data to identify the images which contain the different stereotypes in your coding scheme. Content analyses report the counts and percentages of each of the thematic codes and interpret what those statistics tell us about the research topic at hand. • Where do I start? • How do I conduct an inductive analysis? Do You Need to Transcribe All Your Interviews? • What is the process of analysis when I use deductive coding? • What tools do I use to organize and summarize the codes? Coding or Identifying Themes • How do I write about my findings from qualitative data? Where Do You Start? Analyzing qualitative data frequently requires reducing long texts, video footage, and complex images into shorter and simpler labels that capture the idea. We call these "codes." Codes represent units of meaning. For example, you may use the code "work-family balance” for the above quotation. In other parts of the interviews, you may find other codes and themes such as "career disadvantages," or “unable to do everything," "feeling torn," and so on. Coding is a process to identify small and large units of meaning, which is to be done throughout the analysis process. What is the Purpose of Qualitative Data Analysis? Qualitative data analysis shares some similarities with quantitative data analysis (Neuman 2011). Both methods systematically summarize and compare data to obtain theoretical ideas from empirical data. There are key differences, however, in the purpose and procedure of qualitative data analysis that distinguishes it from quantitative data analysis. Unlike quantitative data analysis which follows standardized procedures and techniques, qualitative analysis uses a variety of creative techniques that require open and flexible approaches. While the purpose of quantitative data analysis is to test already established theories, qualitative data analysis is most often used to “conceptualize and build a new theory” (Neuman 2011: 509). For this reason, qualitative data analysis is most often inductive or "bottom-up," starting from concrete data to extract more generalizable theoretical ideas embedded in the data. There is truly a wide range of techniques for qualitative data analysis. Although we cannot cover all of them here, we will describe some of the more popular techniques in this chapter. But no matter which technique you use, there are principles common to various qualitative data analysis strategies which you can keep in mind before reviewing the different analytic techniques. Establishing Relationships between Codes/Themes Codes or units of meaning by themselves do not really tell a whole story. To truly interpret the meaning of the data you gathered, you will engage in layers of analysis about how each code or theme is related to another. What codes/themes seem to cause the others? What comes first and what comes later? What codes/themes are conflicting? Which are supplementing one another? What are the broader historical, cultural, and social contexts of the relationships? You should keep writing notes and memos on these questions and integrate them into your analysis. In a sense, theorizing and analyzing progress simultaneously during a qualitative data analysis. Since interviews and focus groups are common data collection methods many students use, we most often encounter questions such as “Do I need to tape-record my interviews?” or “Do I need to transcribe everything?” The clear answer to these questions is "yes.” The reasons are: 1) no matter how fast you write, it is simply impossible to take field notes in complete detail during interviews and in focus group discussions; 2) your field notes already reflect your immediate interpretation of the situation and are not objective records of exactly what is said. In short, tape- recording and transcribing is critical to having a record of the full range of data collected during your field research. There are a variety of recording devices today. You may use your smartphone apps, tablet devices, or a digital recorder. In case one device malfunctions, it is not a bad idea to have a back-up device when you record. We would like to remind you one more time that tape-recording requires informed consent by the participant prior to the interview. At the time of recording, you should have informed the participants that the conversation will be recorded and transcribed for the analysis. Interview recordings obtained through this proper cedure should be transcribed before you begin coding. If you were unable to obtain the informed consent, the recording cannot be used. Students who have spent several weeks or sometimes months transcribing their interviews come to us and say, “Here are my data, but I don't know what to do with these!" Unlike the answer choices in surveys which can be easily converted into numbers, qualitative data, such as images and transcribed conversations, do not readily lend themselves to a systematic analysis. You need to develop systematic yet flexible ways to summarize them. How do you do this? First, think about a few things in order to find the starting point for your analysis. • Different types of qualitative data • Deductive and inductive coding • Manual coding and computerized coding • Units of analysis in qualitative coding Coding: Deductive or Inductive Approach In quantitative analysis, the primary goal is to reduce data into numbers that can be manipulated and computed mathematically. The goal of qualitative coding is very different. The primary goal of qualitative coding is “to focus on the potential meanings of your data” (Esterberg 2002: 158). Qualitative coding will eventually allow you to systematically summarize scattered and seemingly episodic stories and images into patterns of themes and emerging theoretical stories. Keep in mind, however, that the primary focus of coding is not reducing the complexity of the data, but identifying and interpreting meaningful patterns in the data. The vast majority of qualitative researchers identify themes in the data through what we call “inductive coding procedures” – that is, to first approach the data without pre-conceived ideas, pay attention to emerging themes, and gradually classify them into a handful of recurring concepts to generate a theoretical story by linking these themes. This bottom-up approach is most common in published qualitative studies. Qualitative data coding can be done in a top-down fashion as well. When conducting content analyses, for instance, researchers often construct coding schemes first, and then sort the data according to the coding scheme. For example, a researcher who is interested in studying Back-and-Forth Processes As you can imagine, qualitative data analysis is, in no way, a straightforward process; in the process of coding, you will go back and forth to your data and may re-code and re-classify codes numerous times. It is quite probable that in the process of coding you will have new questions that had not been a part of your original research questions. You may have changed your initial assumptions. Often, the first stage of coding makes you feel that your data have There are times when an interviewee does not want the interview to be recorded. If this is the case, you will have to rely on taking notes during the interview. You may have to pause from time to time to write notes. You should tell the participant up front that you may go slow and may pause from time to time to write notes. Use shorthand notes during the interview, and immediately after the interview, find a quiet place to extend your notes while your memory is still fresh. As we discussed in Chapter 8, you may have to do this frequently if you are conducting ethnographic research. Different Types of Qualitative Data Qualitative data include but are not limited to transcribed interviews, printed texts (e.g., archival records, diaries, letters, emails, speech scripts, and newspaper stories), images (e.g., photographs, magazine ads, children's drawings), video-recordings (e.g., TV show segments, documentary video footage, music videos), or your own field notes made from observations. You may consider them as two broad categories: texts and images. In-depth interviewing is perhaps the most widely used qualitative method. Transcribed interviews are treated as text data, similar to archival data or other document- type data. When your data are texts, you are likely to Interpreting The foundation of analyzing non-numeric data is finding 80% Page 211 of 267 • Location 5609 of 7225
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Research: Types of Data

Name of Student
Name of Instructor
Institution Affiliation
Date

Quantitative Data

COVID-19 and income inequality in OECD countries by John Wildman
1. What are quantitative data? Provide examples from the article.
Quantitative data refers to data that can be expressed numerically to measure research
variables (Wang & Park, 2016). In this case, each data set is related to a unique numerical value.
Data that is quantifiable, amenable, and subject to statistical manipulation qualifies as
quantitative data. Additionally, quantitative data is structured and suitable for analysis using
statistical methods or mathematical techniques. Quantitative data includes measurable elements
such as length, weight, height, revenues, etc. Examples of quantitative data collected and
analyzed in the research include; the number of COVID-19 deaths, population figures, GDP per
capita, age, and life expectancy. Researchers conducting quantitative research usually collect
quantitative data using surveys, questionnaires, or polling methods.
2. Why did this study require quantitative data?
The research aimed to measure how COVID-19 disease and income inequality in member
countries of Organization for Economic Cooperation and Development (OECD) are related. For
this research, the topic comprises two measurable variables that can be quantified numerically.
While COVID-19 cannot be quantified, the number of deaths from the disease can be quantified
in numbers. Similarly, aspects of income inequality such as GDP per capita are numerically
quantifiable. Therefore, the research needed quantitative data because the measured independent
and dependent variables exist in numerical values. The research data could only be meaningful if
analyzed statistically.
3. How did researchers obtain quantitative data? Provide specific examples from the
article.
The entire data for the research was obtained from credible secondary sources. The
secondary sources included government and institutions. To be specific, GDP per capita data was
obtained from World Bank. Data on COVID-19 deaths for OECD countries was obtained from
the European Center for Disease Prevention and Control (ECDC). Similarly, statistics including
age, health status, and average life expectancy were extracted from government sources of the
concerned countries. Meanwhile, the Gini coefficient figure used to measure income inequality
was also obtained from government statistics. Oxford COVID-19 Government Tracker provided
data about lockdowns and responses. The study confined data collection to wealthy OECD
countries with high educational standards and reliable data storage infrastructure to ensure the
sources were reliable.
4. What are some ways these researchers analyzed quantitative data?
The study combined two statistical analysis techniques to analyze the data collected. The
researcher used regression analysis to establish the relationship between COVID-19, GDP per
capita, and overall national wealth. Covid-19 outcomes were determined using two linear
regression models. The study then used Poisson regression to check the robustness of the results
obtained from linear regression. Results from the two statistical analysis methods were presented
using graphs and tables.

Qualitative Data

Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of
COVID-19 on Daily Life by Matthew T. Tull, Keith A. Edmonds, Kayla M. Scamaldo,
Julia R. Richmond, Jason P. Rose, Kim L. Gratz
1. What are qualitative data? Provide examples from the article.
Qualitative data refers to non-numerical data used by researchers to describe the
phenomena under study. Instead of quantifying variables, qualitative provides approximations
and characterizations. Examples of qualitative data would include; the color of vehicles in a
garage, smooth skin, or the cleanliness of a street. The descriptive nature of qualitative data
makes it difficult to measure and analyze them precisely. Qualitative data is usually presented in
narrative form using descriptive words. Researchers mostly use questionnaires, inter...


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