Running Head: WORKPLACE SAFETY RESEARCH 1 Workplace Safety Research Lina Namu BUS. 642 December 15, 2014 [no notes on this page] -1- WORKPLACE SAFETY RESEARCH 2 The research project is on the study of workplace safety. The concern about the topic is due to the issues that having surrounding the issue of health and safety in several organizations. There are several changes in the measures related to the workplace safety programs. Safety has now become an integral part of the organization and has been a major determinant in the company’s quality assurance systems. There has been pressure from different quarters on the establishment of the link between productivity and performance in organizations and the standard of health and safety employed in such organizations. This has been costs associated with these issues. The costs have either the direct or the indirect effect on the organization. These create the reason for conducting the research on safety in the workplace. 1 The management dilemma in this case is on how organization can be able to link production and quality with the quality of safety in the organization. The issue of safety in the workplace has become a common problem in all types of organization. The management has not been able to address the issue in fullness or even come up with procedures and policies that will ensure that they meet the threshold of improving the health and being able to relate it directly to 1. this Where are your section headings? These need to be in place so I can delineate that each part of the assignment was covered. If I have to go looking for such thing the grade is generally lower. [Jon Webber] quality and production. Research question 2 The research question in the research is: does the workplace safety affect the production and quality in the organization? The research will seek to answer the question in an elaborate 2. does Does the workplace ..... [Jon Webber] manner and ensure that the data gotten is relevant and convincing. Hypothesis 3 In the research project, there is a hypothesis that Poor workplace safety leads to low quality production. The low quality of the products on the other hand will therefore reduce the -2- 3. that State this stronger like: The hypothesis for this study is ... [Jon Webber] WORKPLACE SAFETY RESEARCH 3 revenue and incomes of the organization. This therefore lowers the employee likeliness to improve the quality in turn making it hard to replace the original revenues and profits. Literature review National Research Council (U.S.) and Institute of Medicine (U.S.) (2009) conducted a research to evaluate the occupational health and safety. The main aim was to come up with the ways and the researches that directed towards enhancing the health and safety surveillance in the organizations. The researchers look and evaluate different programs related to the health and safety. The authors give recommendation that can help organizations understand whether they 1. Authors Delete Authors here [Jon meet the threshold for safety programs. Webber] 1 2 Authors LaTourrette, Mendeloff and Rand Corporation looks at ways which organizations can increase safety in the workplace. Using the RAND center as the vocal point for the research, the authors place it clear that there is a relationship between improvement of understanding of the health and safety and the workers in any organization. The authors show that there are monetary benefits related to combining the two (2008). These two articles show the need to have further analysis of the effectiveness of the research in the connection of the health, safety and benefits to the organization. Ethical concerns Ethical issues in a research are the norms that help the researchers to coordinate actions or activities in the research to establish trust and discipline among the researchers. The research will have some ethical concerns that will govern how to engage in the business research. They are the ethical issues to govern the research. As a research, there appropriate to stand to the ethical norms in the research. -3- 2. looks looked [Jon Webber] WORKPLACE SAFETY RESEARCH 4 Some of the ethical concerns include fabrication of information. It is likely that during the research and data collection, some information found from different respondents or those who give the information may have some fabrication and may not be in line with the required information about the safety and health in workplace. Secondly, the information might include 1 some falsification with the lack of meeting the required standards used in representing the research. The falsified information might also be a misrepresentation of research data. The 1. some Support this information with sources, too [Jon Webber] research data is however supposed to promote the truth and avoid any error resulting from such unethical activities that the research comes across during the research. Responses coming from employees, the management as well as other stakeholders may not present the whole truth or falsification with the aim of hiding some information for not meeting certain required standards. The other concern will be on cooperation and coordination of the research process. In the research, one will have to deal with different people in different disciplines and institutions with the aim of getting to access different data from the institutions. Ethical standards will be essential in promoting the values in the collaborative work (Goodwin, 2011). Additionally, these would also be essential in promoting the trust among the members as well as accountability. In addition, in a collaborative work there will be the requirements of issues like the mutual respect among the members of the team as well as the fairness in dealing with each of the team or institutions. The issue of copyrights, authorship and the data hiring policies are some of the regulations and standards that the research will have to contend with especially on intellectual 2 property interests (Landrum 2014). The author will also have to deal with the federal ethical policies on research misconduct, conflict of interests as well as human rights and social responsibility. The researcher will have to show being responsible in accordance to the health -4- 2. author Researcher [Jon Webber] WORKPLACE SAFETY RESEARCH 5 and safety regulations and laws. Ethical lapses in research can significantly harm the rights of the researchers as well as those of the researchers and the public as a whole. 1 Some of the preliminary thoughts about the research design will give the do’s and the don’ts for the framework of the research. The design will therefore include choosing the topic as selected, preparation of the proposal including background and significance to give the background of topic as well as selecting the research design methods including the major experimental challenges (Bernard, 2011). Design will also include the discussion of expected 2 results in the context of the hypothesis. This will help to include any conceivable data to the research ensuring that the research is up to the standards. Not all methods may be suited to this research project. In approaching, the sampling data for this research will use the qualitative data analysis with the aim of bringing together 1. preliminary What will you use: quantitative or qualitative or both? This needs to be explained and detailed better. This and the next section are very much underdeveloped. [Jon Webber] 2. results Where are your section headings for the new material? These need to be in place so I can delineate that each part of the assignment was covered. If I have to go looking for such thing the grade is generally lower. [Jon Webber] 3 collections of statistics to help understanding the meanings, beliefs and experience as brought by the research. -5- 3. statistics This is too preliminary and does not really set out how you will sample. [Jon Webber] WORKPLACE SAFETY RESEARCH 6 1 References Bernard, H (2011). Research methods in anthropology: Qualitative and quantitative 1. References I do not see three scholarly sources here. Which ones do you think would be included in those categories? [Jon Webber] 2 approaches. Lanham, MD Goodwin, K. (2011). Designing for the Digital Age: How to Create Human-Centered Products 2. Lanham, There is missing information on this reference. [Jon Webber] 3 and Services. Chichester: John Wiley & Sons. 3. Chichester: State? [Jon Webber] Landrum, R. E. (2014). Research methods for business: Tools and applications. San Diego, CA: Bridgepoint Education, Inc. LaTourrette, T., Mendeloff, J. M., & Rand Corporation. (2008). Mandatory workplace safety and health programs: Implementation, effectiveness, and benefit-cost trade-offs. Santa Monica, CA National Research Council (U.S.). & Institute of Medicine (U.S.). (2009). Evaluating occupational health and safety research programs: Framework and next steps. Washington: National Academies Press. -6- 8 Effective Survey and Questionnaire Research PRNewsFoto/NADAguides Learning Objectives After reading this chapter, you should be able to: • Discuss the challenges faced when selecting individuals from the population to be studied, including the difference between probability and nonprobability sampling approaches. • Identify key research methods and compare and contrast different methodological approaches. • List the major approaches to survey research design. • Present the key issues regarding the analysis of survey data, including issues related to the types of possible errors, challenges in handling data, and consideration of different data analytic techniques. • Identify the tips provided about survey construction in order to create or edit surveys, attending to the details necessitated by open-ended and closed-ended survey items. • Describe the major types of scales used in business research. • Explain the role of pilot testing prior to launching into a business research project. 217 • Discuss the benefits and limitations to using surveys. Lan81479_08_c08_217-246.indd 217 5/22/14 2:24 PM Pre-Test Pre-Test 1. A researcher who stops people on the street to survey them is using a. probability sampling. b. nonprobability sampling. c. simple random sampling. d. stratified sampling. 2. Which two survey methods can have drawbacks in terms of coverage? a. in-person interviews and mailed surveys b. online surveys and mailed surveys c. in-person interviews and telephone interviews d. online surveys and telephone interviews 3. A survey that collects data at one point in time is known as a a. time series survey design. b. longitudinal study. c. mixed mode survey approach. d. cross-sectional survey. 4. Asking survey questions that are not specific enough is considered a form of measurement error. a. true b. false 5. Surveys are useful tools for understanding respondents’ skills. a. true b. false 6. A survey provides a statement and then asks respondents whether they completely disagree, generally disagree, generally agree, or completely agree with that statement. This is an example of a(n) a. Likert scale. b. dichotomous scale. c. Likert-type scale. d. semantic differential scale. 7. In pilot testing, researchers should check that their survey includes an appropriate vocabulary level, along with an adequate font size. a. true b. false 8. One advantage of surveys, compared to other research methods, is the greater control they offer over variables of interest. a. true b. false Lan81479_08_c08_217-246.indd 218 5/22/14 2:24 PM Sampling the Population Section 8.1 Answers 1. b. nonprobability sampling. The correct answer can be found in Section 8.1. 2. d. o  nline surveys and telephone interviews. The correct answer can be found in Section 8.2. 3. d. cross-sectional survey. The correct answer can be found in Section 8.3. 4. a. true. The correct answer can be found in Section 8.4. 5. b. false. The correct answer can be found in Section 8.5. 6. c. Likert-type scale. The correct answer can be found in Section 8.6. 7. a. true. The correct answer can be found in Section 8.7. 8. b. false. The correct answer can be found in Section 8.8. Introduction A utility infielder in baseball is a player who can play many different positions, which makes this player extremely versatile and valuable. In many ways, survey research methods may play the same role in the tool belt of the business researcher. The survey is a wonderful tool in many business situations, and it is versatile and adaptable. In fact, either conducting survey research or helping colleagues interpret survey research may be one of your most frequent forays into business research methods. Knowledge about survey research design and its many variations on a theme can be particularly helpful to business researchers for different reasons. First, from a general research perspective, the survey method is a popular approach to uncovering the attitudes, beliefs, opinions, or perceptions of multiple stakeholders in the business arena. Surveys can be particularly efficient in gathering large amounts of information from large numbers of individuals in order to draw general conclusions or formulate a snapshot of current conditions. However, even if one is not a producer of business research, businesspeople need to be savvy consumers of business research. As new research findings are made available in journals, magazines, in-service workshops, and national conferences, those in business need to understand the fundamental concepts of research in order to make learned decisions about the viability of research—that is, to what extent findings are to be believed and acted upon. Given the ubiquitous nature of survey research throughout the business world, a deeper understanding of survey methodology, sampling, survey scales, item construction, and online tools is essential. 8.1 Sampling the Population Why sample? If the goal is to understand how a population thinks, acts, feels, and so on, then why not study the entire population? First, researchers often do not have comprehensive lists of members of a population. Say, for example, that a researcher wanted to survey all customers who use iPhones. Is there a comprehensive list available? The telephone service providers might be a good start, but names and addresses are unlikely to be part of the public record. Additionally, some iPhone® users have switched to Android™, and some Android users have switched to iPhones. Lan81479_08_c08_217-246.indd 219 5/22/14 2:24 PM Sampling the Population Section 8.1 The ultimate goal of sampling the population is so a representative portion of the population can be studied. Thus, by studying the sample carefully and methodically, generalizations can be drawn about the variables or behaviors of interest in the greater population. Two major types of sampling approaches exist—probability sampling and nonprobability sampling. In probability sampling, potential survey participants have an equal chance of being selected; that is, their selection is based on a probability (such as 1 out of 100). In nonprobability sampling, there is no equal chance guaranteed. The research surveyor at the mall stopping shoppers is using nonprobability sampling because not all potential shoppers even visit the mall. There are other methodological issues as well. Because of the mathematics and probability behind sampling theory, very good samples can be drawn from populations with relatively small margins of error. It is possible to estimate the percentage of high school graduates, within +/–3 percentage points, in a small county of 25,000 adults by collecting 1,024 completes (completed surveys), and to measure the same among the U.S. population of more than 300 million by collecting 43 more surveys (Dillman, Smyth, and Christian, 2009). Sampling is efficient. Lastly, surveying an entire population might lead to a greater number of nonrespondents. Survey researchers become concerned about nonrespondents because if bias drives a person’s choice not to complete the survey, it may weaken the validity of the data (Dillman et al., 2009). Business researchers are better suited to select a sampling procedure that allows them to estimate any potential of sampling error in order to obtain a representative sample while minimizing bias and high nonresponse rates. Probability sampling strives to achieve each of those goals. Probability Sampling The overarching goal of probability sampling is that the sample drawn will be representative of the population if all members of that population have an equal probability of being selected for the sample. Often, one hears this stated as a nonzero probability (StatPac, 2009; StatTrek, 2009), meaning that there is a chance for every person to be selected, no matter how slim that chance might be. There are a variety of different approaches to probability sampling, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Simple Random Sampling The simple random sample is perhaps the purest form of sampling, and also probably one of the rarest techniques used. Suppose a researcher had a roster of the entire population available. He or she could assign random numbers to all possible participants and then use a random number table to select the sample. In this situation, everyone in the survey population has the same probability of being chosen (Edwards & Thomas, 1993). Systematic Random Sampling In a systematic random sample, every nth person from a list is selected (Edwards & Thomas, 1993). Let’s say that a company has 2,000 employees, and the researcher determines that 100 surveys should be completed. Each employee would have an equal chance of being selected to complete the survey; therefore, the probability of being selected is n/N (Lohr, 2008), or in this example, 100/2000, or 1 out of every 20 employees. So, every 20th Lan81479_08_c08_217-246.indd 220 5/22/14 2:24 PM Sampling the Population Section 8.1 employee would be selected. After determining a random starting point (let’s say #4, for example), every 20th employee on the roster is selected, meaning the 4th, 24th, 44th, 64th, 84th, 104th, 124th, and so forth (Chromy, 2006). Stratified Sampling Stratified sampling involves an approach where extra precautions are taken to ensure representativeness of the sample. Strata define groups of people who share at least one common characteristic that is relevant to the topic of the study (StatPac, 2009). The term “strata” is the plural of stratum; a study could have one stratum, or multiple strata. For example, a researcher might want to ensure that the selected sample is representative based on gender, so he or she would “stratify” on gender. If the researcher knows that 55% of the population is female and 45% of the population is male, then random sampling within a gender stratum is used to extract a sample that precisely matches the gender breakdown of the population. By using relevant strata, sometimes oversampling is used to decrease sampling error from relatively small groups; that is, researchers may choose to oversample from groups less likely to respond (Edwards & Thomas, 1993). In a survey of CEOs where CEO respondents of a particular manufacturing sector were desired, perhaps there is a very small percentage of female CEOs. If that were the case, a researcher might oversample female CEOs, in hopes that the resulting sample would be representative of the population. If the percentages in the population match the sample strata selected (as in the gender example), this is proportionate stratification; if oversampling is used, this practice is considered disproportionate stratification (Henry, 1990). Cluster Sampling When thinking about random sampling, researchers tend to think about how each person should have an equal chance of being selected or not selected for a sample. Rather than think about the individual person level, cluster sampling addresses how groups of people may or may not be selected for inclusion in a study. That is, individual clusters could be neighborhoods or businesses or schools or counties, and so on. Publishing companies often have representatives who work in a particular region of the country, and bookstores within a certain region could be considered a cluster. To determine if a new inventory control program is effective at controlling theft and ensuring accurate pricing at purchase, some clusters could be selected to implement the new inventory control program, and other (yet similar) clusters might serve as control conditions for comparison. In this case, individuals are not being tested, but clusters of stores within a particular region are being tested. Once the bookstores are assigned to a group or cluster, then the entire cluster is selected or not selected at random (Edwards & Thomas, 1993). The cluster sample technique is particularly useful when it is impossible or impractical to compile an exhaustive list of members comprising the target population (Babbie, 1973; Henry, 1990). Multistage Sampling Multistage sampling describes a process that follows after cluster sampling has been implemented. For instance, if you are collecting data from high school seniors, is every high school senior within the selected school/cluster surveyed, or is a systematic random sample drawn? In essence, the multistage sampling approach is two-stage sampling, involving (a) the selection Lan81479_08_c08_217-246.indd 221 5/22/14 2:24 PM Sampling the Population Section 8.1 of clusters as a primary selection, and (b) sampling members from the selected clusters to produce the final sample (Chromy, 2006; Henry, 1990). Nonprobability Sampling Nonprobability sampling means just that: It is unknown what the probability is of each possible participant in the population to be selected for the study. With nonprobability sampling, sampling error cannot be estimated (StatPac, 2009). Two key advantages to nonprobability sampling, however, are cost and convenience (StatTrek, 2009). The main approaches utilizing nonprobability sampling are convenience sampling, quota sampling, snowball sampling, and a volunteer sample. Convenience Sampling Convenience samples are just that—convenient. If a researcher were to email everyone on their contact list and ask survey questions, this would comprise a convenience sample. This technique is often used in exploratory research where a quick and inexpensive method is used to gather data (StatPac, 2009). Researchers have long relied on convenience samples to collect data. For example, if all customers who walk into a restaurant were asked to complete a “customer service card,” the individuals who visit the restaurant would be considered a convenience sample. Quota Sampling Quota sampling as a nonprobability sampling technique is the parallel equivalent of stratified sampling from the probability sampling world. In stratified sampling, one identifies characteristics of interest, and then the researcher strives to ensure that the individuals selected represent the population of interest in a proportional manner. In quota sampling, the researcher also desires the strata of interest but recruits individuals (nonrandomly) to participate in a study (StatPac, 2009). Thus, quotas are filled with respect to the key characteristics needed for survey participants from the population. Snowball Sampling When using the snowball sample technique, members of the target population of interest are asked to recruit other members of the same population to participate in the study. This procedure is often used when there is no roster of members in the population, and those members may be relatively inaccessible, such as illegal drug users, pedophiles, or members of a cult (Fife-Schaw, 2000). Snowball sampling relies on referrals, and may be a relatively low-cost sampling procedure (StatPac, 2009), but there is a high probability that the individuals who participate may not be representative of the larger population. Volunteer Sample Volunteering is commonly used for soliciting survey participation, but often the results are quite limited due to the possible motivational differences between volunteers and nonvolunteers. When a popular website posts a survey and invites volunteers to participate, the explanatory and predictive power of the data gathered may be suspect (StatTrek, 2009). It is Lan81479_08_c08_217-246.indd 222 5/22/14 2:24 PM Survey Research Methods Section 8.2 difficult to make confident generalizations from a sample to a population when nonprobability samples are employed, and even less confidence exists if a volunteer sample is utilized. With one piece of the survey puzzle in place (sampling), the next section presents the major survey research approaches or strategies that are commonly used. 8.2 Survey Research Methods Successful research is a multistep process. Even after choosing a research design (either experimental or quasi-experimental) and determining the sampling plan, there is still much to be done. The next step is to determine the specific methods by which data are collected. This section provides an overview about the choices that survey researchers must answer concerning this important piece. Interviews In some ways, in-person interviews remain the gold standard in survey research. Interviews have fewer limitations about the types and length of survey items to be asked, and trained interviewers can use visual aids during the interview, so that the interviewee can see, feel, or taste a product (Creative Research Systems, 2009; Frey & Oishi, 1995). Interviews are thought to be one of the best ways to obtain detailed information from survey participants. With an in-person interview, the interviewer and the participant can build rapport through conversation and eye contact, which might allow for deeper questions to be asked about the topic of interest. The drawbacks of interviewing include high costs and the reluctance of individuals to take the time to complete an interview (Creative Research Systems, 2009; Frey & Oishi, 1995). In addition to one-on-one interviews that may be prearranged, there are also intercept interviews, such as those seen at a mall where an interviewer intercepts shoppers and asks them questions. The level of intimacy that can be achieved with an in-person interview could also be a drawback for some people. For instance, introverts might be more willing to express their feelings within a group setting rather than during a one-on-one interview. There are also group interviews, or focus groups, where a group of people are interviewed at the same time. The reluctance to participate in in-person interviews led to the growth of using the telephone as a modality of conducting survey research (Tuckel & O’Neill, 2002). The use of telephone methodology has increased over time, but it faces a number of challenges today. For example, coverage has always been a concern of telephone research. That is, the greater percentage of homes with a telephone, the better the survey coverage, and the better the possibility of drawing a representative sample from the population of interest. Notice how telephone coverage in the United States has changed over time (Kempf & Remington, 2007): • • • • • Lan81479_08_c08_217-246.indd 223 In 1920, 65% of households did not have a telephone. In 1970, 10% of households did not have a telephone. In 1986, 7–8% of households did not have a telephone. In 2003, less than 5% of households did not have a telephone. In 2009, 4.7% of households did not have a telephone (Belinfante, 2010). 5/22/14 2:24 PM Survey Research Methods Section 8.2 As you can see, coverage is quite good with regards to households with a phone, but there are multiple challenges for researchers today, such as working within the context of Do Not Call lists. Additionally, the growth of cell phone usage is changing the face of telephone survey research. There are current laws that limit the solicitations made via cellular calls because some recipients of those calls must pay for each call received. Answering machines, caller ID, privacy managers, and call blocking services all add to the increasing challenges of conducting survey research by telephone. However, researchers continue to develop new strategies for improving the efficiency of telephone surveys, such as by using computer-assisted telephone interviewing (CATI) systems, random digit dialing (RDD), and interactive voice response systems (“Press 1 if you are . . . “). Mailed Surveys Mailed surveys remain a viable way of collecting data, and there are both advantages and disadvantages to using this mode. The advantages of mail surveys include (a) relatively low cost per survey respondent, as mailed surveys can be processed with a relatively small staff; (b) no time pressure for respondents; (c) the use of visual stimuli, including different scaling techniques and visual cues for survey completions; (d) the removal of the potential effect (bias) of the interviewer; (e) participants have greater privacy; and (f) if a good sample frame is available with a mailing list, the benefits of random sampling techniques can be utilized (Dillman et al., 2009). The potential disadvantages of mail surveys include (a) potentially low response rates; (b) limited capabilities for complex questions and the inability for an interviewer to clarify questions being answered; (c) when mail is delivered to a household, there is no guarantee that the person for whom the survey is intended is the person completing the survey; and (d) the turnaround time for receiving mailed survey responses can be long (de Leeuw & Hox, 2008). Online Surveys As a comparison to paper-and-pencil surveys, online surveys offer a number of advantages, including (a) easy and inexpensive distribution to large numbers of individuals via email, (b) the participant is guided through the survey by essentially filling out a form (i.e., skip patterns are hidden from view), and (c) digital resources (e.g., video clips, sound, animation) can be incorporated into the survey design, and questions can be “required” to be answered as well as verified instantly (e.g., when asked for a birth year, if something other than a four digit number is entered, the participant can be instantly prompted to use the correct format, and prevented from proceeding until making the correction) (Beidernikl & Kerschbaumer, 2007). There are a number of survey tools available to assist in the collection of data: Two of the more popular choices are SurveyMonkey (www.surveymonkey.com) and Qualtrics (www .qualtrics.com), although there are many others, including QuestionPro, Zoomerang, KeySurvey, SurveyGizmo, and SurveyMethods. Many of these websites will allow free account creation and use on a limited basis to design a survey and collect data with that survey. After creating the survey, the business researcher can use the software to create a custom URL that can be emailed to potential participants or posted on a website. One of the advantages to online survey software is that the results can usually be downloaded directly into an Excel™ file for later analysis (or other types of data files, such as SPSS® files). Also, some of the sites Lan81479_08_c08_217-246.indd 224 5/22/14 2:24 PM Survey Research Methods Section 8.2 can assist with rudimentary data analysis, as well as creating graphs and charts, without exporting the data. Two key drawbacks of online surveys are issues of coverage and nonresponse (de Leeuw & Hox, 2008). The issue of coverage, or who has Internet access and who does not, is sometimes referred to as the digital divide (Suarez-Balcazar, Balcazar, & Taylor-Ritzler, 2009). Some specific examples of the drawbacks of coverage include: (a) individuals from low-income and working-class communities are less likely to have access to the Internet; (b) low-income, working-class, culturally diverse individuals are more likely to have only one computer, which limits the potential for completing Internet-based surveys; (c) limited access often translates into limited familiarity with online applications; and (d) there may be cultural barriers that make Internet research more difficult to successfully accomplish (Suarez-Balcazar et al., 2009). With regard to those who do not answer the survey (called nonrespondents), this can be a tricky situation to address. It is difficult to know why a person does not respond to a survey request; given the nonresponse, it is doubly difficult to ask individuals why they did not respond. If the nonresponse rate is relatively low, then nonresponse is likely not to be a major issue; no large-scale survey garners 100% participation. However, if the nonresponse rate is 80%, then researchers need to wonder about and explore why only 20% of the sample is responding, and if there is something systematically happening that would inhibit possible participants from responding. In addition to coverage, there is also the challenge of representativeness, and an Internet survey approach may not achieve the level of representativeness desired (Beidernikl & Kerschbaumer, 2007; de Leeuw & Hox, 2008). In fact, one can think about whether those replying to an Internet survey are representative of the entire population, representative of the Internet population, or even representative of a certain targeted population (Beidernikl & Kerschbaumer, 2007). Add in the complexity of culture, and one can see that well-designed Internet surveys can take a significant amount of work. Consider this example—even though it is not a business example, it is an excellent example of the challenges of representativeness: For instance, in the Chicago Public Schools, students speak over 100 different languages and dialects. Social scientists planning studies in these types of settings must consider how they are going to communicate with the participants’ parents. Although children of first generation immigrants may be able to speak, read, and participate in Internet-based surveys in English, information such as consent forms and research protocols that are sent to the parents may need to be translated into their native language and administered using paper-and-pencil format. (Suarez-Balcazar et al., 2009, p. 99) If they do not use their tools carefully, online survey researchers are capable of invading privacy (by providing for anonymous responding via the Internet), and care should be taken to minimize the threat to invasion of privacy (Cho & LaRose, 1999). That is, if survey researchers promise anonymity and confidentiality, then those researcher promises must be upheld. Comparison of Methodologies With all the options of survey administration, the natural question arises: Which approach is best? The answer is that it depends. However, there have been studies conducted that compare the different methodologies. It has been found that, on average, web-based Lan81479_08_c08_217-246.indd 225 5/22/14 2:24 PM Survey Research Design Section 8.3 surveys have an 11% lower response rate as compared to mailed and telephone surveys (de Leeuw & Hox, 2008). In an experiment that directly compared regular mail and email surveys, researchers found comparable response rates—57.5% for regular mail, and 58.0% for email (Schaefer & Dillman, 1998). In comparing telephone surveys and web-based surveys, a two-wave web-based approach provided more reliable data estimates than telephone surveys, and at a lower cost—each telephone survey cost $22.75 to complete, whereas the cost of each web-panel survey was $6.50 (Braunsberger, Wybenga, & Gates, 2007). What does the future hold for preferred survey research modality? In addition to comparison studies, a growing trend is to utilize a mixed mode approach, where multiple modalities are accessed to achieve the research goals (Nicolle & Lou, 2008). Thus, participants may receive email reminders to participate in a telephone survey. The mixed mode approach can also utilize the collection of qualitative and quantitative data. Qualitative data, such as the responses to open-ended questions on a survey, can provide particularly rich and useful information and are often the most helpful when we know the least about a topic. If the sampling plan and survey modality are in place, another decision to be made is the overall design of the survey research. In some regard, these concepts overlap with topics from Chapter 7. However, a brief review of how these design decisions affect survey research is warranted here. 8.3 Survey Research Design Although different researchers may use slightly different terminology, the major categories of survey research designs, which include cross-sectional surveys (conducted at one point in time) and longitudinal surveys (conducted over time), are presented in this section. Cross-Sectional Survey In a cross-sectional survey design, data collection occurs at a single point in time with the population of interest (Fife-Schaw, 2000; Visser, Krosnick, & Lavrakas, 2000). One way to think about a cross-sectional survey is that it is a snapshot in time (Fink & Kosecoff, 1985). Regarding the benefits of cross-sectional surveys, they are relatively inexpensive and relatively easy to do (Fife-Schaw, 2000; Fink & Kosecoff, 1985). However, if the landscape changes rapidly, and the amount of change is important to a researcher’s study, then using a cross-sectional design will not capture this change over time (Fife-Schaw, 2000; Fink & Kosecoff, 1985). Longitudinal Survey A longitudinal survey is conducted across time. The key advantage of longitudinal designs is that they allow for the study of age-related development. However, this can be confounded with events during a lifetime that might influence your variables (Fife-Schaw, 2000). Longitudinal studies also face unique challenges, such as keeping track of respondents over time and deciding how to motivate them to continue to respond in the future (Dillman et al., 2009). Attrition (dropping out of the study over time) is a drawback, and participants repeatedly tested can be susceptible to the demand characteristics of the research. Having participated Lan81479_08_c08_217-246.indd 226 5/22/14 2:24 PM Analysis of Survey Data Section 8.4 multiple times in the past, the participants know what is expected and probably understand the variables and general hypotheses being tested (Fife-Schaw, 2000). In a cohort study, new samples of individuals are followed over time (Jackson & Antonucci, 1994). In a panel study, the same people are studied across time, spanning at least two points in time (Fink & Kosecoff, 1985; Jackson & Antonucci, 1994; Visser et al., 2000). This type of study can be particularly useful in understanding why particular changes occur longitudinally because researchers ask the same participants to respond over and over (there is also a baseline comparison measure from when they first entered the study). 8.4 Analysis of Survey Data In most respects, analyzing survey data is the same as analyzing any other type of data; analysis choices are based on the hypotheses, scales of measurement, tools available for data analysis, and so on. Before mentioning specific approaches for data analysis, let’s review the types of errors that are encountered in survey research. Remember that “errors” in this context are not mistakes but are the possible outcomes of the study that researchers cannot account for, or the changes or values of the dependent variable that are not due to the independent variables being manipulated, controlled, or arranged. Types of Errors In classic measurement theory, the total amount of error is assumed to be the sum of measurement error + sampling error (Dutka & Frankel, 1993). Those who study survey research design further categorize the types of threats and errors that can occur with this type of research. The following is a four-cornerstone model of surveying and errors that is useful here for greater understanding (Dillman et al., 2009). Coverage Error A coverage error in survey research refers to the methodology used. For example, if an Internet approach is used, only about 70% of households have Internet access, so coverage error exists (Dillman et al., 2009). The coverage error is much smaller with telephone surveys, but the proportion of individuals with landlines is decreasing, while cell phone subscribers are increasing (Kempf & Remington, 2007). There are laws that govern researchers’ calling respondents’ cell phones, because that could can incur the recipient additional costs. Survey researchers need to be cognizant of coverage error concerns when making methodological choices. Sampling Error A sampling error occurs when not all of the potential participants from a population are represented in a sample, which is often due to the sampling method utilized by the researcher (Dutka & Frankel, 1993; Futrell, 1994). Another related sampling issue is volunteerism, or self-selection. When a study relies on volunteers, there is always a concern that volunteers may behave differently than nonvolunteers; if this is the case, it weakens the generalizability of the survey results. Lan81479_08_c08_217-246.indd 227 5/22/14 2:24 PM Analysis of Survey Data Section 8.4 In fact, volunteers often differ from nonvolunteers in the following ways: (a) volunteers are more educated than nonvolunteers; (b) volunteers are from a higher social class than nonvolunteers; (c) volunteers are more intelligent than nonvolunteers; (d) volunteers are more approval motivated than nonvolunteers; and (e) volunteers are more sociable than nonvolunteers (Rosenthal & Rosnow, 1975). However, if the only way you can conduct your research is by using volunteers, then that is what you do. But it is important to remember these caveats when drawing conclusions from research that depends exclusively on volunteer participants. If you only ask current iPad users about their opinion about the latest iPad version released, these eager volunteers may respond differently than nonvolunteers/nonusers. Measurement Error Measurement error can occur due to a number of reasons, but they tend to fall into the category of measurement variation (the lack of a reliable instrument) and measurement bias (asking the wrong questions or using the results inappropriately) (Dutka & Frankel, 1993). As in any complex enterprise, the potential for mistakes can be high. Some common measurement errors that can occur in survey research are: (1) failing to assess the reliability of the survey; (2) ignoring the subjectivity of participant responses in survey research; (3) asking nonspecific survey questions; (4) failing to ask enough questions to capture the behavior, opinion, or attitude of interest; (5) utilizing incorrect or incomplete data analysis methods; and (6) drawing generalizations that are not supported by the data nor the data analysis strategy selected (Futrell, 1994). Essentially, measurement errors address issues of (a) did we measure what we thought we measured, and (b) did we interpret the results appropriately? Nonresponse Error Nonresponse error is of particular concern in survey research (Dillman et al., 2009). As a general rule, if the response rate is 25% or less (or a nonresponse rate of 75% or more), the survey researcher should ask the question: Are those responding to my survey different from those not responding to my survey (Dillman et al., 2009)? There are many approaches for dealing with high nonresponse rates, and some of those methods involve weighting the responses that are received, as well as following up with a subset of nonresponders and asking why they did not respond (Dale, 2006). The goal here is to determine that there was no systematic bias in responses or nonresponses to the initial survey request. If there is no bias (or no systematic reason driving nonresponse), then the nonresponse rate is less of a concern to the survey researcher. Data Handling Challenges The details and complexity of data handling issues within survey research are beyond the scope of this chapter, but two issues are worth mentioning. After collecting data, but prior to analysis, there must be some data cleaning (sometimes called data editing). Although every survey researcher must do this, there are not commonly accepted standards for data cleaning (Leahey, Entwisle, & Einaudi, 2003). Sometimes this process involves the elimination of outliers, but other times data decisions are more complex. Lan81479_08_c08_217-246.indd 228 5/22/14 2:24 PM Tips for Effective Survey Item Construction Section 8.5 Let’s say a survey included items with a Likert-type agreement scale, with 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. When the respondent answered the question “I am comfortable with the undergraduate major I have selected,” the coded answer was 55. What do you do? Do you assume they meant a 5 (strongly agree), and change their response? Is it possible to go back and confirm what the participant meant, or was the data collected anonymously? You could guess that a 55 meant a 5, but what about a “23” entry—should this be a 2 (disagree) or 3 (neutral)? Here’s one more: In an online survey, where respondents directly enter their age, a participant enters the value “1.9”—should that be recoded as 19 years old, or should that data be deleted? These data cleaning issues are related to how survey researchers handle missing data, which can be done using number of complex approaches (Dale, 2006; Graham, Taylor, Olchowski, & Cumsille, 2006; Rudas, 2005). It is best to make decisions about data cleaning before you have the data, such as (a) discarding data that has a high probability of being incorrect, or (b) seeking out the original, correct data when the data presented are suspect. If you cannot confirm what a participant meant by his or her response, delete it. As you become savvier in performing data cleaning and missing data analyses, you can alter this conservative approach. Furthermore, if you collect your survey data anonymously, you have no method of contacting individuals to clarify their intended response. If you must err, err on the side of caution. Data Analytic Approaches As alluded to earlier, the possibilities for analyzing survey data are vast and depend on many of the same characteristics as other data analysis situations, such as the scale of measurement, the amount of data available, and the hypotheses to be tested. It would not be possible to summarize all of the options here, as entire books are available about the subject (Fink, 1995). See Chapter 12 for a brief overview of the various data analysis options available to researchers. Data analytic strategies can become more or less complicated, however. If your goal is to communicate effectively with the public, you might not choose to present the results of a repeated measures ANOVA, but you might present a table of means, or a bar graph that clearly and succinctly communicates the story you want to tell. If you compare two nominal scale variables, such as gender differences regarding how respondents answered a categorical survey item (“Are you married?”), then a chi-square analysis would be appropriate. Essentially, you will need the knowledge that you learn from a statistics course in order to analyze your survey data. 8.5 Tips for Effective Survey Item Construction Before researchers begin generating their pool of survey questions, it is beneficial to think first about what they are trying to measure—that broad category of human response they are trying to capture. Consider these categories: (a) attitudes, beliefs, intentions, goals, aspirations; (b) knowledge, or perceptions of knowledge; (c) cognitions; (d) emotions; (e) behaviors and practices; (f) skills, or perceptions of skills; and (g) demographics (eSurveyPro, 2009; Rattray & Jones, 2007). Lan81479_08_c08_217-246.indd 229 5/22/14 2:24 PM Tips for Effective Survey Item Construction Section 8.5 Making decisions about which broad category (or categories) to inquire about has implications for the entire survey. For example, if you ask too many difficult knowledge questions, respondents may quit your survey early, not providing you with the data you need. Actual skills may be difficult to capture in a survey format, but you may be able to ask respondents about their perceptions of their own skills. Demographics can be tricky as well. Ask about too many demographics, and participants may feel a sense of intrusion, and the more questions asked, the more identifiable a participant is, even if the data are collected anonymously. Ask about too few demographics, and you may not be able to provide tentative answers to your hypotheses. As you have the opportunity to practice your survey skills over time, you should become more comfortable in being able to assess these broad areas. The following feature provides some helpful advice for constructing survey items. Tips for Survey Item Construction 1. Avoid double-barreled items. That is, each question should contain just one thought. A tipoff to this occurring is sometimes the use of the word “and” in a survey item. Example to avoid: I like iPhones and Androids. 2. Avoid using double negatives. Example to avoid: Should the supervisor not schedule an annual evaluation the same week as the quarterly reports are due? (Answered from Strongly disagree to Strongly agree). 3. Try to avoid using implicit negatives—that is, using words like control, restrict, forbid, ban, outlaw, restrain, or oppose. Examples to avoid: Anonymous Internet use should be banned. All grey market sales should be outlawed. 4. Consider offering a “no opinion” or “don’t know” option. 5. To measure intensity, consider omitting the middle alternative. Example: Strongly disagree, disagree, neutral, agree, and strongly agree. 6. Make sure that each item is meaningful to the respondents completing the survey. That is, are the respondents competent enough to provide meaningful responses? Example to avoid: Windows 8 provides the best infrastructure for coding web-based programs. 7. Use simple language (standard English as appropriate) and avoid unfamiliar or difficult words. Depending on the sample, aim for an 8th-grade reading level. Example to avoid: How ingenuous are you when the marketing manager asks if you have understood the material presented during the sales briefing? 8. Avoid biased questions, words, and phrases. Example to avoid: Using clickers represents state-of-the-art survey technology. To what extent have clickers enhanced your survey participation? 9. Ensure your own biases are not represented in your survey items, such as leading questions. Example to avoid: Do you think gas-guzzling SUVs are healthy for the environment? (continued) Lan81479_08_c08_217-246.indd 230 5/22/14 2:24 PM Tips for Effective Survey Item Construction Section 8.5 Tips for Survey Item Construction (continued) 10. Do not get more personal than you need to be to adequately address your hypotheses. Focus on “need to know” items and not “nice to know” items (helps control for survey length). 11. Try to be as concrete as possible; items should be clear and free from ambiguity. Avoid using acronyms or abbreviations that are not widely understood. Example to avoid: The ROI from NGOs is not relevant because of the nonprofit status of 501c3s. 12. Start the survey with clear instructions, and make sure the first few questions are nonthreatening. Typically, present demographic questions at the end of the survey. If you ask too many demographic items, respondents may be concerned that their responses are not truly anonymous. 13. If the response scales change within a survey, include brief instructions about this so that respondents will be more likely to notice the change. 14. If your survey is long, be sure to put the most important questions first—in a long survey, respondents may become fatigued or bored by the end. 15. Frame questions in such a way as to minimize response set acquiescence. Ask questions that are reverse scored (that is, strongly disagreeing is a positive outcome). Example: This sales training seminar is a waste of time. A positive answer would be strongly disagree. Sources: Babbie (1973), Cardinal (2002), Converse & Presser (1986), Crawford & Christensen (1995), Edwards & Thomas (1993), eSurveyPro (2009), Fink & Kosecoff (1985), HR-Survey (2008), Jackson (1970), McGreevy (2008), and University of Texas at Austin (2007). Steps in the Survey Design Process If you are in a position to create your own survey items, understand that constructing good survey items is difficult and takes practice. Fortunately, there is much advice available on how to construct survey items. Although the details of the process may vary, in general these are the steps in a typical survey design project (DeVellis, 1991): • • • Lan81479_08_c08_217-246.indd 231 Determine what you want to measure. This task may be more difficult than it originally appears. Think about how you will operationalize the business constructs you are interested in. If you are operating from a theoretical perspective, that theory might provide some insight into clarifying what you want to know. Generate clear, testable hypotheses. Once you have determined what is to be measured, generate a potential item pool, selecting only items that will answer your specific hypotheses. If the project allows, redundancy is good. If you can ask about your topic from a variety of perspectives, do so; your analyses may reveal if one approach is preferable. Decide on the survey design and survey modality, as well as response categories (scales to be used), data analysis choices, and availability of statistical packages. Planning on the front end of the survey process will help to yield useable results when your data collection is complete. 5/22/14 2:24 PM Section 8.5 Tips for Effective Survey Item Construction • • • • Whenever possible, ask experts in the field to review your initial item pool; they may notice subtle nuances in item wording (or identify gaps in the design of the survey). If you are developing a survey about attitudes or opinions, consider including other measures that would help to validate your new items, such as a social desirability scale to help ensure your participants are answering honestly (Marlow & Crowne, 1961). Pilot test (pretest) your sample survey. Evaluate the item performance from the pilot test, including initial reliability estimates if possible, and optimize scale length. Ask enough survey items to measure what you want to measure, but not so many items that potential respondents will be unwilling to complete the survey. One of the earliest decisions that should be made in the survey design process, even before you start to generate survey items with potential response scales, is if response scales will be included; that is, will survey items be open ended or closed ended? Open-Ended Versus Closed-Ended Survey Items Although open-ended survey items are typically more difficult for drawing conclusions, these items can be effective depending on the type of information that you want to know. What is an open-ended survey question? It is a survey question that requires a written or verbal response, such as “How do you feel about parking at your workplace?” or “In your own words, what are the three most important skills and abilities you need to succeed in our organization?” Open-ended questions are helpful when answers may be difficult to anticipate, and the researcher is interested in how the participant sees the world (Fink, 1995). These results can be labor intensive to interpret, but they can provide quotable material, which may help tell a better story when presenting research at a conference or preparing a manuscript for publication. The opposite of open-ended survey items are closed-ended survey items, which means that the survey item is presented, and the possible responses are “closed”; that is, the possible options are presented to the participant. So the survey respondent checks a box (male or female) or perhaps provides an answer along a continuum (strongly disagree to strongly agree). In general, it is easier to report statistical outcomes from quantitative (closed-ended) survey data than from qualitative (open-ended) survey data, although it is possible to treat open-ended survey data quantitatively (for instance, in content analysis). Table 8.1 is helpful for determining when to use a closed-ended or open-ended survey item. Table 8.1: Decision table on when to use open-ended versus closed-ended questions Purpose Lan81479_08_c08_217-246.indd 232 If yes, use OPEN. If yes, use CLOSED. Respondents’ own words are essential (to please respondent, to obtain quotes, to obtain testimony). You want data that are rated or ranked (on a scale of very poor to very good, for example), and you have a good idea of how to order the ratings in advance. (continued) 5/22/14 2:24 PM Section 8.6 Scaling Methods Table 8.1: Decision table on when to use open-ended versus closed-ended questions (continued) Respondents’ Characteristics Asking the Question Analyzing the Results Reporting the Results Source: Fink (1995). If yes, use OPEN. If yes, use CLOSED. Respondents are capable of providing answers in their own words. Respondents are willing to provide answers in their own words. You want respondents to answer using a prespecified set of response choices. You have the skills to analyze respondents’ comments even though answers may vary considerably. You can handle responses that appear infrequently. You prefer to count the number of choices and responses. You prefer to ask only the open question because the choices are unknown. You prefer that responses conform to expected response choices. You will provide individual or grouped verbal responses. You will report statistical data. 8.6 Scaling Methods Perhaps one of the most complicated parts of survey research is deciding on the scale by which to measure a person’s attitudes, opinions, behavior, or knowledge. Researchers rely on best practices and established research that guides the decision making necessary to select an appropriate scale. The following is a brief overview of the major types of scales you are likely to use. Dichotomous Scale A dichotomous scale includes two possible options. If the possible options are agree/disagree, yes/no, true/false, male/female, and so on, then you are using a dichotomous (binary) scale. Respondents provide nominal scale data. Some examples of dichotomous scales where a yes/no type of response would be adequate are: • • • I work at a Fortune 500 company. I download music illegally. I have a 401(k). Some argue that single yes/no questions are insufficient because they are not sensitive to subtle change over time, they dictate that individuals place themselves into large categories, and that many phenomena are so complex that a singular yes/no response may fail to capture the complexity (Spector, 1992). As you design surveys, keep in mind that the hypotheses you wish to test will help to inform you if a dichotomous scale can yield the type of information you seek. Lan81479_08_c08_217-246.indd 233 5/22/14 2:24 PM Section 8.6 Scaling Methods Likert-Type Scales Likert scales, or more properly Likert-type scales, may be the most famous type of scale used by researchers today. The Likert scale is named after the psychologist from the University of Michigan, Rensis Likert (pronounced Lick-ert). Likert’s seminal work (1932), now called a Likert scale, called for a survey response scale to have a five-point scale, measuring from one pole of disagreement to the other pole of agreement. Each of the scale points has a specific verbal description (Wuensch, 2005b). A declarative statement is made, and then the respondent selects the appropriate answer. The low value is strongly disagree, and the high value is strongly agree: 1 = strongly disagree 2 = disagree 3 = neutral (neither agree nor disagree) 4 = agree 5 = strongly agree There have been many variations and changes suggested, which are loosely based on these criteria, so you will often see “Likert-type” scale used rather than the very specific Likert scale. For example, it has been argued that Likert-type variations might be better suited because they have lesser emotional ties: 4 = completely agree, 3 = generally agree, 2 = generally disagree, and 1 = completely disagree; or 4 = completely true, 3 = mostly true, 2 = mostly untrue, and 1 = completely untrue (Fowler, 1988). Of course, these would not conform to the true Likert scale but would be categorized as Likert-type scales. There have been many variations on this theme. Table 8.2 demonstrates some of these variations. Note the varying types of response anchors possible with a Likert-type scale approach. As you think about the type of scale you might employ in your survey research, you should begin to appreciate how versatile a Likert-type scale can be. Table 8.2: Variations on the Likert-type approaches to scaling Level of Importance Level of Agreement 1 – Not at all important 1 – Strongly disagree 3 – Slightly important 3 – Somewhat disagree 2 – Low importance 4 – Neutral 5 – Moderately important 6 – Very important 7 – Extremely important Lan81479_08_c08_217-246.indd 234 2 – Disagree 4 – Neither agree nor disagree 5 – Somewhat agree 6 – Agree 7 – Strongly agree (continued) 5/22/14 2:24 PM Section 8.6 Scaling Methods Table 8.2: Variations on the Likert-type approaches to scaling (continued) Knowledge of Action Effect on X 1 – Never true 1 – No effect 3 – Sometimes but infrequently true 3 – Neutral 2 – Rarely true 4 – Neutral 5 – Sometimes true 6 – Usually true 2 – Minor effect 4 – Moderate effect 5 – Major effect 7 – Always true Frequency Amount of Use 1 – Never 1 – Never use 3 – Sometimes 3 – Occasionally/sometimes use 2 – Rarely 4 – Often 5 – Always 2 – Almost never use 4 – Use almost every time Level of Difficulty 5 – Frequently use 1 – Very difficult 1 – Extremely unlikely 3 – Neutral 3 – Neutral 2 – Difficult 4 – Easy 5 – Very easy Likelihood 2 – Unlikely 4 – Likely Level of Satisfaction 5 – Extremely likely 1 – Completely dissatisfied 1 – Poor 3 – Somewhat dissatisfied 3 – Good 2 – Mostly dissatisfied 4 – Neither satisfied nor dissatisfied 5 – Somewhat satisfied 6 – Mostly satisfied Level of Quality 2 – Fair 4 – Very good 5 – Excellent 7 – Completely satisfied Source: Vagias (2006) Lan81479_08_c08_217-246.indd 235 5/22/14 2:24 PM Scaling Methods Section 8.6 Semantic Differential Scale The semantic differential scale technique, developed by Osgood in the 1950s, is a scale that is designed to measure affect or emotion (Henerson, Morris, & Fitz-Gibbon, 1987). With adjectives that are opposites on the polar ends of a continuum, participants are asked to select where they “feel” they are with respect to the survey topic. For example, if you were asked, “Thinking about this course, how do you feel about the grading policies being used?” the semantic differential scale items presented would request that you place a checkmark on one of the seven lines spanning between the polar opposites. fair ___ ___ ___ ___ ___ ___ ___ unfair unreliable ___ ___ ___ ___ ___ ___ ___ reliable confusing ___ ___ ___ ___ ___ ___ ___ clear helpful ___ ___ ___ ___ ___ ___ ___ not helpful good ___ ___ ___ ___ ___ ___ ___ bad Based on prior research, three types of findings tend to emerge from the use of semantic differential scales: an evaluative factor (good–bad), an intensity/potency factor (strong–weak), and an activity factor (slow–fast) (Page-Bucci, 2003). The semantic differential scale is good at capturing feelings and emotions, is relatively simple to construct, and is relatively easy for participants to answer, but the resulting analyses can be complicated (Page-Bucci, 2003). The following are examples of more possible pairings (Henerson et al., 1987): angry–calm bad–good biased–objective boring–interesting closed–open cold–warm confusing–clear dirty–clean dull–lively dull–sharp irrelevant–relevant last–first not brave–brave old–new passive–active purposeless–purposeful Lan81479_08_c08_217-246.indd 236 5/22/14 2:24 PM Scaling Methods Section 8.6 sad–funny slow–fast sour–sweet static–dynamic superficial–profound tense–relaxed ugly–pretty unfair–fair unfriendly–friendly unhappy–happy unhealthy–healthy uninformative–informative useless–useful weak–strong worthless–valuable wrong–right Visual Analog Scales There are many more types of scales that are used in survey research. Visual analog scales can be used to obtain a score along a continuum, where a participant places a checkmark to indicate where their opinion falls along the scale. The following is an example of the visual analog scale: No pain at all____________________________________ The worst pain I ever experienced This would be an example of a subjective continuum scale, where a checkmark is made along the scale to indicate how positive or negative a respondent’s opinion is about a particular topic. Very positive____________________________________ Very negative With the advent of online survey packages, the visual analog scale has become digital. In the online survey software package Qualtrics, visual analog scales are presented as “sliders,” and respondents can click on the pointer and slide it to location along the continuum that represents their belief. See Figure 8.1 for an example of a series of slider questions. Lan81479_08_c08_217-246.indd 237 5/22/14 2:24 PM Scaling Methods Section 8.6 Figure 8.1: Example of visual analog survey items using sliders Case Study: Understanding the Differences Between Polls and Scientific Surveys Like a survey, a poll is designed to elicit information from respondents, and in common parlance, the two terms are often used interchangeably. But there are key differences. While a poll must also be carefully designed, unlike a scientific survey, it uses a series of single-item questions to ask respondents their opinions on a particular issue. There are many types of polls, but the one most commonly used is an opinion poll. The goal of an opinion poll is to elicit information from the public, but then to disseminate the results back to the population at large to keep them informed. Opinion polls are used for both very serious and sometime frivolous reasons, such as determining whether the president of the United States is doing a good job, whether Americans trust the media, or who is America’s favorite movie star. George Gallup developed one of the best-known polls in 1935. Gallup, Inc. conducts surveys in 160 countries using nationally representative samples and focuses on four broad categories— Politics, the Economy, Well-Being, and the World. In the United States, the Gallup poll is conducted over the telephone and targets adults aged 18 or older. Callers are selected using proportionate, stratified random sampling. The typical sample size for each poll is 1,000 national adults. As an example, every year in the United States, Gallup asks people to rate the honesty and ethical standards of people in a wide variety of professions. As is the case in the vast majority of polls, a single question with a simple rating scale is used. The rating scale consists of six choices ranging from “Very high,” “High,” “Average,” “Low,” “Very low,” to “No opinion.” Gallup adds up the percentage of responses in both the “Very high” and “High” categories to determine which profession had the highest ratings for honesty and ethical standards. (continued) Lan81479_08_c08_217-246.indd 238 5/22/14 2:24 PM The Role of Pilot Testing Section 8.7 Case Study: Understanding the Differences Between Polls and Scientific Surveys (continued) Below are some scores from the 22 professions rated in 2012: • • • • • • • • • • • • • • Nurses—85% Medical doctors—70% Police officers—58% College teachers—53% Clergy—52% Bankers—28% Business executives—21% Lawyers—19% Insurance salespeople—15% Senators—14% Advertising practitioners—11% Stockbrokers—11% Members of Congress—10% Car salespeople—8% • • • • • to see if the candidate presents himself/herself professionally—65% to see if the candidate is a good fit for the company culture—51% to learn more about the candidate’s qualifications—45% to see if the candidate is well rounded—35% to look for reasons not to hire the candidate—12% As another example, a recent poll of over 2,300 hiring managers and human resource professionals conducted by Careerbuilder.com found that 37% of companies use social media to gather information about potential employees. One of the key questions in the poll was, “What are hiring managers looking for on social media?” The responses were as follows: While polls are not developed with the same purposes in mind as a scientific survey, it is clear that they provide a great deal of useful and reliable information on a wide variety of topics and that they can gather data in an effective and efficient manner. Sources: Honesty/ethics in professions. Gallup. Retrieved from http://www.gallup.com/poll/1654/honesty-ethics-professions.aspx Thirty-seven percent of companies use social networks to research potential job candidates, according to new CareerBuilder survey. CareerBuilder. Retrieved from http://www.careerbuilder.com/share/aboutus/pressreleasesdetail.aspx?id=pr691&sd=4%2F 18%2F2012&ed=4%2F18%2F2099 8.7 The Role of Pilot Testing Think of a pilot test or pretest as a dress rehearsal prior to conducting a study. It is wise to pilot test because in measuring human behavior, elements of an experiment can go wrong if details are not attended to. For example, in survey research, a pilot test can help to determine if participants understand the survey questions and if the topic is being covered as expected, as well as helping to make sure that participants understand the context of the survey question (Collins, 2003). There are typically four goals to achieve when pilot testing a survey. The Lan81479_08_c08_217-246.indd 239 5/22/14 2:24 PM Benefits and Limitations of Surveys Section 8.8 survey researchers want to (a) evaluate the draft survey items; (b) optimize the length of the scale for adequate response rate; (c) detect any weaknesses in the survey; and (d) attempt to duplicate the conditions under which the survey will be administered. Think of the different pilot tests that car manufacturers use with crash-test dummies prior to the introduction of a new car model. The pilot testing procedure exists as a method of identifying weaknesses in the planned approach so that modifications and tweaks can be made before the survey (or car design) is finalized. When designing survey research, researchers should ensure that respondents (a) know the answers, (b) can recall the answers, (c) understand the questions, and (d) are comfortable reporting the answers in the survey context (Henry, 1990). For instance, survey items should appear at a reading level that is appropriate for the age and educational level of the participants being studied. Additionally, when collecting data with a Likert-type scale, the survey items should be declarative sentences and should not phrased in the form of a question. By assuring participants that their data are anonymous, a researcher encourages honesty in the participants by not linking their identity to responses about sensitive topics or illegal behaviors. Pilot testing allows you to find most problems that may occur in your study before conducting your study (much like proofreading an important email prior to sending it to the management team at a company). The following are just some quick pilot test reminders to consider before conducting survey research (Litwin, 1995): • • • • • • • • • • • • • Are there any typographical errors? Are there any misspelled words? Does the item numbering make sense? Is the font size big enough to be easily read (on paper; on the screen)? Is the vocabulary appropriate for the respondents? Is the survey too long? Is the style of the items monotonous? Are there easy questions mixed in with the difficult questions? Are the skip patterns difficult to follow? Does the survey format flow? Are the items appropriate for the respondents? Are the items sensitive to possible cultural barriers? Is the survey in the best language for the respondents? If survey research may be in your future, this list may be helpful as a checklist when preparing the survey for pilot testing or preparing the survey for distribution to the intended sample. 8.8 Benefits and Limitations of Surveys No research technique is perfect for all situations, and every approach will have advantages and disadvantages, depending on the context. Thus, a survey may not always be advisable when a business researcher wants to know more about a specific topic. Specific instances when a survey would not be advised include (a) in a labor situation, either before or during a strike; (b) times when there is an increased level of strife in an organization and there is Lan81479_08_c08_217-246.indd 240 5/22/14 2:24 PM Summary & Resources high risk that the survey process could be mismanaged; (c) when there is a curiosity about the topic but no commitment to act on the survey results; (d) when the survey outcomes will be used in a deceptive manner (survey not actually administered for the stated purpose); and (e) if an intense understanding of a complex topic is desired, a survey might not yield those results (Schiemann, 1991). As always, understanding the available literature and gaining experience over time will help the business researcher determine if the potential rewards from conducting a survey outweigh the risks such that the survey research project is worth doing. Some of the limitations and risks of the survey research approach include (a) a lack of control over variables of interest; (b) a low response rate may be problematic; (c) an ambiguous survey may lead to difficulties in interpretation; (d) in some contexts, participants may not believe their data are truly anonymous and confidential; (e) the possibilities of bias can be present if nonresponse rates are high or if socially desirable responding is occurring; and (f) a survey research approach almost never allows for cause-and-effect conclusions (Fowler, 1998; Seashore, 1987). Surveys do have advantages though, such as allowing for anonymity of responses and statistical analysis of large amounts of data; they can also be relatively cost effective, sampling mechanisms can be carefully controlled, and by using standardized questions, change can be detected over time (Seashore, 1987). Surveys are pervasive throughout culture. The ability to properly design a survey and interpret its results is a skill that well suits business researchers for a future in the workplace. But it is important to remember that surveys are a measure of self-report and not actual behavior. There are multiple reasons why survey data may be inaccurate; it could be that the respondents do not know the answer, they know the answer but cannot recall the answer, they do not understand the question (but answer anyway), or they just choose not to answer (Fowler, 1998). Because most survey research does not share the same characteristics as experimental designs, it is important not to overinterpret the results of survey research—the survey approach is powerful in helping researchers identify the relationships between variables and differences amongst groups of people, but the results are only as good as the design quality that is necessary for this complex task. Summary & Resources Summary • • • Lan81479_08_c08_217-246.indd 241 As with most concepts in business research, the complexity of the topic can range from easy to difficult, and survey research (the design, data collection, and data analyses) can range from simple to complex. With the acquisition of any skills, practice over time will help to develop and internalize the expertise needed to design survey research to meet the needs of business clients, both within your organization and for external clients. In some ways, survey construction and design can be part art and part science, and it seems that the outcomes of survey data are everywhere. 5/22/14 2:24 PM Summary & Resources • • Pilot testing a survey with a small number of trusted individuals can aid in the debugging process, and it is key to remember that the survey approach is not a one-size-fits-all approach. As with any research approach, its strengths and weaknesses must be carefully considered prior to embarking on a major research project that commits extensive time, resources, and expertise to make sense of the data collected. Post-Test 1. A researcher who stops people on the street to survey them is using a. probability sampling. b. nonprobability sampling. c. simple random sampling. d. stratified sampling. 2. In which method of sampling are participants recruited by other survey participants and therefore unlikely to represent the larger population? a. snowball sampling b. convenience sampling c. quota sampling d. cluster sampling 3. Which two survey methods can have drawbacks in terms of coverage? a. in-person interviews and mailed surveys b. online surveys and mailed surveys c. in-person interviews and telephone interviews d. online surveys and telephone interviews 4. A company emails customers a reminder to complete and send back a survey that was mailed to their home. This is an example of a. representativeness. b. snowball sampling. c. a mixed mode approach. d. comparison of methodologies. 5. A survey that collects data at one point in time is known as a a. time series survey design. b. longitudinal study. c. mixed mode survey approach. d. cross-sectional survey. 6. A researcher would like to find out why certain changes in consumer behavior occur over time. To do this, he studies the same group of people at several points over a few years. This researcher is conducting a a. cohort study. b. panel study. c. cross-sectional survey. d. focus group. Lan81479_08_c08_217-246.indd 242 5/22/14 2:24 PM Summary & Resources 7. Asking survey questions that are not specific enough is considered a form of measurement error. a. true b. false 8. Survey researchers should look for bias in respondents or nonrespondents when they receive a response rate of a. 25% or less. b. 75% or less. c. 75% or more. d. 90% or less. 9. Surveys are useful tools for understanding respondents’ skills. a. true b. false 10. Which of the following is an example of a reverse-scored survey item? a. The training was helpful to me. b. The information session was boring. c. The product provides a good value for the money. d. I have NOT benefitted from the company’s service. 11. A survey provides a statement and then asks respondents whether they completely disagree, generally disagree, generally agree, or completely agree with that statement. This is an example of a(n) a. Likert scale. b. dichotomous scale. c. Likert-type scale. d. semantic differential scale. 12. Which type of scale would you choose if you wanted to measure emotions? a. Likert scale b. dichotomous scale c. Likert-type scale d. semantic differential scale 13. In pilot testing, researchers should check that their survey includes an appropriate vocabulary level, along with an adequate font size. a. true b. false 14. Which of the following is a reason pilot tests are important in survey research? a. Pilot tests show whether participants understand the survey questions, which can keep an experiment from failing. b. Pilot tests provide data to compare to the survey post-tests, allowing researchers to measure the effectiveness of the intervention. c. Pilot tests allow for triangulation, helping to eliminate researcher bias. d. Pilot tests are necessary to protect the anonymity of participants, which makes them more likely to answer the survey questions honestly. Lan81479_08_c08_217-246.indd 243 5/22/14 2:24 PM Summary & Resources 15. One advantage of surveys, compared to other research methods, is the greater control they offer over variables of interest. a. true b. false 16. How can researchers successfully use the survey method to detect change over time? a. by using standardized questions b. by statistically analyzing large amounts of data c. by carefully controlling sampling mechanisms d. by drawing cause-and-effect conclusions Answers 1. b. nonprobability sampling. The correct answer can be found in Section 8.1. 2. a. snowball sampling. The correct answer can be found in Section 8.1. 3. d. online surveys and telephone interviews. The correct answer can be found in Section 8.2. 4. c. a mixed mode approach. The correct answer can be found in Section 8.2. 5. d. cross-sectional survey. The correct answer can be found in Section 8.3. 6. b. panel study. The correct answer can be found in Section 8.3. 7. a. True. The correct answer can be found in Section 8.4. 8. a. 25% or less. The correct answer can be found in Section 8.4. 9. b. False. The correct answer can be found in Section 8.5. 10. b. The information session was boring. The correct answer can be found in Section 8.5. 11. c. Likert-type scale. The correct answer can be found in Section 8.6. 12. d. semantic differential scale. The correct answer can be found in Section 8.6. 13. a. True. The correct answer can be found in Section 8.7. 14. a. Pilot tests show whether participants understand the survey questions, which can keep an experiment from failing. The correct answer can be found in Section 8.7. 15. b. False. The correct answer can be found in Section 8.8. 16. a. by using standardized questions. The correct answer can be found in Section 8.8. Questions for Critical Thinking 1. Throughout your life you have likely been a participant in at least one survey research study, whether it be a telephone survey, a mail survey, someone approaching you at the mall and wanting to ask you some questions, or an Internet survey that pops up as you enter a website. In considering these different survey modalities, does the way in which you are asked the questions affect whether or not you will answer them? Which survey modality would you prefer? Do you think there may be an age difference regarding survey mode? Explain. 2. Sometimes errors are obvious, and sometimes they are quite difficult to find. In looking back on the different types of errors possible in survey research, generate an example from your own experience that demonstrates coverage error, sampling error, measurement error, and nonresponse error. Describe the experience in detail, pointing out which error occurred on which occasion. Lan81479_08_c08_217-246.indd 244 5/22/14 2:24 PM Summary & Resources 3. Have you ever attempted to answer a survey item, but were unable to because the scale didn’t make sense or there was an error? In thinking about recent surveys that you may have completed, what scales were used, and what types of data analytic approaches were linked to the scale selected? For instance, if you were answering a survey item on a Likert-type agreement scale where 1 = strongly disagree and 5 = strongly agree, what type of statistical analysis might you use (depending on the other variable)? If you were going to present this data to a client, what type of chart might you use, and why? Key Terms closed-ended survey items A type of survey question where all possible answers are provided, and the participant selects the items closest to their own beliefs. cluster sampling The sampling practice of “clustering” groups of a population instead of evaluating each individual person in order to gain information when it is impossible or impractical to compile an exhaustive list of members comprising the target population. cohort study A study design in which new samples of individuals are followed over time. coverage The issue of who has Internet access and who does not that provides a barrier to obtaining information through online surveys; similar issue involved with telephone surveys. coverage error An error regarding the methodology used including access to Internet, use of telephones, and other methodologies. cross-sectional survey design A study design where data collection occurs at a single point in time with the population of interest. data cleaning A method of reviewing data entry to ensure that it has been handled and entered accurately. demographics Variables used to identify the traits of a study population. Lan81479_08_c08_217-246.indd 245 dichotomous scale A scale in which there are only two possible responses. focus group A group of people who are interviewed at the same time for the purpose of conducting a survey. in-person interviews A research methodology that allows an interviewer and a participant to build rapport through conversation and eye contact, which might allow for deeper questions to be asked about the topic of interest. longitudinal survey A study design where data collection occurs at several different points over an extended period of time. mixed mode approach A study design where multiple research modalities are accessed to achieve the research goals. multistage sampling The two-stage sampling practice involving the formation of clusters as a primary selection, then sampling members from the selected clusters to produce a final sample. nonprobability sampling The sampling practice where the probability of each participant being selected for a study is unknown and sampling error cannot be estimated. nonresponse error An error occurring when there is a response rate of 25% or less for a particular question. open-ended survey items A type of survey item that is answered in words or phrases. 5/22/14 2:24 PM Summary & Resources panel study A study design in which the same people are studied over time, spanning at least two points in time. pilot test A “practice run” of a questionnaire used to determine weaknesses and optimize the length of the scale for adequate response rate. The conditions in which the survey will be administered are typically replicated as close as possible to the actual survey administration. probability sampling The sampling practice where the probability of each participant being selected for a study is known and sampling error can be estimated. quota sampling The sampling practice where a researcher identifies a target population of interest and then recruits individuals (nonrandomly) of that population to participate in a study. representativeness The assumption that a sample will resemble all qualities of the general population in order to ensure that results of a sample can be applied to the whole general population. sampling error An error occurring when all potential participants from a population may not be represented in a sample. scale A tool used to measure a person’s attitudes, perceptions, behaviors, etc., that is chosen to best represent a study. Additional Resources • • • Lan81479_08_c08_217-246.indd 246 semantic differential scale A survey response scale used to measure affect and emotion using dichotomous pairs of words and phrases that a participant evaluates on a scale of one to seven. simple random sample The practice of the purest form of sampling, and also one of the rarest techniques used, where everybody in the survey population has the same probability of being tested. snowball sample The sampling practice where members of the target population of interest are asked to recruit other members of the same population to participate in the study. stratified sample The practice of dividing a sample into subcategories (strata) in a way that identifies existing subgroups in a general population in order to make a sample the same proportion as displayed in a population. systematic random sample The sampling practice in which every nth person from a sample is selected. visual analog scale A survey response scale used to obtain a score along a continuum, where a participant places a checkmark to indicate where his or her opinion falls along the scale. volunteer sample The common sampling practice where volunteers are asked to participate in a survey. A journal article in which the authors address key issues in international survey research. http://www.harzing.com/intresearch_keyissues.htm A brief review of the different types of survey methods available for small to medium businesses. http://www.surveybuilder.info/various-survey-methods-for-small-and-medium -businesses/ A comprehensive overview of a survey system utilized in business. http://www.surveysystem.com/sdesign.htm 5/22/14 2:24 PM Secondary Data Analysis 11 D-BASE/Photodisc/Getty Images Learning Objectives After reading this chapter, you should be able to: • Explain what secondary data are and identify their sources. • Determine when to use different data analysis procedures and demonstrate awareness about determining the feasibility of secondary data analysis studies. • Articulate the advantages and the disadvantages of using secondary data. 283 Lan81479_11_c11_283-294.indd 283 5/22/14 2:26 PM Secondary Data Section 11.1 Pre-Test 1. Inventory management, sustainability, demand management, and market trends are all business topics that have been studied using secondary data. a. true b. false 2. Geographic Information Systems use assumptions from various cases to emulate and predict real-world behaviors. a. true b. false 3. One advantage of using secondary data is that a data set can contain thousands of variables of interest. a. true b. false Answers 1. a. true. The correct answer can be found in Section 11.1. 2. b. false. The correct answer can be found in Section 11.2. 3. a. true. The correct answer can be found in Section 11.3. Introduction There is clearly a “green” movement alive these days, with an emphasis on reusing and recycling items so that there is less pollution, a reclamation of useable goods and key minerals, and so on. Recycling takes many forms, and it even appears in this chapter to an extent. Those researchers who employ secondary data analysis techniques are essentially recycling available data for new purposes. Sometimes the recycled data is a great match and fit for the secondary data analysis project, but this approach is not without its disadvantages and drawbacks. This chapter provides a brief overview of this approach and its key components. 11.1 Secondary Data Essentially, primary data are collected when a researcher does an empirical research study and generates new data to be analyzed and interpreted. In this type of research, the researcher or team designs, collects, and analyzes the original data. Secondary data are data that are already in existence and are reused (think “recycled”). That is, secondary data are not directly compiled by the researcher conducting the analysis (Koziol & Arthur, 2011; Tasic & Feruh, 2012). For example, the demographic and economic data gathered and published by a government (such as U.S. census data) become secondary data to researchers outside of the agency that specifically gathered the data. Research with secondary sources is sometimes known as desk research (Crawford, 1997). Lan81479_11_c11_283-294.indd 284 5/22/14 2:26 PM Secondary Data Section 11.1 Secondary data analysis techniques have broad applications throughout business. In an analysis of research articles published in the Journal of Business Logistics for 2009–2010, the variety of business topics studied using secondary data is remarkable (Rabinovich & Cheon, 2011). These topics include transportation networks, market trends, inventory management, competitive dynamics, fulfillment and distribution, transportation safety, costs and financial performance, sustainability, risk and disaster management, forecasting, human resources, information technology, demand management, and buyer–supplier relationships. For example, Hofer, Eroglu, and Hofer (2012) examined the role of inventory leanness on financial performance in part using secondary data analysis. Inventory leanness uses an approach similar to just-in-time practices to minimize waste, with the goals being lower inventory, better quality, and shorter production times. To help ascertain the effects of inventory leanness, Hofer et al. (2012) used company-level finance and inventory data from a Standard & Poor database, which allowed access to data from 1,421 firms in the United States. These data, combined with other approaches in the study, allowed the authors to conclude that inventory leanness does contribute positively toward overall company performance. Although it’s a complex approach, secondary data analysis can yield important insights not easily gained otherwise. Most researchers who work in the field of secondary data analysis classify this type of research into two subcategories: internal data (collected by the organization for other reasons) and external data (data collected outside of an organization) (Dobson, 2014). Although this advice may differ depending on the source, it is recommended that internal sources be considered first because internal data are proprietary and are exclusive to the business, so rival businesses will not have access to the information (Riley, 2012). Internal Sources There can be numerous sources of data found within a business, including customer data, information about competitors, and industry-wide data. Examples of internal data sources include sales data (invoices, quarterly sales reports), customer loyalty cards, financial data (such as accounts receivable reports), transport data, reports of complaints and critical incidents, storage data, and advertising data (Crawford, 1997; Dobson, 2014). Even the success of past advertising campaigns can be compared with sales invoices and become a source of secondary data (Riley, 2012). One of the main disadvantages of using internal data is that it will only reveal trends with current customers, and it does not provide much insight into industry-wide trends (Dobson, 2014). External Sources Commonly cited sources of external data include other companies, the press, academic researchers, private sources, library sources, syndicated services, general business publications, statistics from the federal government and regulatory bodies, trade associations, commercial data services, statistics agencies, and national and multinational organizations (Cowton, 1998; Crawford, 1997; Steppingstones Partnership, 2014). Secondary data can also be found in college/university records, journal supplements, and author websites (Koziol & Arthur, 2011); an exhaustive listing of possible external sources is just not possible. The advantages of external sources of data are that they may be cheaper to procure than original Lan81479_11_c11_283-294.indd 285 5/22/14 2:26 PM Secondary Data Section 11.1 primary data, and they may allow for perspectives not readily available within an organization. In a similar vein, these can be the drawbacks to external data, as they may not be individually suited or applicable to a particular organization. To get some ideas, the following are possible locations to find secondary data sets (Koziol & Arthur, 2011): • • • • Inter-university Consortium for Political and Social Research—it provides data and educational opportunities (http://www.icpsr.umich.edu/icpsrweb/landing.jsp); the U.S. government’s open data warehouse (http://www.data.gov); U.S. Census Bureau website, providing measures of America’s people, places, and economy (http://www.census.gov); and a Penn State University resource that is a simple online data archive for population studies (http://sodapop.pop.psu.edu/data-collections). After a company has determined that the examination of secondary data (either internally or externally sourced) is a good idea, research questions and data analysis options are the next key considerations. Case Study: Mining Data to Support a Site Location Decision Profile: Derrick Van Mell, Van Mell Associates Derrick Van Mell, a consultant in strategic planning applied to facilities management, conducted site location research for a successful neighborhood center in Madison, Wisconsin. He combined data sources including demographics, transportation, and land use to identify the best option to serve a diverse constituency of visitors, employees, and funders of the facility. The Goodman Community Center has undergone several changes in organization, funding, and names since it was founded in 1948. Originally conceived as a youth activity center, its activities expanded over the years to serve area residents of all ages with a variety of educational, recreational, and social programs. The center is located in a remarkably diverse 100-year-old neighborhood characterized by a mix of housing, shopping, and jobs, where wealthy and low-income families live in close proximity. © Marlene Ford/Spaces Images/Corbis Atwood Community Center, as it was once known, expanded from one building to four along a busy commercial street as its programs expanded. At the beginning of the search for a new, consolidated facility, center director Becky Steinhoff and the board of directors hired Van Mell to research the Kupfer Ironworks, a century-old building located less than half a mile from the center’s existing facilities. “Sophisticated mapping can depict the intricate forces of the market, labor pool, and transportation web,” wrote Van Mell in Buildings Matter (2005). Van Mell drew on the technique of Graphic Information System analysis, or GIS, for this project, merging cartography and statistical analysis to produce maps designed to aid decision making. (continued) Lan81479_11_c11_283-294.indd 286 5/22/14 2:26 PM Secondary Data Section 11.1 Case Study: Mining Data to Support a Site Location Decision (continued) Van Mell began by consulting demographic data to understand the population served. “The community center has many audiences, from the youngest to the oldest in our community. Each audience has different patterns of using the center, not just for different services but at different times of day,” Van Mell said. Would relocating to the factory, located in a residential area along the rail line between the area’s two major traffic arteries, discourage current members from traveling to the center, or make it more available to a larger population? “We created a plot of the current center participants, using color coding to indicate children, seniors, and everybody in between,” said Van Mell. “We used address-specific information, and then added general census information by census tract.�...
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