Questions are asked in an interactive group setting where participants are free to talk with other group members. The analysis of focus group data presents both challenges and opportunities when compared to other types of qualitative data.
Some authors have suggested that data should be analysed in the same manner as interview data, while others have suggested that the unique features of focus group data - particularly the opportunity that it provides to observe interactions between group members - means that distinctive forms of analysis should be used.
Data analysis can take place at the level of the individual or the group. Focus group data provides the opportunity to analyse the strength with which an individual holds an opinion.
At the collective level, focus group data can sometimes reveal shared understandings or common views. Interview Questioning in the verbal form is known as Interview. As a research tool, interview is different from general interviewing in regard to preparation, construction and execution. It is controlled by the researcher to avoid any biasness and distortion. In the research interview, the interviewer asks specific questions pertaining to research objectives and the respondent answers appropriately.
The interview can be of flexible in its own form, such as structured or unstructured, individual or group, self- administered or other-administered, personal or non-personal, focused, telephonic, etc. Interviewers differ in interest and skill, respondents differ in ability and motivation and content of interview differs in feasibility. To obtain a 23 Ibid. Each of these questions could be addressed using quantitative techniques such as structured questionnaires, attitude scaling, and measurement of standard outcomes such as mortality, morbidity or staff absence rates.
All of these can be readily analysed statistically, and you will get some sort of answer to the question. Qualitative data tends to take up many pages of typescript, or lots of megabytes on a disc! It is usually in the form of words and narratives, but may include visual images, videotape, or other media.
However, not all numbers are continuous and measurable. For example, the social security number is a number, but not something that one can add or subtract. Quantitative data always are associated with a scale measure. These data may be represented by ordinal, interval or ratio scales and lends themselves to most statistical manipulation. Continuous variables should only be used with class intervals, which will be explained shortly.
Frequency distributions can show either the actual number of observations falling in each range or the percentage of observations. In the latter instance, the distribution is called a relative frequency distribution. Qualitative Research Analysis. Today it is being used in every walk of life. Computers are indispensible throughout the research process. The role of computer becomes more when the research is on a large scale. The collected data can be stored on the computer for immediate use or as a backup in auxiliary storage devices.
During data analysis, the computer helps in the mathematical part i. They can also be used to calculate the sample size of the proposed study, hypothesis testing and calculating the power of the study.
Computers are not only useful for statistical analysis, but also to monitor the accuracy and completeness of the collected data. At times the research may cover a large number of people which may result in not being able to cover all, due to the lack of time and resource.
Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of.
This book offers a relatively self-contained presentation of the fundamental results in categorical data analysis, which plays a central role among the statistical techniques applied in the social, political and behavioral sciences, as well as in marketing and medical and biological research.
The methods applied are mainly aimed at understanding the structure of associations among variables and the effects of other variables on these interactions. A great advantage of studying categorical data analysis is that many concepts in statistics become. The nonstatistician's quick reference to applied categorical data analysis With a succinct, unified approach to applied categorical data analysis and an emphasis on applications, this book is immensely useful to researchers and students in the biomedical disciplines and to anyone concerned with statistical analysis.
This self-contained volume provides up-to-date coverage of all major methodologies in this area of applied statistics and acquaints the reader with statistical thinking as expressed through a variety of modern-day topics and techniques.
Applied Categorical Data. Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology.
This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories;.
An epidemiologist who finds an association should try to determine whether the observed statistical association from the study is due to random variation or whether it reflects an actual association between the characteristic and the disease.
Courtroom arguments about the interpretations of these types of associations involve data analyses using statistical concepts as well as a clinical interpretation of the data. Many other examples exist in which statistical models are used in court cases. In salary discrimination cases, a lawsuit is filed claiming that an employer underpays employees on the basis of age, ethnicity, or sex.
Statistical models are developed to explain salary differences based on many factors, such as work experience, years of education, and work performance. The adjusted salaries are then compared across age groups or ethnic groups to deter- mine whether significant salary differences exist after adjusting for the relevant work performance factors.
Estimating Bowhead Whale Population Size Raftery and Zeh discuss the estimation of the population size and rate of increase in bowhead whales, Balaena mysticetus. The importance of such a study derives from the fact that bowheads were the first species of great whale for which commercial whaling was stopped; thus, their status indicates the recovery prospects of other great whales.
To obtain the necessary data, researchers conducted a visual and acoustic census off Point Barrow, Alaska. The researchers then applied statistical models and estimation techniques to the data obtained in the census to determine whether the bowhead population had increased or decreased since commercial whaling was stopped. The statistical estimates showed that the bowhead population was increas- ing at a healthy rate, indicating that stocks of great whales that have been decimated by commercial hunting can recover after hunting is discontinued.
Whereas the decreasing stratospheric ozone layer may lead to increased instances of skin cancer, high ambient ozone intensity has been shown to cause damage to the human respiratory system as well as to agricultural crops and trees.
Carroll et al. Besides the ozone level, each station also recorded three meteorological variables: temperature, wind speed, and wind direction. The statistical aspect of the project had three major goals:. Build a model of ozone intensity to predict the ozone concentration at any given location within Houston at any given time between and Apply this model to estimate exposure indices that account for either a long-term exposure or a short-term high-concentration exposure; also, relate census information to different exposure indices to achieve population exposure indices.
The spatial—temporal model the researchers built provided estimates demon- strating that the highest ozone levels occurred at locations with relatively small populations of young children. An examination of the distribution of population exposure had several policy impli- cations.
In particular, it was concluded that the current placement of monitors is not ideal if one is concerned with assessing population exposure. This project in- volved all four components of learning from data: planning where the monitoring stations should be placed within the city, how often data should be collected, and what variables should be recorded; conducting spatial—temporal graphing of the data; creating spatial—temporal models of the ozone data, meteorological data, and demographic data; and finally, writing a report that could assist local and federal officials in formulating policy with respect to decreasing ozone levels.
Opinion and Preference Polls Public opinion, consumer preference, and election polls are commonly used to assess the opinions or preferences of a segment of the public for issues, products, or candidates of interest. For example, the results of polls related to the following subjects were printed in local newspapers over a 2-day period:.
American cars, Coke vs. A number of questions can be raised about polls. What was the population of interest to the pollster? Was the pollster interested in all residents of Michigan or just those citizens who currently pay income taxes? Was the sample in fact selected from this population? If the popula- tion of interest was all persons currently paying income taxes, did the pollster make sure that all the individuals sampled were current taxpayers?
What questions were asked and how were the questions phrased? Was each person asked the same question? Were the questions phrased in such a manner as to bias the responses? Can we believe the results of these polls? Opinion and preference polls are an important, visible application of statistics for the consumer. We will discuss this topic in more detail in Chapter We hope that after studying this material you will have a better understanding of how to interpret the results of these polls.
What do statisticians do? In the context of learning from data, statisticians are involved with all aspects of designing a study or experiment, preparing the data for analysis using graphical and numerical summaries, analyzing the data, and reporting the results of their analyses.
There are both good and bad ways to gather data. Statisticians apply their knowledge of existing survey techniques and scientific study designs or they develop new techniques to provide a guide to good methods of data collection.
We will explore these ideas further in Chapter 2. Once the data are gathered, they must be summarized before any meaningful interpretation can be made. Statisticians can recommend and apply useful methods for summarizing data in graphical, tabular, and numerical forms. Intelligent graphs and tables are useful first steps in making sense of the data.
Also, measures of the average or typical value and some measure of the range or spread of the data help in interpretation. These topics will be discussed in detail in Chapter 3. The objective of statistics is to make an inference about a population of interest based on information obtained from a sample of measurements from that population.
The analysis stage of learning from data deals with making inferences. If the market research study has been carefully planned and executed, the reactions of those included in the sample should agree reasonably well but not necessarily exactly with the population. We can say this because the basic concepts of probability allow us to make an inference about the population of interest that includes our best guess plus a statement of the probable error in our best guess.
We will illustrate how inferences are made by an example. Suppose an auditor randomly selects 2, financial accounts from a set of more than 25, accounts and finds that 84 4. What can be said about the set of 25, accounts? What inference can we make about the percentage of accounts in error for the population of 25, accounts based on information obtained from the sample of 2, accounts? We will show in Chapter 10 that our best guess inference about the percentage of accounts in error for the population is 4.
The plus-or-minus factor is called the probable error of our inference. Anyone can make a guess about the percentage of accounts in error; concepts of probability allow us to calculate the probable error of our guess.
In dealing with the analyses of data, statisticians can apply existing methods for making inferences; some theoretical statisticians engage in the development of new methods with more advanced mathematics and probability theory.
Our study of the methods for analyzing sample data will begin in Chapter 5, after we discuss the basic concepts of probability and sampling distributions in Chapter 4. Finally, statisticians are involved with communicating the results of their analyses as the final stage in making sense of data.
The form of the communication varies from an informal conversation to a formal report. Too often, this is lost in an informal conversation. The report or communication should convey to the intended audience what can be gleaned from the sample data, and it should be conveyed in as nontechnical terms as possible so there can be no confusion as to what is inferred.
More information about the communication of results is presented in Chapter We will identify the important components that should be included in the report while discussing case studies used to illustrate the statistical concepts in several of the chapters. It is important to note that the ideas in the preceding discussion are relevant to everyone involved in a study or experiment. Degreed statisticians are somewhat rare individuals. Many organizations have no statisticians or only a few in their employment.
Thus, in many studies, the design used in collecting the data, the summary and statistical analyses of the data, and the communication of the study results will be conducted by the individuals involved in the study with little or no support from a degreed statistician. In those cases where a statistician is an active member of the research team, it is still important for the other members of the group to have general knowledge of the concepts involved in a statistical design and data analysis.
In fact, each member of the team brings an area of expertise and experience to the problems being addressed. Then within the context of the team dynamics, decisions will be made about the design of the study and how the results of the analyses will be communicated.
We do so to make you aware of some of the broader issues involved with learning from data in the business and scientific communities. During this time, there was little attempt to change the ways things were done; the major focus was on doing things on a much grander scale, perfecting mass production.
However, from the mids through today, many industries have had to face fierce competi- tion from their counterparts in Japan and, more recently, from other countries in the Far East, such as China and Korea. Quality, rather than quantity, has become the principal buying gauge used by consumers, and American industries have had a difficult time adjusting to this new emphasis. The Japanese were the first to learn the lessons of quality. They readily used the statistical quality-control and process-control suggestions espoused by Deming and others and installed total quality programs.
Through the organization— from top management down—they had a commitment to improving the quality of their products and procedures.
They were never satisfied with the way things were and continually looked for new and better ways. A number of American companies have now begun the journey toward excellence through the institution of a quality-improvement process.
Listed below are ten basic requirements that provide the foundation for a successful quality- improvement process:. A focus on the customer as the most important part of the process 2. A long-term commitment by management to make the quality-im- provement process part of the management system 3. The belief that there is room to improve 4.
The belief that preventing problems is better than reacting to them 5. Management focus, leadership, and participation 6. A performance standard goal of zero errors 7. Participation by all employees, both as groups and as individuals 8. An improvement focus on the process, not the people 9. The belief that suppliers will work with you if they understand your needs Recognition for success. Embedded in a companywide quality-improvement process or running con- current with such a process is the idea of improving the work processes.
For years, companies, in trying to boost and improve performance, have tried to speed up their processes, usually with additional people or technology but without ad- dressing possible deficiencies in the work processes.
If we define a task as a unit of work, and a process as a sequence of related tasks that create value for the customer, Hammer and Champy were offering corporations a way to refocus their change efforts in value-creating activities. The case for change is compelling. Within almost every major business— apparel e. In many cases the industry leader has not kept pace with the dizzying changes occurring in the marketplace. Mergers proliferate with high ex- pectations from management and shareholders for increased market share, cost synergies reductions , and increased profitability.
Unfortunately, the list of suc- cessful mergers as defined by those meeting the initial case for action driving the merger is pitifully small. Something else is needed. A company that can do this well over time, as needs and the competitive environment change, will win.
Whether a company focuses on business process improvement or fast cycle time, the foundation for change will be the underlying data about customer needs, current internal cycle time, and comparable benchmark data in the industry.
These four points, which are very similar to the four steps in learning from data discussed earlier in the chapter, drive home the relevance of statistics learning from data to the business environment.
A number of statistical tools and techniques that can help in these business improvement efforts are shown here. Statistical Tools, Techniques, and Methods Used in Quality Improvement and Reengineering r Histograms r Numerical descriptive measures means, standard deviations, proportions, etc.
As you encounter these tools and concepts in various parts of this text, keep in mind where you think they may have application in business improvement efforts. Quality improvement, process redesign, and fast cycle time are clearly the focus of American industry for the s in world markets characterized by in- creased competition, more consolidation, and increased specialization.
These shifts will have impacts on us all, either as consumers or business participants, and it will be useful to know some of the statistical tools that are part of this revolution. Finally, in recent years the ideas and principles of quality control have been applied in areas outside of manufacturing. Service industries such as hotels, restau- rants, and department stores have successfully applied the principles of quality control in their businesses.
Many federal agencies—for example, the IRS, the Department of Defense, and the USDA—have adapted the principles of quality control to improve the performance of their agencies.
A study of the discipline of statistics requires us to memorize new terms and concepts as does the study of a foreign language. Commit these definitions, theorems, and concepts to memory. Also, focus on the broader concept of making sense of data.
Do not let details obscure these broader characteristics of the subject. The teaching objective of this text is to identify and amplify these broader concepts of statistics. Medical researchers, social scientists, accountants, agronomists, consumers, government leaders, and professional statisticians are all involved with data collection, data summarization, data analysis, and the effective communica- tion of the results of data analysis.
Supplementary Exercises Basic Techniques Bio. A researcher wishes to estimate the mean weight of shrimp maintained on a specific diet for a period of 6 months. One hundred shrimp are randomly selected from an artificial pond and each is weighed.
Identify the population of measurements that is of interest to the researcher. Identify the sample. What characteristics of the population are of interest to the researcher? If the sample measurements are used to make inferences about certain characteristics of the population, why is a measure of the reliability of the inferences important?
State health officials have decided to investigate the radioactivity levels in one suspect area. Two hundred points in the area are randomly selected and the level of radioactivity is measured at each point. Answer questions a , b , c , and d in Exercise 1. Supplementary Exercises A random sample of households is selected from the city welfare rolls. A check on welfare recipient data provides the number of children in each household.
Answer questions a , b , c , and d of Exercise 1. Identify the items that were observed in order to obtain the sample measurements. Identify the measurement made on each item. Clearly identify the population associated with the survey. What characteristic s of the population was were of interest to the pollster? Does the article explain how the sample was selected? Does the article include the number of measurements in the sample?
What type of inference was made concerning the population characteristics? Does the article tell you how much faith you can place in the inference about the population characteristic? A total of helmets were collected from the five companies that currently produce helmets. The agency then sent the helmets to an independent testing agency to evaluate the impact cushioning of the helmet and the amount of shock transmitted to the neck when the face mask was twisted.
What is the population of interest? What is the sample? What variables should be measured? What are some of the major limitations of this study in regard to the safety of helmets worn by high school players? For example, is the neck strength of the player related to the amount of shock transmitted to the neck and whether the player will be injured? How could the sample be selected? What type of questions should be included in the questionnaire? Scientific Studies to Preparing Data for Summarization and.
Gather Data Analysis 2. The design of the data collection process is the crucial step in intelligent data gathering. The process takes a conscious, concerted effort focused on the following steps:. To specify the objective of the study you must understand the problem being addressed. Thus, the department needs to determine what aspects of the bus system determine whether or not a person will ride the bus.
The objective of the study is to identify factors that the transportation department can alter to increase the number of people using the bus system. To identify the variables of interest, you must examine the objective of the study. For the bus system, some major factors can be identified by reviewing studies conducted in other cities and by brainstorming with the bus system employ- ees. The measurements to be obtained in the study would consist of importance ratings very important, important, no opinion, somewhat unimportant, very unimportant of the identified factors.
Demographic information, such as age, sex, income, and place of residence, would also be measured. Finally, the measurement of variables related to how frequently a person currently rides the buses would be of importance. Once the objectives are determined and the variables of interest are specified, you must select the most. Data collection processes include surveys, experiments, and the examination of existing data from business records, censuses, government records, and previous studies.
The theory of sample surveys and the theory of experimental designs provide excellent methodology for data collection. Usually surveys are passive. The goal of the survey is to gather data on existing conditions, attitudes, or behaviors. Thus, the transportation department would need to construct a questionnaire and then sample current riders of the buses and persons who use other forms of transportation within the city. Scientific studies, on the other hand, tend to be more active: The person conducting the study varies the experimental conditions to study the effect of the conditions on the outcome of the experiment.
For example, the transportation department could decrease the bus fares on a few selected routes and assess whether the usage of its buses increased.
Thus, an increase in bus usage may have taken place because of a strike of subway workers or an increase in gasoline prices.
In most scientific experiments, as many as possible of the factors that affect the measurements are under the control of the experimenter. A floriculturist wants to determine the effect of a new plant stimulator on the growth of a commercially produced flower.
The floriculturist would run the experiments in a greenhouse, where temperature, humidity, moisture levels, and sunlight are controlled. An equal number of plants would be treated with each of the selected quantities of the growth stimulator, including a control—that is, no stimulator applied. At the conclusion of the experiment, the size and health of the plants would be measured. The optimal level of the plant could then be determined, because ideally all other factors affecting the size and health of the plants would be the same for all plants in the experiment.
In this chapter, we will consider some of the survey methods and designs for scientific studies. We will also make a distinction between a scientific study and an observational study. These surveys determine such government policies as the control of the economy and the promotion of social programs.
Opinion polls are the basis of much of the news reported by the various news media. Ratings of television shows determine which shows will be available for viewing in the future. Who conducts surveys? However, the vast majority of surveys are conducted for a specific industrial, governmental, administrative, or scientific purpose. For example, auto manufacturers use surveys to find out how satisfied customers are with their cars.
Frequently we are asked to complete a survey as part of the warranty registration process following the purchase of a new product. Many important studies involving health issues are determined using surveys—for exam- ple, amount of fat in the diet, exposure to secondhand smoke, condom use and the prevention of AIDS, and the prevalence of adolescent depression.
The U. Bureau of the Census is required by the U. With the growing involvement of the government in the lives of its citizens, the Census Bureau has expanded its role beyond just counting the population. An attempt is made to send a census question- naire in the mail to every household in the United States.
Since the census, in addition to the complete count information, further information has been obtained from representative samples of the population. In the census, variable sam- pling rates were employed.
For most of the country, approximately five of six households were asked to answer the 14 questions on the short version of the form. The remaining households responded to a longer version of the form containing an additional 45 questions. Many agencies and individuals use the resulting informa- tion for many purposes. The federal government uses it to determine allocations of funds to states and cities. Businesses use it to forecast sales, to manage personnel, and to establish future site locations.
Urban and regional planners use it to plan land use, transportation networks, and energy consumption.
Social scientists use it to study economic conditions, racial balance, and other aspects of the quality of life. Some of the best known and most widely used are the surveys that establish the consumer price index CPI.
The CPI is a measure of price change for a fixed market basket of goods and services over time. It is a measure of inflation and serves as an economic indicator for government policies. Businesses tie wage rates and pension plans to the CPI. Federal health and welfare programs, as well as many state and local programs, tie their bases of eligibility to the CPI. Escalator clauses in rents and mortgages are based on the CPI.
This one index, determined on the basis of sample surveys, plays a fundamental role in our society. Many other surveys from the BLS are crucial to society. The monthly Current Population Survey establishes basic information on the labor force, employment, and unemployment. The consumer expenditure surveys collect data on family expenditures for goods and services used in day-to-day living.
The Establishment Survey collects information on employment hours and earnings for nonagricultural business establishments.
The survey on occupational outlook provides information on future employment opportunities for a variety of occupations, projecting to approximately 10 years ahead. Opinion polls are constantly in the news, and the names of Gallup and Harris have become well known to everyone. These polls, or sample surveys, reflect the attitudes and opinions of citizens on everything from politics and religion to sports and entertainment. The Nielsen ratings determine the success or failure of TV shows.
The Nielsen retail index furnishes up-to-date sales data on foods, cosmetics, pharmaceuticals, beverages, and many other classes of products. The data come from auditing inventories and sales in 1, stores across the United States every 60 days. Businesses conduct sample surveys for their internal operations in addition to using government surveys for crucial management decisions. Auditors estimate account balances and check on compliance with operating rules by sampling accounts. Quality control of manufacturing processes relies heavily on sampling techniques.
Another area of business activity that depends on detailed sampling activities is marketing. Decisions on which products to market, where to market them, and how to advertise them are often made on the basis of sample survey data. The data may come from surveys conducted by the firm that manufactures the product or may be purchased from survey firms that specialize in marketing data.
Sampling Techniques A crucial element in any survey is the manner in which the sample is selected from the population. If the individuals included in the survey are selected based on convenience alone, there may be biases in the sample survey, which would prevent the survey from accurately reflecting the population as a whole.
For example, a marketing graduate student developed a new approach to advertising and, to evaluate this new approach, selected the students in a large undergraduate business course to assess whether the new approach is an improvement over standard advertisements.
Would the opinions of this class of students be represen- tative of the general population of people to which the new approach to advertising would be applied?
The income levels, ethnicity, education levels, and many other socioeconomic characteristics of the students may differ greatly from the popula- tion of interest.
Furthermore, the students may be coerced into participating in the study by their instructor and hence may not give the most candid answers to questions on a survey. Thus, we can obtain a random sample of eligible voters in a bond-issue poll by drawing names from the list of registered voters in such a way that each sample of size n has the same probability of selection. The details of simple random sampling are discussed in Section 4.
At this point, we merely state that a simple random sample will contain as much information on community preference as any other sample survey design, provided all voters in the community have similar socioeconomic backgrounds. Suppose, however, that the community consists of people in two distinct income brackets, high and low.
Voters in the high-income bracket may have opinions on the bond issue that are quite different from the opinions of low-income bracket voters. Therefore, to obtain accurate information about the population, we want to sample voters from each bracket. We can divide the population elements into two groups, or strata, according to income and select a simple random sample stratified random sample from each group. The resulting sample is called a stratified random sample.
See Chapter 5 of Scheaffer et al. Note that stratification is accomplished by using knowledge of an auxiliary variable, namely, personal income. By stratifying on high and low values of income, we increase the accuracy of our estimator. Ratio estimators not only use measurements on the response of interest but they also incorporate measurements on an auxiliary variable. Ratio estimation can also be used with stratified random sampling.
Although individual preferences are desired in the survey, a more economical procedure, especially in urban areas, may be to sample specific families, apartment buildings, or city blocks rather than individual voters.
Individual preferences can then be obtained from each eligible voter within the unit sampled. This technique cluster sampling is called cluster sampling. Although we divide the population into groups for both cluster sampling and stratified random sampling, the techniques differ.
In stratified random sampling, we take a simple random sample within each group, whereas, in cluster sampling, we take a simple random sample of groups and then sample all items within the selected groups clusters. See Chapters 8 and 9 of Scheaffer et al. For this situation, an economical technique is to draw the sample by selecting one name near the beginning of the list and then selecting every tenth or fifteenth name systematic sample thereafter.
If the sampling is conducted in this manner, we obtain a systematic sample. As you might expect, systematic sampling offers a convenient means of obtaining sample information; unfortunately, we do not necessarily obtain the most information for a specified amount of money.
Details are given in Chapter 7 of Scheaffer et al. The important point to understand is that there are different kinds of surveys that can be used to collect sample data. For the surveys discussed in this text, we will deal with simple random sampling and methods for summarizing and analyzing data collected in such a manner.
More complicated surveys lead to even more complicated problems at the summarization and analysis stages of statistics. These documents describe many of the elements crucial to obtaining a valid and useful survey. They list many of the potential sources of errors commonly found in surveys with guidelines on how to avoid these pitfalls.
A discussion of some of the issues raised in these brochures follows. Problems Associated with Surveys Even when the sample is selected properly, there may be uncertainty about whether the survey represents the population from which the sample was se- lected. It is stated in Judging the Quality of a Survey that in surveys of the general population women are more likely to participate than men; that is, the nonresponse rate for males is higher than for females.
Thus, a political poll may be biased if the percentage of women in the population in favor of a particular issue is larger than the percentage of men in the population supporting the issue. The poll would overestimate the percentage of the population in favor of the issue because the sample had a larger percentage of women than their percentage in the population. In all surveys, a careful examination of the nonresponse group must be conducted to determine whether a particular segment of the population may be either under- or overrepresented in the sample.
Some of the remedies for nonresponse are. Offering an inducement for participating in the survey 2. Using statistical techniques to adjust the survey findings to account for the sample profile differing from the population profile.
These problems often are due to the specific wording of questions in a survey, the manner in which the respondent answers the survey questions, and the fashion in which an interviewer phrases questions during the interview. Examples of specific problems and possible remedies are as follows:. Inability to recall answers to questions: The interviewee is asked how many times he or she visited a particular city park during the past year. This type of question often results in an underestimate of the av- erage number of times a family visits the park during a year because people often tend to underestimate the number of occurrences of a common event or an event occurring far from the time of the inter- view.
A possible remedy is to request respondents to use written re- cords or to consult with other family members before responding. Thus, the survey results may be biased in the direction in which the question is slanted.
The remedy is to write questions care- fully in an objective fashion. Unclear wording of questions: An exercise club attempted to determine the number of times a person exercises per week. The word exercise has different meanings to different individu- als. The result of allowing different definitions of important words or phrases in survey questions is to greatly reduce the accuracy of survey results.
Several remedies are possible: The questions should be tested on a variety of individuals prior to conducting the survey to determine whether there are any confusing or misleading terms in the questions. Many other issues, problems, and remedies are provided in the brochures from the ASA. The stages in designing, conducting, and analyzing a survey are contained in Figure 2. This diagram provides a guide for properly conducting a successful survey.
Interviewer Interviewer Code hiring training preparation. Revision of Questionnaire Questionnaire Data Data Data Data Final report Pretest operational Original preparation revision collection reduction processing analysis outline plan study idea editing coding keyboarding Preliminary Interviewer machine operational hiring Final sample Listing Sample cleaning Report plan design work selection preparation.
Preliminary sampling plan Quality control verification. Data Collection Techniques Having chosen a particular sample survey, how does one actually collect the data? The most commonly used methods of data collection in sample surveys are personal interviews and telephone interviews. A mailed questionnaire sent to a specific group of interested persons can achieve good results, but generally the response rates for this type of data collection are so low that all reported results are suspect. Frequently, objective information can be found from direct observa- tion rather than from an interview or mailed questionnaire.
For example, we can use personal interviews with eligible voters to obtain a sample of public sentiment toward a community bond issue. The primary advantage of these interviews is that people will usually respond when confronted in person.
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