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# Mba2216 week 07 08 measurement and data collection forms

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• This &quot;Deco&quot; border was drawn on the Slide master using PowerPoint&apos;s Rectangle and Line tools. A smaller version was placed on the Notes Master by selecting all of the elements (using Select All from the Edit menu), deselecting the unwanted elements such as the Title (holding down the Shift key and clicking on the unwanted elements), and then using Paste as Picture from the Edit menu to place the border on the Notes Master. After pasting as a picture, we used the resize handles (with Shift to maintain the proportions) to reduce it to the size you see. Be sure to delete this word processing box before using this template for your own presentation.
• Exhibit 6-1 illustrates design in the research process and highlights the topics covered by the term research design. Subsequent chapters will provide more detailed coverage of the research design topics.
• Exhibit 11-4 While Exhibit 11-3 summarized the characteristics of all the measurement scales. Exhibit 11-4, shown in the slide, illustrates the process of deciding which type of data is appropriate for one’s research needs.
• Measurement in research consists of assigning numbers to empirical events, objects or properties, or activities in compliance with a set of rules. This slide illustrates the three-part process of measurement. Text uses an example of auto show attendance. A mapping rule is a scheme for assigning numbers to aspects of an empirical event.
• Exhibit 11-1. The goal of measurement – of assigning numbers to empirical events in compliance with a set of rules – is to provide the highest-quality, lowest-error data for testing hypotheses, estimation or prediction, or description. The object of measurement is a concept, the symbols we attach to bundles of meaning that we hold and share with others. Higher-level concepts, constructs, are for specialized scientific explanatory purposes that are not directly observable and for thinking about and communicating abstractions. Concepts and constructs are used at theoretical levels while variables are used at the empirical level. Variables accept numerals or values for the purpose of testing and measurement. An operational definition defines a variable in terms of specific measurement and testing criteria. These are further reviewed in Exhibit 11-2 on page 341 of the text.
• Students will be building their measurement questions from different types of scales. They need to know the difference in order to choose the appropriate type. Each scale type has its own characteristics.
• This is a good time to ask students to develop a question they could ask that would provide only classification of the person answering it . Classification means that numbers are used to group or sort responses. Consider asking students if a number of anything is always an indication of ratio data. For example, what if we ask people how many cookies they eat a day? What if a business calls themselves the “number 1” pizza in town? These questions lead up to the next slide. Does the fact that James wears 23 mean he shoots better or plays better defense than the player donning jersey number 18? In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio.
• Nominal scales collect information on a variable that can be grouped into categories that are mutually exclusive and collectively exhaustive. For example, symphony patrons could be classified by whether or not they had attended prior performances. The counting of members in each group is the only possible arithmetic operation when a nominal scale is employed. If we use numerical symbols within our mapping rule to identify categories, these numbers are recognized as labels only and have no quantitative value. Nominal scales are the least powerful of the four data types. They suggest no order or distance relationship and have no arithmetic origin. The researcher is restricted to use of the mode as a measure of central tendency. The mode is the most frequently occurring value. There is no generally used measure of dispersion for nominal scales. Dispersion describes how scores cluster or scatter in a distribution. Even though LeBron James wears #23, it doesn’t mean that he is better player than #24 or a worse player than #22. The number has no meaning other than identifying James for someone who doesn’t follow the Cavs.
• Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. You can ask students to develop a question that allows them to order the responses as well as group them. This is the perfect place to talk about the possible confusion that may exist when people order objects but the order may be the only consistent criteria. For instance, if two people tell them that Pizza Hut is better than Papa Johns, they are not necessarily thinking precisely the same. One could really favor Pizza Hut and never considering eating another Papa John’s pizza, which another could consider them almost interchangeable with only a slight preference for Pizza Hut. This discussion is a perfect lead in to the ever confusing ‘terror alert’ scale (shown on the next slide)…or the ‘weather warning’ system used in some states to keep drivers off the roads during poor weather. Students can probably come up with numerous other ordinal scales used in their environment.
• Ordinal data require conformity to a logical postulate, which states: If a is greater than b , and b is greater than c , then a is greater than c . Rankings are examples of ordinal scales. Attitude and preference scales are also ordinal. The appropriate measure of central tendency is the median. The median is the midpoint of a distribution. A percentile or quartile reveals the dispersion. Nonparametric tests should be used with nominal and ordinal data. This is due to their simplicity, statistical power, and lack of requirements to accept the assumptions of parametric testing.
• In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio.
• Researchers treat many attitude scales as interval (this will be illustrated in the next chapter). When a scale is interval and the data are relatively symmetric with one mode, one can use the arithmetic mean as the measure of central tendency. The standard deviation is the measure of dispersion. The product-moment correlation, t-tests, F-tests, and other parametric tests are the statistical procedures of choice for interval data.
• In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. Mapping rules have four characteristics and these are named in the slide. Classification means that numbers are used to group or sort responses. Order means that the numbers are ordered. One number is greater than, less than, or equal to another number. Distance means that differences between numbers can be measured. Origin means that the number series has a unique origin indicated by the number zero. Combinations of these characteristics provide four widely used classifications of measurement scales: nominal, ordinal, interval, and ratio.
• Examples Weight Height Number of children Ratio data represent the actual amounts of a variable. In business research, there are many examples such as monetary values, population counts, distances, return rates, and amounts of time. All statistical techniques mentioned up to this point are usable with ratio scales. Geometric and harmonic means are measures of central tendency and coefficients of variation may also be calculated. Higher levels of measurement generally yield more information and are appropriate for more powerful statistical procedures.
• This note relates to the effort it takes to develop a good measurement scale, and that the emphasis is always on helping the manager make a better decision—actionable data.
• Exhibit 12-1 Exhibit 12-1 illustrates where scaling fits into the research process.
• An attitude is a learned, stable predisposition to respond to oneself, other persons, objects, or issues in a consistently favorable or unfavorable way. Attitudes can be expressed or based cognitively, affectively, and behaviorally. A example for each is provided in the slide. Business researchers treat attitudes as hypothetical constructs because of their complexity and the fact that they are inferred from the measurement data, not actually observed.
• Several factors have an effect on the applicability of attitudinal research for business. Specific attitudes are better predictors of behavior than general ones. Strong attitudes are better predictors of behavior than weak attitudes composed of little intensity or topic interest. Direct experiences with the attitude object produce behavior more reliably. Cognitive-based attitudes influence behaviors better than affective-based attitudes. Affective-based attitudes are often better predictors of consumption behaviors. Using multiple measurements of attitude or several behavioral assessments across time and environments improve prediction. The influence of reference groups and the individual’s inclination to conform to these influences improves the attitude-behavior linkage.
• This note relates to the effort it takes to develop a good measurement scale, and that the emphasis is always on helping the manager make a better decision—actionable data.
• Attitude scaling is the process of assessing an attitudinal disposition using a number that represents a person’s score on an attitudinal continuum ranging from an extremely favorable disposition to an extremely unfavorable one. Scaling is the procedure for the assignment of numbers to a property of objects in order to impart some of the characteristics of numbers to the properties in question. Selecting and constructing a measurement scale requires the consideration of several factors that influence the reliability, validity, and practicality of the scale. These factors are listed in the slide. Researchers face two types of scaling objectives : 1) to measure characteristics of the participants who participate in the study, and 2) to use participants as judges of the objects or indicants presented to them. Measurement scales fall into one of four general response types : rating, ranking, categorization, and sorting. These are discussed further on the following slide. Decisions about the choice of measurement scales are often made with regard to the data properties generated by each scale: nominal, ordinal, interval, and ratio. Measurement scales are either unidimensional or multidimensional, balanced or unbalanced, forced or unforced . These characteristics are discussed further as is the issue of number of scale points and rater errors.
• A rating scale is used when participants score an object or indicant without making a direct comparison to another object or attitude. For example, they may be asked to evaluate the styling of a new car on a 7-point rating scale. Ranking scale constrain the study participant to making comparisons and determining order among two or more properties or objects. Participants may be asked to choose which one of a pair of cars has more attractive styling. A choice scale requires that participants choose one alternative over another. They could also be asked to rank-order the importance of comfort, ergonomics, performance, and price for the target vehicle. Categorization asks participants to put themselves or property indicants in groups or categories. Sorting requires that participants sort card into piles using criteria established by the researcher. The cards might contain photos or images or verbal statements of product features such as various descriptors of the car’s performance.
• With a unidimensional scale, one seeks to measure only one attribute of the participant or object. One measure of an actor’s star power is his or her ability to “carry” a movie. It is a single dimension. A multidimensional scale recognizes that an object might be better described with several dimensions. The actor’s star power variable might be better expressed by three distinct dimensions - ticket sales for the last three movies, speed of attracting financial resources, and column-inch/amount of TV coverage of the last three movies.
• A balanced rating scale has an equal number of categories above and below the midpoint. Scales can be balanced with or without a midpoint option. An unbalanced rating scale has an unequal number of favorable and unfavorable response choices.
• An unforced-choice rating scale provides participants with an opportunity to express no opinion when they are unable to make a choice among the alternatives offered. A forced-choice scale requires that participants select one of the offered alternatives.
• What is the ideal number of points for a rating scale? A scale should be appropriate for its purpose. For a scale to be useful, it should match the stimulus presented and extract information proportionate to the complexity of the attitude object, concept, or construct. E.g., A product that requires little effort or thought to purchase can be measured with a simple scale (perhaps a 3 point scale). When the product is complex, a scale with 5 to 11 points should be considered. As the number of scale points increases, the reliability of the measure increases. In some studies, scales with 11 points may produce more valid results than 3, 5, or 7 point scales. Some constructs require greater measurement sensitivity and the opportunity to extract more variance, which additional scale points provide. A larger number of scale points are needed to produce accuracy when using single-dimension versus multiple dimension scales.
• Some raters are reluctant to give extreme judgments and this fact accounts for the error of central tendency . Participants may also be “easy raters” or “hard raters” making what is called error of leniency . Suggestions for addressing these tendencies are provided in the slide.
• A primacy effect is one that occurs when respondents tend to choose the answer that they saw first. When respondents choose the answer seen most recently, the recency effect has occurred. These problems can be avoided by randomizing the order in which responses are presented.
• The halo effect is the systematic bias that the rater introduces by carrying over a generalized impression of the subject from one rating to another. For instance, a teacher may expect that a student who did well on the first exam to do well on the second. Ways of counteracting the halo effect are listed in the slide.
• This scale is also called a dichotomous scale . It offers two mutually exclusive response choices. In the example shown in the slide, the response choices are yes and no, but they could be other response choices too such as agree and disagree.
• When there are multiple options for the rater but only one answer is sought, the multiple-choice, single-response scale is appropriate. The other response may be omitted when exhaustiveness of categories is not critical or there is no possibility for an other response. This scale produces nominal data.
• This scale is a variation of the last and is called a checklist. It allows the rater to select one or several alternatives. The cumulative feature of this scale can be beneficial when a complete picture of the participant’s choice is desired, but it may also present a problem for reporting when research sponsors expect the responses to sum to 100 percent. This scale generates nominal data.
• The Likert scale was developed by Rensis Likert and is the most frequently used variation of the summated rating scale. Summated rating scales consist of statements that express either a favorable or unfavorable attitude toward the object of interest. The participant is asked to agree or disagree with each statement. Each response is given a numerical score to reflect its degree of attitudinal favorableness and the scores may be summed to measure the participant’s overall attitude. Likert-like scales may use 7 or 9 scale points. They are quick and easy to construct. The scale produces interval data. Originally, creating a Likert scale involved a procedure known as item analysis . Item analysis assesses each item based on how well it discriminates between those people whose total score is high and those whose total score is low. It involves calculating the mean scores for each scale item among the low scorers and the high scorers. The mean scores for the high-score and low-score groups are then tested for statistical significance by computing t values. After finding the t values for each statement, the statements are rank-ordered, and those statements with the highest t values are selected. Researchers have found that a larger number of items for each attitude object improves the reliability of the scale.
• From Exhibit 12-3 The semantic differential scale measures the psychological meanings of an attitude object using bipolar adjectives. Researchers use this scale for studies of brand and institutional image, employee morale, safety, financial soundness, trust, etc. The method consists of a set of bipolar rating scales, usually with 7 points, by which one or more participants rate one or more concepts on each scale item. The scale is based on the proposition that an object can have several dimensions of connotative meaning. The meanings are located in multidimensional property space, called semantic space. The semantic differential scale is efficient and easy for securing attitudes from a large sample. Attitudes may be measured in both direction and intensity. The total set of responses provides a comprehensive picture of the meaning of an object and a measure of the person doing the rating. It is standardized and produces interval data. Exhibit 12-7 provides basic instructions for constructing an SD scale.
• The steps in constructing a semantic differential scale are provided in Exhibit 12-7 .
• In Exhibit 12-8 , we see a scale used by a consulting firm to help a movie production company evaluate actors for the leading role of a risky film venture. The selection of concepts is driven by the characteristics they believe the actor must possess to produce box office financial targets. To analyze the results, the set of values for each component (evaluation, potency, and activity) is averaged.
• In Exhibit 12-9 , the data are plotted on a snake diagram. Here the adjective pairs are reordered so evaluation, potency, and activity descriptors are grouped together, with the ideal factor reflected by the left side of the scale. Profiles of the three actor candidates may be compared to each other and to the ideal.
• From Exhibit 12-3 Numerical scales have equal intervals that separate their numeric scale points. The verbal anchors serve as the labels for the extreme points. Numerical scales are often 5-point scales but may have 7 or 10 points. The participants write a number from the scale next to each item. It produces either ordinal or interval data.
• From Exhibit 12-3: A multiple rating scale is similar to the numerical scale but differs in two ways: it accepts a circled response from the rater, and the layout facilitates visualization of the results. The advantage is that a mental map of the participant’s evaluations is evident to both the rater and the researcher. This scale produces interval data.
• From Exhibit 12-3: The Stapel scale is used as an alternative to the semantic differential, especially when it is difficult to find bipolar adjectives that match the investigative question. In the example, there are three attributes of corporate image. The scale is composed of the word identifying the image dimension and a set of 10 response categories for each of the three attributes. Stapel scales produce interval data.
• From Exhibit 12-3: The constant-sum scale helps researchers to discover proportions. The participant allocates points to more than one attribute or property indicant, such that they total a constant sum, usually 100 or 10. Participant precision and patience suffer when too many stimuli are proportioned and summed. A participant’s ability to add may also be taxed. Its advantage is its compatibility with percent and the fact that alternatives that are perceived to be equal can be so scored. This scale produces interval data.
• From Exhibit 12-3: The graphic rating scale was originally created to enable researchers to discern fine differences. Theoretically, an infinite number of ratings is possible if participants are sophisticated enough to differentiate and record them. They are instructed to mark their response at any point along a continuum. Usually, the score is a measure of length from either endpoint. The results are treated as interval data. The difficulty is in coding and analysis. Graphic rating scales use pictures, icons, or other visuals to communicate with the rater and represent a variety of data types. Graphic scales are often used with children.
• From Exhibit 12-3: In ranking scales , the participant directly compares two or more objects and makes choices among them. The participant may be asked to select one as the best or most preferred.
• From Exhibit 12-10: Using the paired-comparison scale , the participant can express attitudes unambiguously by choosing between two objects. The number of judgments required in a paired comparison is [(n)(n-1)/2], where n is the number of stimuli or objects to be judged. Paired comparisons run the risk that participants will tire to the point that they give ill-considered answers or refuse to continue. Paired comparisons provide ordinal data.
• From Exhibit 12-10: The forced ranking scale lists attributes that are ranked relative to each other. This method is faster than paired comparisons and is usually easier and more motivating to the participant. With five item, it takes ten paired comparisons to complete the task, but the simple forced ranking of five is easier. A drawback of this scale is the limited number of stimuli (usually no more than 7) that can be handed by the participant. This scale produces ordinal data.
• From Exhibit 12-10: When using a comparative scale , the participant compares an object against a standard. The comparative scale is ideal for such comparisons if the participants are familiar with the standard. Some researchers treat the data produced by comparative scales as interval data since the scoring reflects an interval between the standard and what is being compared, but the text recommends treating the data as ordinal unless the linearity of the variables in question can be supported.
• Q-sorts require sorting of a deck of cards into piles that represent points along a continuum. The participant groups the cards based on his or her response to the concept written on the card. Researchers using Q-sort resolve three special problems: item selection, structured or unstructured choices in sorting, and data analysis. The basic Q-sort procedure involves the selection of a verbal statements, phrases, single words, or photos related to the concept being studied. For statistical stability, the number of cards should not be less than 60, and, for convenience, not be more than 120. After the cards are created, they are shuffled, and the participant is instructed to sort the cards into a set of piles (usually 7 to 11), each pile representing a point on the judgment continuum. The left-most pile represents the concept statements, which are “most valuable,” “favorable,” and “agreeable.” The right-most pile contains the least favorable cards. In the case of a structured sort, the distribution of cards allowed in each pile is predetermined. With an unstructured sort, only the number of piles will be determined. The purpose of sorting is to get a conceptual representation of the sorter’s attitude toward the attitude object and to compare the relationships between people.
• Exhibit 12-12 There is never just one correct way to ask a question. The MindWriter Close-Up gives you the opportunity to discuss why MindWriter chose the scales that they did. In the Close-Up, Jason and Myra are conversing with the general manager of MindWriter about the necessity of testing their measurement questions. T Henry &amp; Associates has developed three scales shown in the exhibit in the slide. They also debated the wording of the anchors. This would be a good place to discuss the MindWriter scale exercise from the vignette and the Close-Up.
• Exhibit 12-14 With a cumulative scale , a participant’s agreement with one extreme scale item endorses all other items that take a less extreme position. A pioneering scale of this type was the scalogram. Scalogram analysis is a procedure for determining whether a set of items forms a unidimensional scale. A scale is unidimensional if the responses fall into a pattern in which endorsement of the item reflecting the extreme position results in endorsing all items that are less extreme. The scalogram and similar procedures for discovering underlying structure are useful for assessing attitudes and behaviors that are highly structured, such as social distance, organizational hierarchies, and evolutionary product stages.
• Exhibit 13-1 is a suggested flowchart for instrument design. The procedures followed in developing an instrument vary from study to study, but the flowchart suggests three phases. Each phase is discussed in this chapter.
• Exhibit 13-2 : By this stage in a research project, the process of moving from the general management dilemma to specific measurement question has traveled through the first three question levels: Management question –the dilemma, stated in question form, that the manager needs resolved; Research question(s) – the fact-based translation of the question the researcher must answer to contribute to the solution of the management questions; Investigative questions – specific questions the researcher must answer to provide sufficient detail and coverage of the research question. Within this level, there may be several questions as the researcher moves from the general to the specific; Measurement questions – questions participants must answer if the researcher is to gather the needed information and resolve the management question. Once the researcher understands the connection between the investigative questions and the potential measurement questions, a strategy for the survey is the next logical step.
• Exhibit 13-3: Researchers are concerned with adequate coverage of the topic and with securing the information in its most usable form. A good way to test how well the study plan meets those needs is to develop “dummy” tables that display the data one expects to secure. For example, we might be interested to know whether age influences the use of convenience foods. The dummy table would match the age ranges of participants with the degree to which they use convenience foods. The preliminary analysis plan serves as a check on whether the planned measurement questions meet the data needs of the research questions. This also helps the researcher determine the type of scale needed for each question.
• In Phase 2 (Exhibit 13-4), you generate specific measurement questions considering subject content, the wording of each question, and each response strategy. The order, type, and wording of the measurement questions, the introduction, the instructions, the transitions, and the closure in a quality communication instrument should accomplish the following: Encourage each participant to provide accurate responses, Encourage each participant to provide an adequate amount of information, Discourage each participant from refusing to answer specific questions, Discourage each participant from early discontinuation of participation, and Leave the participant with a positive attitude about survey participation.
• Questionnaires can range from those that have a great deal of structure to those that are unstructured. They contain three categories of measurement questions. Administrative questions identify the participant, interviewer, interviewer location, and conditions. These questions are rarely asked of the participant but are necessary for studying patterns within the data and identify possible error sources. Classification questions usually cover sociological-demographic variables that allow participants’ answers to be grouped so that patterns are revealed and can be studied. These questions usually appear at the end of a survey. Target questions address the investigative questions of a specific study. These are grouped by topic in the survey. Target questions may be structured or unstructured.
• This note relates to the effort it takes to develop a good measurement scale, and that the emphasis is always on helping the manager make a better decision—actionable data.
• These are the seven activities the researcher must accomplish to make an experiment a success. In the first step, the researcher is challenged to select variables that are the best operational definitions of the original concepts, determine how many variables to test, and 3) select or design appropriate measures for the chosen variables. The selection of measures for testing requires a thorough review of the available literature and instruments. In an experiment, participants experience a manipulation of the independent variable, called the experimental treatment. The treatment levels are the arbitrary or natural groups the researcher makes within the independent variable. A control group can provide a base level for comparison. A control group is a group of participants that is measured but not exposed the independent variable being studied. Environmental control means holding the physical environment of the experiment constant. When participants do not know if they are receiving the experimental treatment, they are said to be blind. When neither the participant nor the researcher knows, the experiment is said to be double-blind. The design is then selected. Several designs are discussed on the next several slides. The participants selected for the experiment should be representative of the population to which the researcher wishes to generalize the study’s results. Random assignment is required to make the groups as comparable as possible.
• Survey questions should be revised until they satisfy these criteria: Is the question stated in terms of a shared vocabulary? Does the question contain vocabulary with a single meaning? Does the question contain unsupported or misleading assumptions? Does the question contain biased wording? Is the question correctly personalized? Are adequate alternatives presented within the question?
• In choosing response options in questions, researchers must consider these factors.
• Free-response questions, also known as open-ended questions, ask the participant a question and either the interviewer pauses for the answer or the participant records his or her ideas in his or her own words in the space provided on a questionnaire. Survey researchers try to reduce the number of these questions as they are difficult to interpret and are costly to analyze.
• Dichotomous questions are measurement questions that offer two mutually exclusive and exhaustive alternatives.
• Multiple-choice questions are appropriate when there are more than two alternatives or where we seek gradations of preference, interest, or agreement. A problem can occur with this question type when one or more responses have not been anticipated. A second problem occurs when the list of choices is not exhaustive. Participants may also feel that they have multiple answers while the question allows for only one response; in this case, the response choices are not mutually exclusive. The order in which choices are given can also cause bias. Order bias with non-numeric response categories often leads the participant to choose the first alternative (primacy effect) or the last alternative (recency effect) over the middle ones. Primacy effects dominate in visual surveys while recency effects dominate oral surveys. Multiple choice questions usually generate nominal data.
• The checklist question is a question that poses numerous alternatives and encourages multiple unordered responses. Checklists are efficient and provided nominal data.
• When relative order of the alternatives is important, the ranking question is ideal. The ranking question is a measurement question that asks the participant to compare and order two or more objects or properties using a numeric scale. It is always best to have participants rank only those elements with which they are familiar. For this reason, ranking questions might follow a checklist question which identifies the objects of familiarity. Avoid asking participants to rank more than seven items. Ranking generates ordinal data.
• Exhibit 13-7 summarizes some important considerations in choosing between the various response strategies. While all of the response strategies are available for use in Web questionnaires, there are slightly different layout options for response in Web surveys.
• Exhibit 13-7 summarizes some important considerations in choosing between the various response strategies. While all of the response strategies are available for use in Web questionnaires, there are slightly different layout options for response in Web surveys.
• Exhibit 13-7 summarizes some important considerations in choosing between the various response strategies. While all of the response strategies are available for use in Web questionnaires, there are slightly different layout options for response in Web surveys.
• Exhibit 13-7 summarizes some important considerations in choosing between the various response strategies. While all of the response strategies are available for use in Web questionnaires, there are slightly different layout options for response in Web surveys. Exhibit 13-6, starting on the next slide, illustrates these layout options.
• Exhibit 13-6 ( 1 of 3)
• Exhibit 13-6 ( 2 of 3)
• Exhibit 13-6 ( 3 of 3)
• Exhibit 13-8 provides sources of questions from books and web sites. This slide highlights many of the books listed in the Exhibit.
• As depicted in Exhibit 13-9, instrument design is a multistep process. Develop the participant-screening process along with the introduction. A screen question is a question to qualify the participant’s knowledge about the target questions of interest or experience necessary to participate. Arrange the measurement question sequence: Identify groups of target questions by topic Establish a logical sequence for the question groups and questions within groups Develop transitions between these question groups. Prepare and insert instructions including termination instructions, skip directions, and probes. Create and insert a conclusion, including a survey disposition statement. Pretest specific questions and the instrument as a whole.
• The question process must quickly awaken interest and motivate the participant to participate in the interview. More interesting topical target questions should come early. Classification questions that are not used as filters or screens should come at the end of the survey. The participant should not be confronted by early requests for information that might be considered personal or ego-threatening. Buffer questions are neutral measurement questions designed to establish rapport with the participant. These can be used prior to sensitive questions. The questioning process should begin with simple items and then move to the more complex, as well as move from general items to the more specific. Taxing and challenging questions later in the questioning process. Changes in the frame of references should be small and should be clearly pointed out. Use transition statements between different topics of the target question set. An example of a transition is provided in Exhibit 13-10.
• The procedure of moving from general to more specific questions is sometimes called the funnel approach. The objectives of this procedure are to learn the participant’s frame of reference and to extract a full range of desired information while limiting the distortion effect of earlier questions on later ones.
• Sometimes the content of one question assumes other questions have been asked and answered. This is a branched question. In web surveys, branching allows for respondents to avoid “skip patterns”. Based on their answers, respondents are branched to the appropriate section of the survey. Web surveys also allow for piping. Piping takes the answer from one question and uses the answer in a later question. For instance, if in one question, a respondent named Diet Coke as his or her favorite beverage, a later question might ask “Which is your favorite characteristic of Diet Coke?”
• Exhibit 13-10 illustrates the components of a communication instruments. Instructions to the interviewer or participant attempt to ensure that all participants are treated equally. Two principles form the foundation for good instructions: clarity and courtesy. Instruction language needs to be unfailingly simple and polite. Instruction topics include those for 1) terminating an unqualified participant, 2) terminating a discontinued interview, 3) moving between questions on an instrument, and 4) disposing of a completed questionnaire. The role of the conclusion is to leave the participant with the impression that his or her involvement has been valuable.
• Now is a great time to evaluate the MindWriter instrument that has been discussed in several vignettes and Close-ups. It is contained in this chapter’s Close-Up. In Exhibit 13-12
• There is no substitution for a thorough understanding of question wording, question content, and question sequencing issues. However, the researcher can do several things to help improve survey results. These are listed in the slide. Most information can be secured by direct undisguised questioning if rapport has been developed. The assurance of confidentiality can also increase participant motivation. You can redesign the questioning process to improve the quality of answers by modifying the administrative process and the response strategy. When drafting the original question, try developing positive, negative, and neutral versions of each type of question. Minimize nonresponses to particular questions by recognizing the sensitivity of certain topics. The final step toward improving survey results is pretesting, the assessment of questions and instruments before the start of a study. Pretesting can allow one to 1) discover ways to increase participant interest, 2) increase the likelihood that participants will remain engaged, 3) discover question content, wording, and sequencing problems, 4) discover target question groups where researcher training is needed, and 5) explore ways improve the overall quality of survey data. Urge your students to review Appendix 13a, especially Exhibit 13a-5, Restructuring Questions, for some insight into overcoming problems.
• ### Mba2216 week 07 08 measurement and data collection forms

1. 1. Research Design :Research Design : Measurement &Measurement & Data Collection FormsData Collection Forms Research Design :Research Design : Measurement &Measurement & Data Collection FormsData Collection Forms MBA2216 BUSINESS RESEARCH PROJECTMBA2216 BUSINESS RESEARCH PROJECT by Stephen Ong Visiting Fellow, Birmingham City University, UK
3. 3. MeasurementMeasurement ConceptsConcepts 13–3
4. 4. 13–4 LEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMES 1. Determine what needs to be measured to address a research question or hypothesis 2. Distinguish levels of scale measurement 3. Know how to form an index or composite measure 4. List the three criteria for good measurement 5. Perform a basic assessment of scale reliability and validity After this lecture, you should be able to
5. 5. 6. Describe how business researchers think of attitudes 7. Identify basic approaches to measuring attitudes 8. Discuss the use of rating scales for measuring attitudes 9. Represent a latent construct by constructing a summated scale 10. Summarize ways to measure attitudes with ranking and sorting techniques 11. Discuss major issues involved in the selection of a measurement scale 13–5 LEARNING OUTCOMESLEARNING OUTCOMES (cont’d)(cont’d) LEARNING OUTCOMESLEARNING OUTCOMES (cont’d)(cont’d)
6. 6. 12. Explain the significance of decisions about questionnaire design and wording 13. Define alternatives for wording open-ended and fixed-alternative questions 14. Summarize guidelines for questions that avoid mistakes in questionnaire design 15. Describe how the proper sequence of questions may improve a questionnaire 16. Discuss how to design a questionnaire layout 17. Describe criteria for pretesting and revising a questionnaire and for adapting it to global markets LEARNING OUTCOMES (cont’d)LEARNING OUTCOMES (cont’d)LEARNING OUTCOMES (cont’d)LEARNING OUTCOMES (cont’d)
7. 7. 11-7 FromFrom InvestigativeInvestigative toto MeasurementMeasurement QuestionsQuestions
8. 8. WHAT DO I MEASURE?WHAT DO I MEASURE?  Before the measurement process can be defined, researchers have to decide exactly what it is that needs to be produced.  The decision statement, corresponding research questions and research hypotheses can be used to decide what concepts need to be measured.  Measurement is the process of describing some property of a phenomenon of interest usually by assigning numbers in a reliable and valid way.  When numbers are used, the researcher must have a rule for assigning a number to an observation in a way that provides an accurate description.  All measurement, particularly in the social sciences, contains error. 13–8
9. 9. WHAT DO I MEASURE?WHAT DO I MEASURE? (cont’d)(cont’d) Concepts A researcher has to know what to measure before knowing how to measure something. A concept is a generalized idea that represents something of meaning. Concepts such as age, sex, education and number of children are relatively concrete properties and present few problems in either definition or measurement. Concepts such as brand loyalty, corporate culture, and so on are more abstract and are more difficult to both define and measure. 13–9
10. 10. WHAT DO I MEASURE?WHAT DO I MEASURE? (cont’d)(cont’d) Operational Definitions Researchers measure concepts through a process known as operationalization, which is a process that involves identifying scales that correspond to variance in the concept. Scales provide a range of values that correspond to different values in the concept being measured. Scales provide correspondence rules that indicate that a certain value on a scale corresponds to some true value of a concept, hopefully in a truthful way. 13–10
11. 11. WHAT DO I MEASURE? (cont’d)WHAT DO I MEASURE? (cont’d) Operational Definitions (cont’d) Variables  Researchers use variance in concepts to make diagnoses.  Variables capture different concept values.  Scales capture variance in concepts and as such, the scales provide the researcher’s variables.  For practical purposes, once a research project is underway, there is little difference between a concept and a variable.
12. 12. WHAT DO I MEASURE?WHAT DO I MEASURE? (cont’d)(cont’d) Operational Definitions (cont’d) Constructs  Sometimes a single variable cannot capture a concept alone.  Using multiple variables to measure one concept can often provide a more complete account of some concept than could any single variable.  A construct is a term used for concepts that are measured with multiple variables.  Can be very helpful in operationlizing a concept. 13–12
13. 13. EXHIBIT 13.EXHIBIT 13.33 Susceptibility to Interpersonal Influence: An Operational DefinitionSusceptibility to Interpersonal Influence: An Operational Definition
14. 14. 11-14 MeasurementMeasurement SelectSelect measurable phenomenameasurable phenomena Develop a set ofDevelop a set of mapping rulesmapping rules Apply the mapping ruleApply the mapping rule to each phenomenonto each phenomenon
15. 15. 11-15 Characteristics of MeasurementCharacteristics of Measurement
16. 16. 11-16 Types of ScalesTypes of Scales OrdinalOrdinal intervalinterval NominalNominal RatioRatio
17. 17. 11-17 Levels of MeasurementLevels of Measurement OrdinalOrdinal intervalinterval RatioRatio NominalNominalNominalNominal ClassificationClassification
18. 18. 11-18 Nominal ScalesNominal Scales Mutually exclusiveMutually exclusive andand Collectively exhaustiveCollectively exhaustive categoriescategories Exhibits onlyExhibits only classificationclassification
19. 19. 11-19 Levels of MeasurementLevels of Measurement OrdinalOrdinalOrdinalOrdinal intervalinterval RatioRatio NominalNominal ClassificationClassification OrderOrder ClassificationClassification
20. 20. 11-20 Ordinal ScalesOrdinal Scales • Characteristics ofCharacteristics of nominal scalenominal scale • OrderOrder • Implies greater thanImplies greater than or less thanor less than
21. 21. 11-21 Levels of MeasurementLevels of Measurement OrdinalOrdinal IntervalIntervalIntervalInterval RatioRatio NominalNominal ClassificationClassification OrderOrder ClassificationClassification OrderOrder ClassificationClassification DistanceDistance
22. 22. 11-22 Interval ScalesInterval Scales Characteristics ofCharacteristics of nominal and ordinalnominal and ordinal scalesscales Equality of interval.Equality of interval. Equal distanceEqual distance between numbersbetween numbers
23. 23. 11-23 Levels of MeasurementLevels of Measurement OrdinalOrdinal intervalinterval RatioRatioRatioRatio NominalNominal ClassificationClassification OrderOrder ClassificationClassification OrderOrder ClassificationClassification DistanceDistance Natural OriginNatural Origin OrderOrder ClassificationClassification DistanceDistance
24. 24. 11-24 Ratio ScalesRatio Scales Characteristics ofCharacteristics of nominal, ordinal,nominal, ordinal, interval scalesinterval scales Absolute zeroAbsolute zero
25. 25. Levels of Scale MeasurementLevels of Scale Measurement  The level of scale measurement is important because it determines the mathematical comparisons that are allowed.  The four levels of scale measurement are:
26. 26. 13–26 Levels of Scale MeasurementLevels of Scale Measurement (cont’d)(cont’d)  Nominal  Assigns a value to an object for identification or classification purposes.  Most elementary level of measurement.  Ordinal  Ranking scales allowing things to be arranged based on how much of some concept they possible.  Have nominal properties.
27. 27. 13–27 Levels of Scale MeasurementLevels of Scale Measurement (cont’d)(cont’d)  Interval  Capture information about differences in quantities of a concept.  Have both nominal and ordinal properties.  Ratio  Highest form of measurement.  Have all the properties of interval scales with the additional attribute of representing absolute quantities.  Absolute zero.
28. 28. EXHIBIT 13.EXHIBIT 13.44 Nominal, Ordinal, Interval, and Ratio Scales Provide DifferentNominal, Ordinal, Interval, and Ratio Scales Provide Different InformationInformation
29. 29. EXHIBIT 13.EXHIBIT 13.55 Facts About the Four Levels of ScalesFacts About the Four Levels of Scales
30. 30. 12-30 Measurements are RelativeMeasurements are Relative “Any measurement must take into account the position of the observer. There is no such thing as measurement absolute, there is only measurement relative.” Jeanette Winterson journalist and author
31. 31. 12-31 The Scaling ProcessThe Scaling Process
32. 32. 12-32 Nature of AttitudesNature of Attitudes Cognitive I think oatmeal is healthier than corn flakes for breakfast. Affective Behavioural I hate corn flakes. I intend to eat more oatmeal for breakfast.
33. 33. 12-33 Improving PredictabilityImproving Predictability Reference groups Reference groups Multiple measures Multiple measures FactorsFactors StrongStrong Specific Basis DirectDirect
34. 34. 12-34 Measurement ScalesMeasurement Scales “All survey questions must be actionable if you want results.” Frank Schmidt, senior scientist The Gallup Organization
35. 35. 12-35 Selecting aSelecting a Measurement ScaleMeasurement Scale Research objectives Response types Data properties Number of dimensions Forced or unforced choices Balanced or unbalanced Rater errors Number of scale points
36. 36. 12-36 Response TypesResponse Types Rating scaleRating scale Ranking scaleRanking scale CategorizationCategorization SortingSorting
37. 37. 12-37 Number of DimensionsNumber of Dimensions Unidimensional Multi-dimensional
38. 38. 12-38 Balanced or UnbalancedBalanced or Unbalanced Very badVery bad BadBad Neither good norNeither good nor badbad GoodGood Very goodVery good PoorPoor FairFair GoodGood Very goodVery good ExcellentExcellent How good an actress is Angelina Jolie?
41. 41. 12-41 Rater ErrorsRater Errors Error of central tendency Error of leniency •Adjust strength of descriptive adjectives •Space intermediate descriptive phrases farther apart •Provide smaller differences in meaning between terms near the ends of the scale •Use more scale points
42. 42. 12-42 Rater ErrorsRater Errors Primacy Effect Recency Effect Reverse order of alternatives periodically or randomly
43. 43. 12-43 Rater ErrorsRater Errors Halo Effect • Rate one trait at a time • Reveal one trait per page • Reverse anchors periodically
44. 44. ATTITUDES AS HYPOTHETICALATTITUDES AS HYPOTHETICAL CONSTRUCTSCONSTRUCTS  Attitude  An enduring disposition to consistently respond in a given manner to various aspects of the world.  Components of attitudes:  Affective Component  The feelings or emotions toward an object  Cognitive Component  Knowledge and beliefs about an object  Behavioural Component  Predisposition to action  Intentions  Behavioural expectations
45. 45. Techniques for MeasuringTechniques for Measuring AttitudesAttitudes  Ranking  Requiring the respondent to rank order objects in overall performance on the basis of a characteristic or stimulus.  Rating  Asking the respondent to estimate the magnitude of a characteristic, or quality, that an object possesses by indicating on a scale where he or she would rate an object.
46. 46. 14–46 Techniques for MeasuringTechniques for Measuring Attitudes (cont’d)Attitudes (cont’d)  Sorting  Presenting the respondent with several concepts typed on cards and requiring the respondent to arrange the cards into a number of piles or otherwise classify the concepts.  Choice  Asking a respondent to choose one alternative from among several alternatives; it is assumed that the chosen alternative is preferred over the others.
47. 47. Attitude Rating ScalesAttitude Rating Scales  Simple Attitude Scale  Requires that an individual agree/disagree with a statement or respond to a single question.  This type of self-rating scale classifies respondents into one of two categories (e.g., yes or no).  Example: THE PRESIDENT SHOULD RUN FOR RE-ELECTION _______ AGREE ______ DISAGREE
48. 48. 12-48 Simple Category ScaleSimple Category Scale I plan to purchase a MindWriter laptop in the 12 months.  Yes  No
49. 49. Attitude Rating Scales (cont’d)Attitude Rating Scales (cont’d)  Category Scale  A more sensitive measure than a simple scale in that it can have more than two response categories.  Question construction is an extremely important factor in increasing the usefulness of these scales.  Example: How important were the following in your decision to visit San Diego? (check one for each item) VERY SOMEWHAT NOT TOO IMPORTANT IMPORTANT IMPORTANT CLIMATE ___________ ___________ ___________ COST OF TRAVEL ___________ ___________ ___________ FAMILY ORIENTED ___________ ___________ ___________ EDUCATIONAL/HISTORICAL ASPECTS ___________ ___________ ___________ FAMILIARITY WITH AREA ___________ ___________ ___________
50. 50. EXHIBIT 14.EXHIBIT 14.11 Selected Category ScalesSelected Category Scales
51. 51. 12-51 Multiple-Choice,Multiple-Choice, Single-Response ScaleSingle-Response Scale What newspaper do you read most often for financial news?  East City Gazette  West City Tribune  Regional newspaper  National newspaper  Other (specify:_____________)
52. 52. 12-52 Multiple-Choice,Multiple-Choice, Multiple-Response ScaleMultiple-Response Scale What sources did you use when designing your new home? Please check all that apply.  Online planning services  Magazines  Independent contractor/builder  Designer  Architect  Other (specify:_____________)
53. 53. 12-53 Likert ScaleLikert Scale The Internet is superior to traditional libraries for comprehensive searches.  Strongly disagree  Disagree  Neither agree nor disagree  Agree  Strongly agree
54. 54. Attitude Rating Scales (cont’d)Attitude Rating Scales (cont’d)  Likert Scale  A popular means for measuring attitudes.  Respondents indicate their own attitudes by checking how strongly they agree or disagree with statements.  Typical response alternatives: “strongly agree,” “agree,” “uncertain,” “disagree,” and “strongly disagree.”  Example: It is more fun to play a tough, competitive tennis match than to play an easy one. ___Strongly Agree ___Agree ___Not Sure ___Disagree ___Strongly Disagree
55. 55. EXHIBIT 14.EXHIBIT 14.22 Likert Scale Items for Measuring Attitudes toward Patients’Likert Scale Items for Measuring Attitudes toward Patients’ Interaction with a Physician’s Service StaffInteraction with a Physician’s Service Staff
56. 56. 12-56 Semantic DifferentialSemantic Differential
57. 57. 14–57 Attitude Rating Scales (cont’d)Attitude Rating Scales (cont’d)  Semantic Differential  A series of seven-point rating scales with bipolar adjectives, such as “good” and “bad,” anchoring the ends (or poles) of the scale.  A weight is assigned to each position on the scale. Traditionally, scores are 7, 6, 5, 4, 3, 2, 1, or +3, +2, +1, 0, -1, -2, -3.  Example: ExcitingExciting ___ : ___ : ___ : ___ : ___ : ___ : ___ Calm___ : ___ : ___ : ___ : ___ : ___ : ___ Calm InterestingInteresting ___ : ___ : ___ : ___ : ___ : ___ : ___ Dull___ : ___ : ___ : ___ : ___ : ___ : ___ Dull SimpleSimple ___ : ___ : ___ : ___ : ___ : ___ : ___ Complex___ : ___ : ___ : ___ : ___ : ___ : ___ Complex PassivePassive ___ : ___ : ___ : ___ : ___ : ___ : ___ Active___ : ___ : ___ : ___ : ___ : ___ : ___ Active
58. 58. EXHIBIT 14.EXHIBIT 14.33 Semantic Differential Scales for Measuring Attitudes TowardSemantic Differential Scales for Measuring Attitudes Toward SupermarketsSupermarkets
59. 59. 12-59 Adapting SD ScalesAdapting SD Scales Convenience of Reaching the Store from Your Location Nearby ___: ___: ___: ___: ___: ___: ___: Distant Short time required to reach store ___: ___: ___: ___: ___: ___: ___: Long time required to reach store Difficult drive ___: ___: ___: ___: ___: ___: ___: Easy Drive Difficult to find parking place ___: ___: ___: ___: ___: ___: ___: Easy to find parking place Convenient to other stores I shop ___: ___: ___: ___: ___: ___: ___: Inconvenient to other stores I shop Products offered Wide selection of different kinds of products ___: ___: ___: ___: ___: ___: ___: Limited selection of different kinds of products Fully stocked ___: ___: ___: ___: ___: ___: ___: Understocked Undependable products ___: ___: ___: ___: ___: ___: ___: Dependable products High quality ___: ___: ___: ___: ___: ___: ___: Low quality Numerous brands ___: ___: ___: ___: ___: ___: ___: Few brands Unknown brands ___: ___: ___: ___: ___: ___: ___: Well-known brands
60. 60. 12-60 SD Scale for Analyzing ActorSD Scale for Analyzing Actor CandidatesCandidates
61. 61. 12-61 Graphic of SD AnalysisGraphic of SD Analysis
62. 62. Other Scale Types (cont’d)Other Scale Types (cont’d)  Image Profile  A graphic representation of semantic differential data for competing brands, products, or stores to highlight comparisons.  Because the data are assumed to be interval, either the arithmetic mean or the median will be used to compare the profile of one product, brand, or store with that of a competing product, brand, or store.
63. 63. EXHIBIT 14.EXHIBIT 14.44 Image Profiles of Commuter Airlines versus Major AirlinesImage Profiles of Commuter Airlines versus Major Airlines
64. 64. 12-64 Numerical ScaleNumerical Scale
65. 65. Attitude Rating Scales (cont’d)Attitude Rating Scales (cont’d)  Numerical Scales  Scales that have numbers as response options, rather than “semantic space” or verbal descriptions, to identify categories (response positions).  In practice, researchers have found that a scale with numerical labels for intermediate points on the scale is as effective a measure as the true semantic differential.  Example:  Now that you’ve had your automobile for about one year, please tell us how satisfied you are with your Ford Taurus. Extremely Dissatisfied 1 2 3 4 5 6 7 Extremely Satisfied
66. 66. 12-66 Multiple Rating List ScalesMultiple Rating List Scales “Please indicate how important or unimportant each service characteristic is:” IMPORTANT UNIMPORTANT Fast, reliable repair 7 6 5 4 3 2 1 Service at my location 7 6 5 4 3 2 1 Maintenance by manufacturer 7 6 5 4 3 2 1 Knowledgeable technicians 7 6 5 4 3 2 1 Notification of upgrades 7 6 5 4 3 2 1 Service contract after warranty 7 6 5 4 3 2 1
67. 67. 12-67 Stapel ScalesStapel Scales
68. 68. Other Scale Types (cont’d)Other Scale Types (cont’d)  Stapel Scale  Uses a single adjective as a substitute for the semantic differential when it is difficult to create pairs of bipolar adjectives.  Tends to be easier to conduct and administer than a semantic differential scale.
69. 69. EXHIBIT 14.EXHIBIT 14.55 A Stapel Scale for Measuring a Store’s ImageA Stapel Scale for Measuring a Store’s Image
70. 70. 12-70 Constant-Sum ScalesConstant-Sum Scales
71. 71. Other Scale Types (cont’d)Other Scale Types (cont’d)  Constant-Sum Scale  Respondents are asked to divide a constant sum to indicate the relative importance of attributes.  Respondents often sort cards, but the task may also be a rating task (e.g., indicating brand preference).  Example:  Divide 100 points among each of the following brands according to your preference for the brand:  Brand A _________  Brand B _________  Brand C _________
72. 72. 12-72 Graphic Rating ScalesGraphic Rating Scales
74. 74. EXHIBIT 14.EXHIBIT 14.88 Graphic Rating Scale with Picture ResponseGraphic Rating Scale with Picture Response Categories Stressing Visual CommunicationCategories Stressing Visual Communication
75. 75. Other Scale Types (cont’d)Other Scale Types (cont’d)  Graphic Rating Scale  A measure of attitude that allows respondents to rate an object by choosing any point along a graphic continuum.  Advantage:  Allows the researcher to choose any interval desired for scoring purposes.  Disadvantage:  There are no standard answers.
76. 76. EXHIBIT 14.EXHIBIT 14.66 Graphic Rating ScaleGraphic Rating Scale
78. 78. 12-78 Ranking ScalesRanking Scales Paired-comparison scale Forced ranking scale Comparative scale
79. 79. RankingRanking  An ordinal scale may be developed by asking respondents to rank order (from most preferred to least preferred) a set of objects or attributes.  Paired comparisons  Sorting
80. 80. 12-80 Paired-Comparison ScalePaired-Comparison Scale
81. 81. Paired ComparisonPaired Comparison  A measurement technique that involves presenting the respondent with two objects and asking the respondent to pick the preferred object; more than two objects may be presented, but comparisons are made in pairs.  Number of comparisons = [(n)(n-1)/2]  Example: I would like to know your overall opinion of two brands of adhesive bandages. They are MedBand and Super-Aid. Overall, which of these two brands— MedBand or Super-Aid—do you think is the better one? Or are both the same? MedBand is better _____ Super-Aid is better _____ They are the same _____
82. 82. 12-82 Forced Ranking ScaleForced Ranking Scale
83. 83. 12-83 Comparative ScaleComparative Scale
84. 84. 12-84 SortingSorting
85. 85. SortingSorting  Require that respondents indicate their attitudes or beliefs by arranging items on the basis of perceived similarity or some other attribute.  Example: Here is a sheet that lists several airlines. Next to the name of each airline is a pocket. Here are ten cards. I would like you to put these cards in the pockets next to the airlines you would prefer to fly on your next trip. Assume that all of the airlines fly to wherever you would choose to travel. You can put as many cards as you want next to an airline, or you can put no cards next to an airline. Cards American Airlines _____ Delta Airlines _____ United Airlines _____ Southwest Airlines _____ Northwest Airlines _____
86. 86. 12-86 Example : MindWriter ScalingExample : MindWriter Scaling Likert Scale The problem that prompted service/repair was resolved Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree 1 2 3 4 5 Numerical Scale (MindWriter’s Favourite) To what extent are you satisfied that the problem that prompted service/repair was resolved? Very Dissatisfied Very Satisfied 1 2 3 4 5 Hybrid Expectation Scale Resolution of the problem that prompted service/repair. Met Few Expectations Met Some Expectations Met Most Expectations Met All Expectations Exceeded Expectations 1 2 3 4 5
87. 87. 12-87 Ideal Scalogram PatternIdeal Scalogram Pattern Item Participant Score 2 4 1 3 X X X X 4 __ X X X 3 __ __ X X 2 __ __ __ X 1 __ __ __ __ 0
88. 88. Measuring Behavioural IntentionMeasuring Behavioural Intention  Behavioural Component  The behavioural expectations (expected future actions) of an individual toward an attitudinal object.  Example:  How likely is it that you will purchase a Honda Fit?  I definitely will buy  I probably will buy  I might buy  I probably will not buy  I definitely will not buy
89. 89. Measuring Behavioural IntentionMeasuring Behavioural Intention (cont’d)(cont’d)  Behavioural Differential  A rating scale instrument similar to a semantic differential, developed to measure the behavioural intentions of subjects toward future actions.  A description of the object to be judged is placed on the top of a sheet, and the subjects indicate their behavioural intentions toward this object on a series of scales.  Example: A 25 year-old woman sales representative Would ___ : ___ : ___ : ___ : ___ : ___ : ___ : Would Not ask this person for advice.
90. 90. Mathematical and StatisticalMathematical and Statistical Analysis of ScalesAnalysis of Scales  Although you can put numbers into formulas and perform calculations with almost any numbers, the researcher has to know the meaning behind the numbers before useful conclusions can be drawn (e.g., averaging the numbers used to identify school busses is meaningless).
91. 91. Mathematical and StatisticalMathematical and Statistical Analysis of Scales (cont’d)Analysis of Scales (cont’d)  Discrete Measures  Discrete measures are those that take on only one of a finite number of values.  Most often used to represent a classificatory variable and thus do not represent intensity of measures, only membership.  Common discrete scales include any yes-no response, matching, colour choice or practically all scales that involve selecting from a small number of categories.  Nominal and ordinal scales are discrete measures.  The central tendency of discrete measures is best captured by the mode (i.e., most frequent level).
92. 92. 13–92 Mathematical and StatisticalMathematical and Statistical Analysis of Scales (cont’d)Analysis of Scales (cont’d)  Continuous Measures  Continuous measures are those assigning values anywhere along some scale range in a place that corresponds to the intensity of some concept.  Ratio measures are continuous measures.  Strictly speaking, interval scales are not necessarily continuous.  e.g., Likert item ranging from 1=strongly disagree to 5=strongly agree.  This is a discrete scale because only the values 1, 2, 3, 4, or 5 can be assigned.
93. 93. Index MeasuresIndex Measures  Attributes  Single characteristics or fundamental features that pertain to an object, person, or issue.  Index Measures  Assign a value based on how much of the concept being measured is associated with an observation.  Indexes often are formed by putting several variables together.  Composite Measures  Assign a value to an observation based on a mathematical derivation of multiple variables.
94. 94. Computing Scale ValuesComputing Scale Values  Summated Scale  A scale created by simply summing (adding together) the response to each item making up the composite measure.  Reverse Coding  Means that the value assigned for a response is treated oppositely from the other items.
95. 95. EXHIBIT 13.EXHIBIT 13.66 Computing a Composite ScaleComputing a Composite Scale
96. 96. Three Criteria for GoodThree Criteria for Good MeasurementMeasurement SensitivitySensitivitySensitivitySensitivity ReliabilityReliabilityReliabilityReliability ValidityValidityValidityValidity GoodGood MeasurementMeasurement GoodGood MeasurementMeasurement
97. 97. ReliabilityReliability  Reliability  Reliability is an indicator of a measure’s internal consistency.  A measure is reliable when different attempts at measuring something converge on the same result.  When the measuring process provides reproducible results, the measuring instrument is reliable.  Internal Consistency  Represents a measure’s homogeneity or the extent to which each indicator of a concept converges on some common meaning.  Measured by correlating scores on subsets of items making up a scale.
98. 98. Internal ConsistencyInternal Consistency  Split-half Method  Assessing internal consistency by checking the results of one-half of a set of scaled items against the results from the other half.  The two scale halves should correlate highly.  They should also produce similar scores.
99. 99. Internal Consistency (cont’d)Internal Consistency (cont’d)  Coefficient alpha (α)  The most commonly applied estimate of a multiple item scale’s reliability.  Represents the average of all possible split-half reliabilities for a construct.  The coefficient demonstrates whether or not the different items converge.  Ranges in value from 0 (no consistency) to 1 (complete consistency).  Generally, scales with a coefficient α:
100. 100. Test-Retest ReliabilityTest-Retest Reliability  Test-retest Method  Administering the same scale or measure to the same respondents at two separate points in time to test for stability.  Represents a measure’s repeatability.  Problems:  The pre-measure, or first measure, may sensitize the respondents and subsequently influence the results of the second measure.  Time effects that produce changes in attitude or other maturation of the subjects.
101. 101. ValidityValidity  ValidityValidity  Good measures should be both precise (i.e., reliable) and accurate (i.e., valid).  Validity is the accuracy of a measure or the extent to which a score truthfully represents a concept. Does a scale measure what was intended to be measured?  When a measure lacks validity, any conclusions based on that measure are also likely to be faulty.
102. 102. Validity : Face, Content …Validity : Face, Content …  Establishing Validity:  The four basic approaches to establishing validity are face validity, content validity, criterion validity, and construct validity. Face validityFace validity refers to the subjective agreement among professionals that a scale logically reflects the concept being measured. Content validityContent validity refers to the degree that a measure covers the domain of interest.
103. 103. Validity : Criterion …Validity : Criterion …  Criterion validityCriterion validity addresses the question: “Does my measure correlate with measures“Does my measure correlate with measures of similar concepts or known quantities?”of similar concepts or known quantities?” May be classified as either concurrent validity or predictive validity depending on the time sequence in which the new measurement scale and the criterion measure are correlated. If measures taken at the same time 􀃎 concurrent validity. If measures taken at different times 􀃎 predictive validity.
104. 104. Validity : Construct …Validity : Construct …  Construct validityConstruct validity exists when a measure reliably measures and truthfully represents a unique concept and consists of several components:  Face and Content validityFace and Content validity  Convergent validityConvergent validity – another way of expressing internal consistency; highly reliable scales contain convergent validity.  Criterion validityCriterion validity  Discriminant validityDiscriminant validity – represents how unique or distinct is a measure; a scale should not correlate too highly (i.e., above .75) with a measure of a different construct.
105. 105. EXHIBIT 13.EXHIBIT 13.77 Reliability and Validity on TargetReliability and Validity on Target
106. 106. SensitivitySensitivity  Sensitivity  A measurement instrument’s ability to accurately measure variability in stimuli or responses.  Generally increased by adding more response points or adding scale items.
107. 107. Selecting a Measurement ScaleSelecting a Measurement Scale  Some Practical Questions:  Is a ranking, sorting, rating, or choice technique best?  Should a monadic or a comparative scale be used?  What type of category labels, if any, will be used for the rating scale?  How many scale categories or response positions are needed to accurately measure an attitude?  Should a balanced or unbalanced rating scale be chosen?  Should a scale that forces a choice among predetermined options be used?  Should a single measure or an index measure be used?
108. 108. Selecting a Measurement ScaleSelecting a Measurement Scale (cont’d)(cont’d)  Monadic Rating Scale  Asks about a single concept in isolation.  The respondent is not given a specific frame of reference.  Example: Now that you’ve had your automobile for about 1 year, please tell us how satisfied you are with its engine power and pickup.
109. 109. Please indicate how the amount of authority in your present position compares with the amount of authority that would be ideal for this position. TOO MUCH  ABOUT RIGHT  TOO LITTLE  Selecting a Measurement ScaleSelecting a Measurement Scale (cont’d)(cont’d)  Comparative Rating Scale  Asks respondents to rate a concept in comparison with a benchmark explicitly used as a frame of reference.  Example:
110. 110. Selecting a MeasurementSelecting a Measurement Scale (cont’d)Scale (cont’d)  What Type of Category Labels, If Any?  Verbal labels for response categories help respondents better understand the response positions.  The maturity and educational levels of the respondents will influence the labeling decision.  How Many Scale Categories or Response Positions?  Five to eight points are optimal for sensitivity.  The researcher must determine the number of positions that is best for the specific project.
111. 111. Selecting a Measurement ScaleSelecting a Measurement Scale (cont’d)(cont’d)  Balanced Rating Scale  A fixed-alternative rating scale with an equal number of positive and negative categories; a neutral point or point of indifference is at the center of the scale.  Example:
112. 112. Selecting a Measurement ScaleSelecting a Measurement Scale (cont’d)(cont’d)  Unbalanced Rating Scale  A fixed-alternative rating scale that has more response categories at one end than the other resulting in an unequal number of positive and negative categories.  Example:
113. 113. Selecting a MeasurementSelecting a Measurement Scale (cont’d)Scale (cont’d)  Forced-choice Rating Scale  A fixed-alternative rating scale that requires respondents to choose one of the fixed alternatives.  Non-forced Choice Scale  A fixed-alternative rating scale that provides a “no opinion” category or that allows respondents to indicate that they cannot say which alternative is their choice.
114. 114. Selecting a MeasurementSelecting a Measurement Scale (cont’d)Scale (cont’d)  Factors affecting the choice of using a single measure or an index measure:  The complexity of the issue to be investigated.  The number of dimensions the issue contains.  Whether individual attributes of the stimulus are part of a holistic attitude or are seen as separate items.  The researcher’s conceptual (problem) definition will be helpful in making this choice.
115. 115. 13-115 Overall Flowchart for InstrumentOverall Flowchart for Instrument DesignDesign
116. 116. 13-116 Flowchart for InstrumentFlowchart for Instrument Design Phase 1Design Phase 1
117. 117. Strategic Concerns inStrategic Concerns in Instrument DesignInstrument Design What type of scale is needed? What communication approach will be used? Should the questions be structured? Should the questioning be disguised?
118. 118. Technology AffectsTechnology Affects Questionnaire DevelopmentQuestionnaire Development WebSurveyor used to write an instrument. Write questionnairesWrite questionnaires more quicklymore quickly Create visually drivenCreate visually driven instrumentsinstruments Eliminate manualEliminate manual data entrydata entry Save time in dataSave time in data analysisanalysis
119. 119. 13-119 Disguising Study ObjectivesDisguising Study Objectives Situations where disguise is unnecessary Situations where disguise is unnecessary Willingly shared, Conscious-level information Reluctantly shared, Conscious-level information Knowable, Limited-conscious-level information Subconscious-level information
120. 120. Dummy Table forDummy Table for American Eating HabitsAmerican Eating Habits Age Use of Convenience Foods Always Use Use Frequently Use Sometime s Rarely Use Never Use 18-24 25-34 35-44 55-64 65+
121. 121. 13-121 Flowchart for Instrument DesignFlowchart for Instrument Design Phase 2Phase 2
122. 122. 13-122 Question CategoriesQuestion Categories and Structureand Structure Administrative Target Classification
123. 123. Engagement = ConvenienceEngagement = Convenience “Participants are becoming more and more aware of the value of their time. The key to maintaining a quality dialogue with them is to make it really convenient for them to engage, whenever and wherever they want.” Tom Anderson managing partner Anderson Analytics
124. 124. 13-124 Question ContentQuestion Content Should this question be asked? Is the question of proper scope and coverage? Can the participant adequately answer this question as asked? Will the participant willingly answer this question as asked?
125. 125. 13-125 Question WordingQuestion Wording CriteriaCriteria Shared vocabulary Single meaning Misleading assumptions Adequate alternatives Personalized Biased
126. 126. 13-126 Response StrategyResponse Strategy FactorsFactors Objectives of the study Participant’s level of information Degree to which participants have thought through topic Ease and clarity with which participant communicates Participant’s motivation to share
127. 127. 13-127 Free-Response StrategyFree-Response Strategy What factors influenced your enrollment in Metro U? ____________________________________________ ____________________________________________
128. 128. 13-128 Dichotomous ResponseDichotomous Response StrategyStrategy Did you attend the “A Day at College” program at Metro U? Yes No
129. 129. 13-129 Multiple Choice ResponseMultiple Choice Response StrategyStrategy Which one of the following factors was most influential in your decision to attend Metro U? Good academic standing Specific program of study desired Enjoyable campus life Many friends from home High quality of faculty
130. 130. 13-130 Checklist Response StrategyChecklist Response Strategy Which of the following factors influenced your decision to enroll in Metro U? (Check all that apply.)  Tuition cost  Specific program of study desired  Parents’ preferences  Opinion of brother or sister  Many friends from home attend  High quality of faculty
131. 131. 13-131 Rating Response StrategyRating Response Strategy Strongly influential Somewhat influential Not at all influential Good academic reputation    Enjoyable campus life    Many friends    High quality faculty    Semester calendar   
132. 132. 13-132 RankingRanking Please rank-order your top three factors from the following list based on their influence in encouraging you to apply to Metro U. Use 1 to indicate the most encouraging factor, 2 the next most encouraging factor, etc. _____ Opportunity to play collegiate sports _____ Closeness to home _____ Enjoyable campus life _____ Good academic reputation _____ High quality of faculty
133. 133. 13-133 Summary of Scale TypesSummary of Scale Types Type Restrictions Scale Items Data Type Rating Scales Simple Category Scale • Needs mutually exclusive choices One or more Nominal Multiple Choice Single-Response Scale • Needs mutually exclusive choices • May use exhaustive list or ‘other’ Many Nominal Multiple Choice Multiple-Response Scale (checklist) • Needs mutually exclusive choices • Needs exhaustive list or ‘other’ Many Nominal Likert Scale • Needs definitive positive or negative statements with which to agree/disagree One or more Ordinal Likert-type Scale •Needs definitive positive or negative statements with which to agree/disagree One or more Ordinal
134. 134. 13-134 Summary of Scale TypesSummary of Scale Types Type Restrictions Scale Items Data Type Rating Scales Numerical Scale •Needs concepts with standardized meanings; •Needs number anchors of the scale or end-points •Score is a measurement of graphical space One or many Ordinal or Interval Multiple Rating List Scale •Needs words that are opposites to anchor the end-points on the verbal scale Up to 10 Ordinal Fixed Sum Scale •Participant needs ability to calculate total to some fixed number, often 100. Two or more Interval or Ratio
135. 135. 13-135 Summary of Scale TypesSummary of Scale Types Type Restrictions Scale Items Data Type Rating Scales Stapel Scale •Needs verbal labels that are operationally defined or standard. One or more Ordinal or Interval Graphic Rating Scale •Needs visual images that can be interpreted as positive or negative anchors •Score is a measurement of graphical space from one anchor. One or more Ordinal (Interval, or Ratio)
136. 136. 13-136 Summary of Scale TypesSummary of Scale Types Type Restrictions Scale Items Data Type Ranking Scales Paired Comparison Scale • Number is controlled by participant’s stamina and interest. Up to 10 Ordinal Forced Ranking Scale • Needs mutually exclusive choices. Up to 10 Ordinal or Interval Comparative Scale • Can use verbal or graphical scale. Up to 10 Ordinal
137. 137. 13-137 Internet SurveyInternet Survey Scale OptionsScale Options
138. 138. 13-138 Internet SurveyInternet Survey Scale OptionsScale Options
139. 139. 13-139 Internet SurveyInternet Survey Scale OptionsScale Options
140. 140. 13-140 Sources of QuestionsSources of Questions Handbook ofHandbook of Marketing ScalesMarketing Scales The Gallup PollThe Gallup Poll Cumulative IndexCumulative Index Measures ofMeasures of Personality andPersonality and Social-Social- PsychologicalPsychological AttitudesAttitudes Measures ofMeasures of Political AttitudesPolitical Attitudes Index to InternationalIndex to International Public OpinionPublic Opinion Sourcebook of HarrisSourcebook of Harris National SurveysNational Surveys Marketing ScalesMarketing Scales HandbookHandbook American SocialAmerican Social Attitudes DataAttitudes Data SourcebookSourcebook
141. 141. 13-141 Flowchart for Instrument Design Phase 3Flowchart for Instrument Design Phase 3
142. 142. 13-142 Guidelines forGuidelines for Question SequencingQuestion Sequencing Interesting topics earlyInteresting topics early Simple topics earlySimple topics early Sensitive questions laterSensitive questions later Classification questions laterClassification questions later Transition between topicsTransition between topics Reference changes limitedReference changes limited
143. 143. Illustrating the Funnel ApproachIllustrating the Funnel Approach How do you think this country is gettingHow do you think this country is getting along in its relations with otheralong in its relations with other countries?countries? How do you think we are doing in ourHow do you think we are doing in our relations with Iran?relations with Iran? Do you think we ought to be dealing withDo you think we ought to be dealing with Iran differently than we are now?Iran differently than we are now? (If yes) What should we be doing(If yes) What should we be doing differently?differently? Some people say we should get tougherSome people say we should get tougher with Iran and others think we are toowith Iran and others think we are too tough as it is; how do you feel about it?tough as it is; how do you feel about it?
144. 144. 13-144 Branching QuestionBranching Question
145. 145. 13-145 Components of QuestionnairesComponents of Questionnaires
146. 146. Example :MindWriter SurveyExample :MindWriter Survey
147. 147. 13-147 Overcoming InstrumentOvercoming Instrument ProblemsProblems Build rapportBuild rapport Redesign question processRedesign question process Explore alternativesExplore alternatives Use other methodsUse other methods PretestPretest
148. 148. Questionnaire Quality and Design:Questionnaire Quality and Design: Basic ConsiderationsBasic Considerations  Questionnaire design is one of the most critical stages in the survey research process.  A questionnaire (survey) is only as good as the questions it asks—ask a bad question, get bad results.  Composing a good questionnaire appears easy, but it is usually the result of long, painstaking work.  The questions must meet the basic criteria of relevance and accuracy.
149. 149. Decisions in QuestionnaireDecisions in Questionnaire DesignDesign 1. What should be asked? 2. How should questions be phrased? 3. In what sequence should the questions be arranged? 4. What questionnaire layout will best serve the research objectives? 5. How should the questionnaire be pretested? Does the questionnaire need to be revised?
150. 150. What Should Be Asked?What Should Be Asked?  Questionnaire Relevancy  All information collected should address a research question in helping the decision maker in solving the current business problem.  Questionnaire Accuracy  Increasing the reliability and validity of respondent information requires that:  Questionnaires should use simple, understandable, unbiased, unambiguous, and nonirritating words.  Questionnaire design should facilitate recall and motivate respondents to cooperate.  Proper question wording and sequencing to avoid confusion and biased answers.
151. 151. Wording QuestionsWording Questions  Open-ended Response Questions  Pose some problem and ask respondents to answer in their own words.  Advantages:  Are most beneficial in exploratory research, especially when the range of responses is not known.  May reveal unanticipated reactions toward the product.  Are good first questions because they allow respondents to warm up to the questioning process.  Disadvantages:  High cost of administering open-ended response questions.  The possibility that interviewer bias will influence the answer.  Bias introduced by articulate individuals’ longer answers.
152. 152. Wording Questions (cont’d)Wording Questions (cont’d)  Fixed-alternative Questions  Questions in which respondents are given specific, limited-alternative responses and asked to choose the one closest to their own viewpoint.  Advantages:  Require less interviewer skill  Take less time to answer  Are easier for the respondent to answer  Provides comparability of answers  Disadvantages:  Lack of range in the response alternatives  Tendency of respondents to choose convenient alternative
153. 153. Types of Fixed-AlternativeTypes of Fixed-Alternative QuestionsQuestions  Simple-dichotomy (dichotomous) Question  Requires the respondent to choose one of two alternatives (e.g., yes or no).  Determinant-choice Question  Requires the respondent to choose one response from among multiple alternatives (e.g., A, B, or C).  Frequency-determination Question  Asks for an answer about general frequency of occurrence (e.g., often, occasionally, or never).  Checklist Question  Allows the respondent to provide multiple answers to a single question by checking off items.
154. 154. Phrasing Questions for Self-Phrasing Questions for Self- Administered,Telephone, andAdministered,Telephone, and Personal Interview SurveysPersonal Interview Surveys  Influences on Question Phrasing:  The means of data collection—telephone interview, personal interview, self- administered questionnaire—will influence the question format and question phrasing.  Questions for mail, Internet, and telephone surveys must be less complex than those used in personal interviews.  Questionnaires for telephone and personal interviews should be written in a conversational style.
155. 155. EXHIBIT 15.EXHIBIT 15.11 Reducing Question Complexity by Providing Fewer Responses forReducing Question Complexity by Providing Fewer Responses for Telephone InterviewsTelephone Interviews
156. 156. Guidelines for ConstructingGuidelines for Constructing QuestionsQuestions  Avoid complexity: Simpler language is better.  Avoid leading and loaded questions.  Avoid ambiguity: Be as specific as possible.  Avoid double-barreled items.  Avoid making assumptions.  Avoid burdensome questions that may tax the respondent’s memory.  Make certain questions generate variance.
157. 157. What Is the Best QuestionWhat Is the Best Question Sequence?Sequence?  Order bias  Bias caused by the influence of earlier questions in a questionnaire or by an answer’s position in a set of answers.  Funnel technique  Asking general questions before specific questions in order to obtain unbiased responses.  Filter question  A question that screens out respondents who are not qualified to answer a second question.  Pivot question  A filter question used to determine which version of a second question will be asked.
158. 158. EXHIBIT 15.EXHIBIT 15.22 Flow of QuestionsFlow of Questions to Determine theto Determine the Level of PromptingLevel of Prompting Required toRequired to Stimulate RecallStimulate Recall
159. 159. What Is the Best Layout?What Is the Best Layout?  Traditional Questionnaires  Multiple-grid question  Several similar questions arranged in a grid format.  The title of a questionnaire should be phrased carefully:  To capture the respondent’s interest, underline the importance of the research  Emphasize the interesting nature of the study  Appeal to the respondent’s ego  Emphasize the confidential nature of the study  To not bias the respondent in the same way that a leading question might
160. 160. EXHIBIT 15.EXHIBIT 15.33 Layout of a Page from a Telephone QuestionnaireLayout of a Page from a Telephone Questionnaire
161. 161. EXHIBIT 15.EXHIBIT 15.44 Telephone Questionnaire with Skip QuestionsTelephone Questionnaire with Skip Questions
162. 162. EXHIBIT 15.EXHIBIT 15.55 Personal Interview QuestionnairePersonal Interview Questionnaire
163. 163. EXHIBIT 15.EXHIBIT 15.66 Example of a Skip QuestionExample of a Skip Question
164. 164. Internet QuestionnairesInternet Questionnaires  Graphical User Interface (GUI) Software  The researcher can control the background, colours, fonts, and other features displayed on the screen so as to create an attractive and easy-to-use interface between the user and the Internet survey.  Layout Issues  Paging layout - going from screen to screen.  Scrolling layout – entire questionnaire appears on one page and respondent has the ability to scroll down.
165. 165. Internet Questionnaire LayoutInternet Questionnaire Layout  Push Button  A small outlined area, such as a rectangle or an arrow, that the respondent clicks on to select an option or perform a function, such as submit.  Status Bar  A visual indicator that tells the respondent what portion of the survey he or she has completed.  Radio Button  A circular icon, resembling a button, that activates one response choice and deactivates others when a respondent clicks on it.
166. 166. Internet Questionnaire LayoutInternet Questionnaire Layout (cont’d)(cont’d)  Drop-down Box  A space saving device that reveals responses when they are needed but otherwise hides them from view.  Check Boxes  Small graphic boxes, next to an answers, that a respondent clicks on to choose an answer; typically, a check mark or an X appears in the box when the respondent clicks on it.  Open-ended Boxes  Boxes where respondents can type in their own answers to open-ended questions.  Pop-up Boxes  Boxes that appear at selected points and contain information or instructions for respondents.
167. 167. EXHIBIT 15.EXHIBIT 15.77 Question in an Online Screening Survey for Joining a ConsumerQuestion in an Online Screening Survey for Joining a Consumer PanelPanel
168. 168. 15–168 EXHIBIT 15.EXHIBIT 15.88 Alternative Ways of Displaying Internet QuestionsAlternative Ways of Displaying Internet Questions
169. 169. Internet Questionnaire LayoutInternet Questionnaire Layout (cont’d)(cont’d)  Software That Makes Questionnaires Interactive  Variable piping software  Allows variables to be inserted into an Internet questionnaire as a respondent is completing it.  Error trapping software  Controls the flow of an Internet questionnaire.  Forced answering software  Prevents respondents from continuing with an Internet questionnaire if they fail to answer a question.  Interactive help desk  A live, real-time support feature that solves problems or answers questions respondents may encounter in completing the questionnaire.
170. 170. Pretesting and RevisingPretesting and Revising QuestionnairesQuestionnaires  Pretesting Process  Seeks to determine whether respondents have any difficulty understanding the questionnaire and whether there are any ambiguous or biased questions.  Preliminary Tabulation  A tabulation of the results of a pretest to help determine whether the questionnaire will meet the objectives of the research.
171. 171. Designing Questionnaires forDesigning Questionnaires for Global MarketsGlobal Markets  Back Translation  Taking a questionnaire that has previously been translated into another language and having a second, independent translator translate it back to the original language.  A questionnaire developed in one country may be difficult to translate because equivalent language concepts do not exist or because of differences in idiom and vernacular.
172. 172. Further ReadingFurther Reading  COOPER, D.R. AND SCHINDLER, P.S. (2011) BUSINESS RESEARCH METHODS, 11TH EDN, MCGRAW HILL  ZIKMUND, W.G., BABIN, B.J., CARR, J.C. AND GRIFFIN, M. (2010) BUSINESS RESEARCH METHODS, 8TH EDN, SOUTH-WESTERN  SAUNDERS, M., LEWIS, P. AND THORNHILL, A. (2012) RESEARCH METHODS FOR BUSINESS STUDENTS, 6TH EDN, PRENTICE HALL.  SAUNDERS, M. AND LEWIS, P. (2012) DOING RESEARCH IN BUSINESS & MANAGEMENT, FT PRENTICE HALL.