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  • Understand... The purpose and process of exploratory research. The two types and three levels of management decision-related secondary sources. The five types of external information and the factors for evaluating the value of a source and its content.
  • See the text Instructors Manual (downloadable from the text website) for ideas for using this research-generated statistic.
  • Exploration is particularly useful when researchers lack a clear idea of the problems they will meet during the study. Through exploration researchers develop concepts more clearly, establish priorities, develop operational definitions, and improve the final research design. The exploratory phase usually consists of one or more of the search strategies detailed in the slide. Discovery and analysis of secondary data sources include published studies, document analysis, and retrieval of information from organization’s databases. Expert interviews are interviews with those who knowledgeable about the problem or its possible solutions. IDIs are interviews with individuals involved with the problem. Formal or informal group discussions may also be held. Most researchers find a review of secondary sources critical to moving from the management question to the research question.
  • Exhibit 5-1 suggests that exploration of secondary sources can be useful at any stage of the management-research question hierarchy.
  • This slide details the objectives that should be accomplished during the exploratory research phase of a project. Expand understanding of management dilemma Gather background information Identify information that should be gathered Identify sources for and actual questions that might be used Identify sources for and actual sample frames that might be used
  • Generally, the exploration phase will begin with a literature search. A literature search is a review of books, journal articles, and professional literature that relate to the management dilemma. This may also include Web-published material. This slide details the five steps of a literature search. The result of a literature search could be a solution to the management dilemma. If so, no further research is necessary. Otherwise, a research proposal is generated.
  • Information sources are categorized into three levels. Primary sources are original works of research or raw data without interpretation or pronouncements that represent an official opinion or position. Secondary sources are interpretations of primary data. A firm searching for secondary sources can search either internally or externally, as depicted in Exhibit 5-2. This exhibit is shown on the next slide. Tertiary sources are aids to discover primary or secondary sources or an interpretation of a secondary source.
  • Exhibit 5-2 This slide illustrates some of the possible internal and external secondary sources. To verify that students understand each of the types of sources, ask them for examples.
  • The U.S. Government is the world’s largest source of data. This ad promotes access to government data.
  • These are the five types of information sources used most by researchers at this phase of a project. Indexes and bibliographies help one to identify books and journal articles. An index is a secondary data source that helps to identify and locate a single book, journal article, author, etc. from a larger set. A bibliography is an information source that helps locate a single book, article, photograph, etc. Dictionaries are secondary sources that define words, terms, and jargon. Encyclopedias are secondary sources that provide background or historical information about a topic. A handbook is a secondary source used to identify key terms, people, or events relevant to the management dilemma or management question. Directories are reference sources used to identify contact information.
  • A researcher using secondary sources will want to conduct a source evaluation. Marketers should evaluate and select information sources based on five factors. Purpose is the explicit or hidden agenda of the information source. Scope is the breadth or depth of topic coverage, including time period, geographic limitations, and the criteria for information inclusion. Authority is the level of the data (primary, secondary, tertiary) and the credentials of the source author. Audience refers to the characteristics and background of the people or groups for whom the source was created. Format refers to how the information is presented and the degree of ease in locating specific information within the source. Students often accept web-delivered information as of the same quality as electronic databases. One exercise is to have them view a web-site and present an analysis of it using the five factors. Students are often active participants in blogging, so having them use such a site for analysis might open their eyes. Exhibit 5-3 offers several questions to answer when evaluating web sites on the five factors.
  • Exhibit 5-4: Data mining is a type of record analysis. It uses mathematical models to extract meaningful knowledge from integrated databases. This Exhibit discusses the evolution of data mining.
  • Early use of data mining was still being driven by our search for understanding of customers, as noted by the emphasis on marketing’s use of data mining (green bars). Business also does a lot of data mining in search of greater profitability (financial analysis=yellow bar). Operations use was growing (gold bars). Notably absent was Human Resources use of data mining.
  • Exhibit 5-5 The data mining process involves five steps: sample, explore, modify, model, and assess. In the sample step, the researcher decides between census data and sample data. Explore involves identifying relationships with the data. In the third step, data are modeled and/or transformed. In the fourth step, a model is developed that explains the data relationships. Finally, the model is tested for accuracy.
  • Exhibit 4-1 illustrates the research process. This slide focuses on the first stage of the process, clarifying the research question. A useful way to approach the research process is to state the basic dilemma that prompts the research and then try to develop other questions by progressively breaking down the original question into more specific ones. This process can be thought of as the management-research question hierarchy. The process begins at the most general level with the management dilemma. This is usually a symptom of an actual problem, such as rising costs, declining sales, or a large number of defects. Exhibit 3-2 illustrates the formulation of the research question for MindWriter. A management question is a restatement of the manager’s dilemma in question form. A research question is the hypothesis that best states the objective of the research; the question that focuses the researcher’s attention. An investigative question is the question the researcher must answer to satisfactorily answer the research question. A measurement question is the question asked of the participant or the observations that must be recorded.
  • Exhibit 5-6 The management-research question hierarchy process is designed to move the researcher through various levels of questions, each with a specific function within the overall marketing research process. This multi-step process is illustrated in the slide. An example is provided on the following slide. The role of exploration in this process is depicted in Exhibit 3-4, located on Slide 3-9.
  • Exhibit 5-7 Declining sales is one of the most common symptoms serving as a stimulus for a research project. SalePro, a large manufacturer of industrial goods, faces this situation. Exploration 1 reveals that sales should not be declining in the South and Northeast. Environmental factors there are as favorable as in the growing regions. Subsequent exploration leads management to believe that the problem is in one of three areas: salesperson compensation, product formulation, or trade advertising. Further exploration (4) has SalePro management narrowing the focus of its research to alternative ways to alter the sales compensation system, which (5) leads to a survey of all sales personnel in the affected regions.
  • This slide depicts how exploration leads back into the formulation of management questions and research questions. Examples of management questions are provided on the next slide.
  • This table shows examples of management questions that might flow from general questions, some drawn from Exhibit 5-9.
  • A research question best states the objective of the marketing research study. Incorrectly defining the research question is the fundamental weakness in the marketing research process. After the exploration process is complete, the researcher must fine-tune the research question. At this point, the research question will have evolved in some fashion. It will have better focus. In addition to fine-tuning the original question, other research question-related activities should be addressed in this phase to enhance the direction of the project. Examine variables to be studied and assess whether they are operationally defined. Review the research questions to break them down into second and third-level questions. If hypotheses are used, be sure they meet the quality tests. Determine what evidence must be collected to answer the various questions and hypotheses. Set the scope of the study by stating what is not a part of the research question.
  • A Gantt chart is a common project planning tool that reveals summary tasks, benchmarking milestones, and detailed tasks against a time frame for the overall project. Tasks may be color coded to indicate a particular team member’s responsibilities. Many project-management software packages include Gantt charting. The chart may be used to monitor projects to keep them on time, as well as to alert the client or manager to steps requiring their approval—and what happens to the project’s schedule if approval is not forthcoming when it is needed.
  • Exhibit 5a-1
  • Exhibit 5b-2 The basis of searching is understanding how electronic databases are constructed for search. This exhibit is designed to provide the student with a 2-step process for writing advanced query statements using Boolean language. Each database has its own structure so students should always read the material provided by the particular provider about connectors, limiters, truncation symbols, etc. Students should also be encourage to search more than one database using the same search query.
  • Exhibit 5b-1: If you do a day in the library or the computer lab, using this exhibit and the one on the previous slide is a good way to teach the development of query or search statement.
  • This chapter introduces the major descriptors and types of research design.
  • See the text Instructors Manual (downloadable from the text website) for ideas for using this research-generated statistic.
  • Exhibit 6-3 organizes research design into eight categories. This slide is offered as a recap of the issues discussed.
  • There are many definitions of research design. Research design is the blueprint for fulfilling research objectives and answering questions. Its essentials include 1) an activity and time-based plan, 2) a plan based on the research questions, 3) a guide for selecting sources and types of information, 4) a framework for specifying the relationships among the study’s variables, and 5) a procedural outline for every research activity.
  • Exhibit 6-2 provides one project management tool: critical path method (CPM). In a CPM chart: The nodes represent major milestones. The arrows suggest the work needed to get to the milestones. More than one arrow pointing to a node indicates all those tasks must be completed before the milestone has been met. Usually a number is placed along the arrow showing the number of days or weeks required for that task to be completed. The pathway from start to end that takes the longest time to complete is called the critical path .
  • 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 6-3 information is presented here in a discussion format. The degree to which the research question has been crystallized Exploratory study Formal study The method of data collection Monitoring Communication Study The power of the researcher to produce effects in the variables under study Experimental Ex post facto The purpose of the study Reporting Descriptive Causal-Explanatory Causal-Predictive The time dimension Cross-sectional Longitudinal The topical scope —breadth and depth—of the study Case Statistical study The research environment Field setting Laboratory research Simulation The participants’ perceptional awareness of the research activity Actual routine Modified routine
  • The degree to which the research question has been crystallized or structured is the first descriptor of research design. There are two options. Exploratory studies are used when the research question is still fluid or undetermined. The goal of exploration is to develop hypotheses or questions for future research. Formal studies are used when the research question is fully developed and there are hypotheses to be examined.
  • The objectives of exploration may be accomplished with qualitative and quantitative techniques, but exploration relies more heavily on qualitative techniques. Qualitative techniques are non-quantitative data collection used to increase understanding of a topic. Qualitative refers to the meaning, definition, analogy, model, or metaphor characterizing something, while quantitative assumes the meaning and refers to a measure of it. There are many approaches useful for exploratory investigations of management questions. Several such approaches are listed in the slide. These techniques are expanded upon in Chapter 8 .
  • While there are several types of exploratory techniques possible these are the three techniques with the widest applications for business researchers. Secondary data analysis is also called a literature search. Within secondary data exploration, researchers should start first with an organization’s own data archives. The second source of secondary data is published documents prepared by authors outside the sponsor organization. Experience surveys are semistructured or unstructured interviews with experts on a topic or a dimension of a topic. Focus groups are discussions on a topic involving a small group of participants led by a trained moderator.
  • Experience surveys are sometimes called expert interviews or key informant surveys. Even though the term survey is in the name, it is not a closed-ended, structured survey. Rather, experience surveys are interviews designed to extract as much information as possible from the expert’s knowledge. Broad questions guide the discussion. Several questions that could be used in an experience survey are listed in the slide. Some examples of groups who might be identified for an experience survey include potential car buyers, dealer sales representatives, advertising columnists, and automotive industry analysts.
  • Focus groups are widely used in business research. They are led by a trained moderator and typically include 6-10 participants. Mini-focus groups with just 3 people are increasingly common. The facilitator uses group dynamics principles to focus or guide the group in an exchange of ideas, feelings, and experiences on a specific topic. Focus groups can take place in a variety of settings, but many take place in a focus group room equipped with one-way window and recording devices.
  •   Method of data collection distinguishes between monitoring and communication processes. Monitoring processes are studies in which the researcher inspects the activities of a participant or the nature of some material without eliciting responses from the participant. Examples of monitoring include traffic counts, library searches, and counting cars in a parking lot. In a communication study, the researcher questions the participants and then collects their responses by personal or impersonal means.The collected data may result from 1) telephone or interview conversations, 2) self-administered or self-reported instruments sent through the mail, dropped-off in convenient locations, or transmitted electronically, 3) instruments presented before and/or after a treatment or stimulus condition in an experiment.
  •   This photo catches the surfer at a moment in time…much like a single survey catches an attitude at a given moment. That’s what a cross-sectional study does. A study that captures behavior, attitudes, etc. at several moments over time is longitudinal. Cross-sectional studies are studies conducted only once. They seek to reveal a snapshot at one point in time. Longitudinal studies include repeated measures over an extended period of time. Therefore, longitudinal studies can track changes over time. Despite this advantage, longitudinal studies are expensive and time-intensive.
  • Designs also differ as to whether they occur under actual environmental conditions. Field conditions mean that the research occurs in the actual environmental conditions where the dependent variable occurs. Under laboratory conditions, the studies occur under conditions that do not simulate actual environmental conditions. In a simulation, the study environment seeks to replicate the natural environment in a controlled situation. For instance, a lab set up as a kitchen would serve as a simulation of a consumer’s own kitchen.
  • The degree to which the research question has been crystallized Exploratory study Formal study The method of data collection Monitoring Communication Study The power of the researcher to produce effects in the variables under study Experimental Ex post facto The purpose of the study Reporting Descriptive Causal-Explanatory Causal-Predictive The time dimension Cross-sectional Longitudinal The topical scope —breadth and depth—of the study Case Statistical study The research environment Field setting Laboratory research Simulation The participants’ perceptional awareness of the research activity Actual routine Modified routine
  • The purpose of the study asks whether the research is concerned with describing the population’s characteristics or with trying to explain the relationships among variables. Descriptive studies discover the answers to the questions who, what, when, where, or how much.
  •   In contrast to exploratory studies, more formalized studies are typically structured with clearly stated hypotheses or investigative questions. Formal studies serve a variety of research objectives such as those listed in the slide. The third objective, discovery of variable associations, is sometimes labeled a correlational study, which is a subset of descriptive studies. Correlation is the relationship by which two or more variables change together, such that systematic changes in one accompany systematic changes in the other.
  • The degree to which the research question has been crystallized Exploratory study Formal study The method of data collection Monitoring Communication Study The power of the researcher to produce effects in the variables under study Experimental Ex post facto The purpose of the study Reporting Descriptive Causal-Explanatory Causal-Predictive The time dimension Cross-sectional Longitudinal The topical scope —breadth and depth—of the study Case Statistical study The research environment Field setting Laboratory research Simulation The participants’ perceptional awareness of the research activity Actual routine Modified routine
  • Causal studies are differentiated by their ability to control and manipulate variables. Causal studies may be experiments or ex post facto studies. Experiments are studies involving the manipulation of one or more variables to determine the effect on another variable. For example, direct marketers can use split tests on mailings to test which mailing resulted in the highest response rate. Ex post facto designs are evaluations made after-the-fact based on measured variables.
  • This shows employment absenteeism results by age of head of household and club membership. This is an example of results that could come from an ex post facto study. Instead of manipulating variables or controlling exposure to an experimental variable to judge absenteeism, we study subjects who have been exposed to the independent factor and those who have not.
  • Exhibit 8-3 details…relationship but students need to understand the foundations: A stimulus is an event or force (e.g., drop in temperature, crash of stock market, product recall, or explosion in factory). A response is a decision or reaction. A property is an enduring characteristic of a subject that does not depend on circumstances for its activation (e.g., age, gender, family status, religious affiliation, ethnic group, or physical condition). A disposition is a tendency to respond in a certain way under certain circumstances (e.g., attitudes, opinions, habits, values, and drives). A behavior is an action (e.g., consumption habits, work performance, interpersonal acts, and other kinds of performance).
  • Exhibit 6-6
  •   When testing causal hypotheses, we seek three types of evidence: Covariation between A and B. Do we find that A and B occur together in the way hypothesized? When A does not occur, is there also an absence of B? When there is more or less of A, does one also find more or less of B? 2. Time order of events moving in the hypothesized direction. Does A occur before B? 3. No other possible causes of B. Can one determine that C, D, and E do not covary with B in a way that suggests possible causal connections?
  • The usefulness of a research design is reduced when people in a disguised study perceive that research is being conducted. Participants’ perceptions can influence the outcomes of research. This was first discovered in the 1920s when researchers at the Hawthorne plant of the Western Electric Company found that participants reacted favorably to receiving attention. There are three levels of perception to consider and these are highlighted in the slide. Mystery shopping sometimes provides an example of the third level of perception. Mystery shopping involves individuals who pose as customers and visit retail or service organizations to observe and measure specific behaviors or circumstances. If a retail sales associate knows that she is being observed and evaluated, she is likely to modify her performance.
  • This chapter introduces the concept of measurement, the four scale types, the major sources of error and criteria for evaluating good measurement.
  • This note relates to why we must understand the issues around measurement. Students can be asked to relate to their own jobs or business experiences as to what they observed being measured and why management needed to measure the things they did--the consequences of not measuring adequately and accurately. They are bound to have war stories of their own which will make the concept become more real to them.
  • See the text Instructors Manual (downloadable from the text website) for ideas for using this research-generated statistic.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • The ideal study should be designed and controlled for precise and unambiguous measurement of the variables. Since complete control is unattainable, error does occur. Much error is systematic (results from bias), while the remainder is random (occurs erratically). Four major error sources may contaminate results and these are listed in the slide. Opinion differences that affect measurement come from relatively stable characteristics of the respondent such as employee status, ethnic group membership, social class, and gender. Respondents may also suffer from temporary factors like fatigue and boredom. Any condition that places a strain on the interview or measurement session can have serious effects on the interviewer-respondent rapport. The interviewer can distort responses by rewording, paraphrasing, or reordering questions. Stereotypes in appearance and action also introduce bias. Careless mechanical processing will distort findings and can also introduce problems in the data analysis stage through incorrect coding, careless tabulation, and faulty statistical calculation. A defective instrument can cause distortion in two ways. First, it can be too confusing and ambiguous. Second, it may not explore all the potentially important issues.
  • What are the characteristics of a good measurement tool? A tool should be an accurate indicator of what one needs to measure. It should be easy and efficient to use. There are three major criteria for evaluating a measurement tool. Validity is the extent to which a test measures what we actually wish to measure. Reliability refers to the accuracy and precision of a measurement procedure. Practicality is concerned with a wide range of factors of economy, convenience, and interpretability. These criteria are discussed further on the following slides.
  • There are three major forms of validity : content, construct, and criterion. Students need to know that they are in control of the level of validity of their own measurement. Content validity refers to the extent to which measurement scales provide adequate coverage of the investigative questions. If the instrument contains a representative sample of the universe of subject matter of interest, then content validity is good. To evaluate content validity, one must first agree on what elements constitute adequate coverage. To determine content validity, one may use one’s own judgment and the judgment of a panel of experts. Criterion-related validity reflects the success of measures used for prediction or estimation. There are two types of criterion-related validity: concurrent and predictive. These differ only on the time perspective. An attitude scale that correctly forecasts the outcome of a purchase decision has predictive validity. An observational method that correctly categorizes families by current income class has concurrent validity. Criterion validity is discussed further on the following slide. Construct validity is a measurement scale that demonstrates both convergent validity and discriminant validity . In attempting to evaluate construct validity, one considers both the theory and measurement instrument being used. For instance, suppose we wanted to measure the effect of trust in relationship marketing. We would begin by correlating results obtained from our measure with those obtained from an established measure of trust. To the extent that the results were correlated, we would have indications of convergent validity. We could then correlate our results with the results of known measures of similar, but different measures such as empathy and reciprocity. To the extent that the results are not correlated, we can say we have shown discriminant validity.
  • Content validity refers to the extent to which measurement scales provide adequate coverage of the investigative questions. If the instrument contains a representative sample of the universe of subject matter of interest, then content validity is good. To evaluate content validity, one must first agree on what elements constitute adequate coverage. To determine content validity, one may use one’s own judgment and the judgment of a panel of experts. Using the example of trust in relationship marketing, what would need to be included as measures of trust? Ask the students for their own ideas. To extend the questions included and to check for representativeness, students could check the literature on trust, conduct interviews with experts, conduct group interviews, check a database of questions, and so on.
  • Construct validity is a measurement scale that demonstrates both convergent validity and discriminant validity. In attempting to evaluate construct validity, one considers both the theory and measurement instrument being used. For instance, suppose we wanted to measure the effect of trust in relationship marketing. We would begin by correlating results obtained from our measure with those obtained from an established measure of trust. To the extent that the results were correlated, we would have indications of convergent validity. We could then correlate our results with the results of known measures of similar, but different measures such as empathy and reciprocity . To the extent that the results are not correlated, we can say we have shown discriminant validity. This example is expanded upon in the following slide.
  • Again, for a measure to illustrate construct validity , it must have both convergent validity and discriminant validity. Continuing with the trust in relationship marketing example, to show convergent validity, we must show that our measure correlates with a known and trusted measure. To show discriminant validity, our measure must be able to discriminate from other similar, but different measures. This means we would begin by conducting a pilot test to gather data using both the new and known measures of trust and the similar variables such as empathy and credibility . We would then run a correlation analysis to determine if the measures are correlated. To the extent that the new and known measures of trust are correlated, we have demonstrated convergent validity. To the extent that the new trust measure and the measures of empathy and credibility are not correlated, we have shown discriminant validity.
  • Criterion-related validity reflects the success of measures used for prediction or estimation. There are two types of criterion-related validity: concurrent and predictive. These differ only on the time perspective. An attitude scale that correctly forecasts the outcome of a purchase decision has predictive validity . An observational method that correctly categorizes families by current income class has concurrent validity . Criterion validity is discussed further on the following slide.
  • The researcher must ensure that the validity criterion used is itself valid. Any criterion measure must be judged in terms of the four qualities named in the slide. A criterion is relevant if it is defined and scored in the terms the researchers judge to be the proper measures. Freedom from bias is attained when the criterion gives each unit of interest an opportunity to score well. A reliable criterion is stable or reproducible. Finally, the information specified by the criterion must be available .
  • Exhibit 11-6 Exhibit 11-6 illustrates reliability and validity by using an archer’s bow and target as an analogy. High reliability means that repeated arrows shot from the same bow would hit the target in essentially the same place. If we had a bow with high validity as well, then every arrow would hit the bull’s eye. If reliability is low , arrows would be more scattered. High validity means that the bow would shoot true every time. It would not pull right or send an arrow careening into the woods. Arrows shot from a high-validity bow will be clustered around a central point even when they are dispersed by reduced reliability.
  • A measure is reliable to the degree that it supplies consistent results. Reliability is a necessary contributor to validity but is not a sufficient condition for validity. It is concerned with estimates of the degree to which a measurement is free of random or unstable error. Reliable instruments are robust and work well at different times under different conditions. This distinction of time and condition is the basis for three perspectives on reliability – stability, equivalence, and internal consistency (see Exhibit 11-7). A measure is said to possess stability if one can secure consistent results with repeated measurements of the same person with the same instrument . Test-retest (comparisons of two tests to learn how reliable they are) can be used to assess stability. A correlation between the two tests indicates the degree of stability. These are discussed further on the following slide.
  • See the text Instructors Manual (downloadable from the text website) for ideas for using this research-generated statistic.
  • Several terms are used by researchers to converse about applied and theoretical business problems. A concept is a bundle of meanings or characteristics associated with certain concrete, unambiguous events, objects, conditions, or situations. The importance of conceptualization is discussed in the following slide. A construct is a definition specifically invented to represent an abstract phenomena for a given research project. Exhibit 3-1, a depiction of job redesign constructs, is provided in Slide 2-13. A conceptual scheme is the interrelationship between concepts and constructs. An operational definition defines a variable in terms of specific measurement and testing criteria. An example of an operational definition is provided in Slide 2-14. A variable is used as a synonym for the construct being studied. Slides 2-15 through 2-20 expand on different types of variables. A proposition is a statement about observable phenomena that may be judged as true or false. (Slide 2-21) A hypothesis is a proposition formulated for empirical testing. (Slides 2-22 through 2-25) A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. Slide 2-26 shows an example of a theory. A model is a representation of a system constructed to study some aspect of that system. Slide 2-27 shows an example of a model.
  • We must attempt to measure concepts in a clear manner that others can understand. If concepts are not clearly conceptualized and measured, we will receive confusing answers.
  • Exhibit 3-1 Exhibit 3-1 illustrates some of the concepts and constructs relevant to job redesign. The concepts at the bottom of the exhibit (format accuracy, manuscript errors, and keyboarding speed) are the most concrete and easily measured. Keyboarding speed is one just concept in the group that defines a construct that the human resource analyst calls Presentation Quality . It is not directly observable like keyboarding speed. It is a term used to communicate (a label) the combination of meanings presented by the three concepts. Concepts at the next level are vocabulary, syntax, and spelling. As they are related, the analyst groups them into a construct she calls language skill . Language skills is placed at a higher level of abstraction in the exhibit because two of the concepts that comprise it, vocabulary and syntax, are more difficult to observe and measure. The construct of job interest is not yet measured nor are its components specified. Researchers often refer to such constructs as hypothetical constructs because they are inferred only from the data—they are presumed to exist but no measure tests whether such constructs actually exist. If research shows the concepts and constructs in this example to be interrelated, and if the connections can be supported, then the analyst has the beginning of a conceptual scheme. One exercise you can try is to have students attempt to identify the concepts/constructs in the hypothetical construct…job interest, and discuss which elements are truly measurable…and how.
  • Operational definitions are definitions stated in terms of specific criteria for testing or measurement. The specifications must be so clear that any competent person using them would classify the objects in the same way. If a study of college students required classifying students by class level, a definition of each category would be necessary. Students could be grouped by class level based on self-report, number of years in school, or number of credit hours completed. Credit hours is the most precise measure.
  • In practice, the term variable is used as a synonym for the property being studied . In this context, a variable is a symbol of an event, act, characteristic, trait, or attribute that can be measured and to which we assign categorical values. The different types of variables are presented on the following slides.
  • For the purposes of data entry and analysis, we assign numerical values to a variable based on that variable’s properties. Dichotomous variables have only two values that reflect the absence or presence of a property. Variables also take on values representing added categories such as demographic variables. All such variables are said to be discrete since only certain values are possible. Continuous variables take on values within a given range or, in some cases, an infinite set.
  • Exhibit 3-2 Exhibit 3-2 presents the commonly used synonyms for independent and dependent variables. An independent variable is the variable manipulated by the researcher to cause an effect on the dependent variable. The dependent variable is the variable expected to be affected by the manipulation of an independent variable.
  • Moderating variables are variables that are believed to have a significant contributory or contingent effect on the originally stated IV-DV relationship. Whether a variable is treated as an independent or as a moderating variable depends on the hypothesis. Examples of moderating variables are shown in the slide.
  • Extraneous variables are variables that could conceivably affect a given relationship. Some can be treated as independent or moderating variables or assumed or excluded from the study. If an extraneous variable might confound the study, the extraneous variable may be introduced as a control variable to help interpret the relationship between variables. Examples are given in the slide.
  • An intervening variable (IVV) is a factor that affects the observed phenomenon but cannot be measured or manipulated. It is a conceptual mechanism through which the IV and MV might affect the DV.
  • A proposition is a statement about observable phenomena that may be judged as true or false. A hypothesis is a proposition formulated for empirical testing. A case is the entity or thing the hypothesis talks about. When the hypothesis is based on more than one case, it would be a generalization. Examples are provided in the slide.
  • A descriptive hypothesis is a statement about the existence, size, form, or distribution of a variable. Researchers often use a research question rather than a descriptive hypothesis. Examples are provided in the slide. Either format is acceptable, but the descriptive hypothesis has three advantages over the research question. Descriptive hypotheses encourage researchers to crystallize their thinking about the likely relationships. Descriptive hypotheses encourage researchers to think about the implications of a supported or rejected finding. Descriptive hypotheses are useful for testing statistical significance.
  • A relational hypothesis is a statement about the relationship between two variables with respect to some case. Relational hypotheses may be correlational or explanatory (causal). A correlational hypothesis is a statement indicating that variables occur together in some specified manner without implying that one causes the other. A causal hypothesis is a statement that describes a relationship between two variables in which one variable leads to a specified effect on the other variable.
  • This slide presents the functions served by hypotheses.
  • The conditions for developing a strong hypothesis are more fully developed in Exhibit 3-4.
  • Exhibit 3-5 What is the difference between theories and hypotheses? Theories tend to be complex, abstract, and involve multiple variables. Hypotheses tend to be simple, limited-variable statements involving concrete instances. A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced to explain or predict phenomena. To the degree that our theories are sound and fit the situation, we are successful in our explanations and predictions. The product life cycle, shown in Exhibit 3-5, is an example of a theory.
  • Exhibit 3-7: Business models are developed through the use of inductive and deductive reasoning. As illustrated in Exhibit 3-7, a business model may originate from empirical observations about market behavior based on researched facts and relationships among variables. Inductive reasoning allows the modeler to draw conclusions from the facts or evidence in planning the dynamics of the model. The modeler may also use existing theory, managerial experience or judgment, or facts.
  • Exhibit 3-6 A model is a representation of a system constructed to study some aspect of that system or the system as a whole. Models versus Theories a model’s role is to represent or describe A theory’s role is to explain . Models in business research may be descriptive, predictive, and normative. Descriptive models are used for complex systems because they allow for the visualization of numerous variables and relationships. Predictive models forecast future events and facilitate business planning. Normative models are used for control, because they indicate necessary actions. Exhibit 3-6, shown in the slide, is a distribution network model called a maximum flow model used in management science. In this example, a European manufacturer of automobiles needs an increased flow of shipping to its Los Angeles distribution center to meet demand. However the primary distribution channel is saturated and alternatives must be sought. Models allow researchers to specify hypotheses that characterize present or future conditions: the effect of advertising on consumer awareness or intention to purchase, brand switching behavior, an employee training program, or other aspects of business.
  • Good business research is based on sound reasoning because reasoning is essential for producing scientific results. This slide introduces the scientific method and its essential tenets. The scientific method guides our approach to problem-solving. An important term in the list is empirical . Empirical testing denotes observations and propositions based on sensory experiences and/or derived from such experience by methods of inductive logic, including mathematics and statistics. Researchers using this approach attempt to describe, explain, and make predictions by relying on information gained through observation. The scientific method is described as a puzzle-solving activity.
  • The steps followed by business researchers to approach a problem are presented in the slide.
  • Exposition consists of statements that describe without attempting to explain. Argument allows us to explain, interpret, defend, challenge, and explore meaning. There are two types of argument: deduction and induction. Deduction is a form of reasoning in which the conclusion must necessarily follow from the premises given. The next slide provides an example of a deductive argument. Induction is a form of reasoning that draws a conclusion from one or more particular facts or pieces of evidence. Slide 2-8 illustrates an inductive argument.
  •   This slide provides an example of a deductive argument.
  • This slide provides an example of an inductive argument.
  • Exhibit 3-8 Induction and deduction can be used together in research reasoning. Induction occurs when we observe a fact and ask, “Why is this?” In answer to this question, we advance a tentative explanation or hypothesis. The hypothesis is plausible if it explains the event or condition (fact) that prompted the question. Deduction is the process by which we test whether the hypothesis is capable of explaining the fact. Exhibit 3-8 illustrates this process.
  • Exhibit 3-9

2 purpose of business research, inductive & deductive approaches 2 purpose of business research, inductive & deductive approaches Presentation Transcript

  • Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.Week 2 Purpose of Business ResearchDr. H Holloman
  • Objectives1. State the purpose of the business research.2. Develop appropriate research questions andhypotheses.3. Identify dependent and independent variables inbusiness research.
  • Recap1. Why do managers need business research? Does your workplace dobiz research? Why or why not (say, benefits a/o challenges)?2. What is a research process? As a manager in your workplace, howmight you apply the research process learned?
  • Clarifying the Research Question & Reduces Information Overload“Companies are certainly aware of data mining, but mostcompanies are not making effective use of the data collected.They are not so good at analyzing it or applying these insights tothe business.”Gregory Piatetsky-ShapiropresidentKdnuggets
  • PulsePoint:Research Revelation33 The percent of financial executiveswho have full confidence in theircurrent risk strategies.
  • Exploratory Phase Search StrategySearchStrategyDiscovery/ AnalysisSecondary SourcesIndividualIn-Depth InterviewsExpertInterviewGroupDiscussions• Develop concepts more clearly, establish priorities• Develop operational definitions, and improve the final research design.• Secondary data sources: published studies, document analysis, organization’sdatabases, expert interviews, form formal/informal group discussions• Review secondary sources: moving from management question to researchquestion.
  • Exhibit 5-1 suggeststhat exploration ofsecondary sources canbe useful at any stageof the management-research questionhierarchy.
  • What Are the Objectives of Secondary Searches?1. Expand understanding of management dilemma2. Gather background information3. Identify information to gather4. Identify sources for and actual questions5. Identify sources for and actual sample frames
  • Conducting a Literature SearchDefine management dilemmaDefine management dilemmaConsult books for relevant termsConsult books for relevant termsUse terms to searchUse terms to searchLocate/review secondarysourcesLocate/review secondarysourcesEvaluate value of each sourceand contentEvaluate value of each sourceand contentThe result of a literature search could be a solution to the management dilemma. Ifso, no further research is necessary. Otherwise, a research proposal is generated.
  • Levels of InformationPrimarySources:MemosLettersInterviewsSpeechesLawsInternal recordsSecondarySources:EncyclopediasTextbooksHandbooksMagazinesNewspapersNewscastsTertiarySources:IndexesBibliographiesInternetsearch engines
  • Integrating Secondary Data
  • TheU.S. Governmentis theworld’s largestsource of data
  • Information Sources
  • Exhibit 5-3 offers several questions to answer when evaluatingweb sites on the five factors.AuthorityFormatFormatEvaluationFactorsEvaluationFactorsPurposeexplicit or hiddenagendaScope(criteria for infoinclusion (time,space, depth)Scope(criteria for infoinclusion (time,space, depth)
  • Exhibit 5-4: Evolution of Data MiningEvolutionaryStepInvestigativeQuestionEnablingTechnologiesCharacteristicsDatacollection(1960s)“What was myaverage total revenueover the last fiveyears?”Computers, tapes,disksRetrospective,static data deliveryData access(1980s)“What were unit salesin California lastDecember?”Relational databases(RDBMS), structuredquery language(SQL), ODBCRetrospective,dynamic datadelivery at recordlevelDatanavigation(1990s)“What were unit salesin California lastDecember? Drill downto Sacramento.”Online analyticprocessing (OLAP),multidimensionaldatabases, datawarehousesRetrospective,dynamic datadelivery at multiplelevels
  • Data Mining in Business--for understanding of customers, as noted by the emphasis on marketing’s useof data mining (green bars).--in search of greater profitability (financial analysis=yellow bar).Operations use was growing (gold bars). Notably absent was Human Resourcesuse of data mining.
  • 5-StepsData-Mining Process
  • BusinessResearchProcess
  • Stage 1: Clarifying the Research QuestionManagement-research question hierarchy begins by identifying themanagement dilemma
  • Exhibit 5-6Management-Research Question Hierarchy
  • Exhibit 5-7SalePro’s Hierarchy3areas:salespersoncompensation,productformulation, ortrade advertising.
  • Formulatingthe Research Question
  • Types of Management QuestionsDiscussions: Drawing upon your work exp, identify 3 potential biz risks, & convert theminto 3 types of questions respectively.
  • Discussions: Drawing upon your work exp, identify 3 potential bizrisks, & convert them into 3 types of questions respectively.
  • The Research Question4. Determine necessaryevidence5. Setscope of study1.Examinevariables2. Break questionsdown3. Evaluate hypothesesFine-Tuning--states the objective , then fine-tuning--other activities:1. operationally defined.2. second & third-level questions.3. hypotheses meet the quality tests.4. Collect evidence to answer questions & hypotheses.5. Set the scope of the study
  • Gantt Chart: projectplanning tool thatreveals summary tasks,benchmarkingmilestones & detailedtasks against a timeframe for the overallproject.MindWriter Project Plan
  • Searching Databases vs. the Web
  • Exhibit 5b-2 Advanced Searching Process
  • Review of Advanced Search Options
  • Chapter 6Research Design: An OverviewMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • Learning ObjectivesUnderstand . . .• The basic stages of research design.• The major descriptors of research design.• The major types of research designs.• The relationships that exist between variables inresearch design and the steps for evaluating thoserelationships.
  • Research Guides Decisions“Most human beings and most companiesdon’t like to make choices. And they particularly don’t liketo make a few choices that they really have to live with.”Alan Lafleyformer president and chairman of the boardP&G
  • PulsePoint:Research Revelation76 The percent of mobile phonesubscribers worldwide who use SMStext messaging.
  • Key Terms1. Research design2. Case study3. Causal study4. Correlation5. Causation6. Reciprocal relationship7. Statistical study8. Cross-sectional study9. Longitudinal study10. Primary data11. Secondary data12. Descriptive study13. Communication study14. Ethnographic research15. Qualitative techniques16. Field conditions17. Individual depth interview18. Focus group19. Experiment20. Control group21. Laboratory conditions22. Random assignment23. Ex post facto design24. Monitoring25. Exploratory study26. Formal study27. Simulation
  • Research Design DescriptorsCategory OptionsDegree to which the research question iscrystallizedExploratory study. Formal studyMethod of data collection Monitoring. Communication StudyThe power of the researcher to produceeffects in the variables under studyExperimental. Ex post factoThe purpose of the study Reporting. DescriptiveCausal-ExplanatoryCausal-PredictiveTime dimension Cross-sectionalLongitudinalScope—breadth and depth—of the study Case study. Statistical studyThe research environment Field settingLaboratory researchSimulationParticipants’ perceptional awareness Actual routineModified routine
  • What Is Research Design?Research design: A blueprint for fulfilling research objectives &answering questions.
  • Exhibit 6-2What Tools Are Used in Designing Research?
  • Exhibit 6-1Design in the Researches
  • Exhibit 6-3 Research Design DescriptorsExperimentalEffectsPerceptualAwarenessPerceptualAwarenessResearchEnvironmentResearchEnvironmentDescriptorsDescriptorsQuestionCrystallization Data CollectionMethodData CollectionMethodTime DimensionTime DimensionTopical ScopePurpose ofStudy
  • Degree of Question CrystallizationExploratory StudyLoose structureExpand understandingProvide insightDevelop hypothesesFormal StudyPrecise proceduresBegins with hypothesesAnswers research questions
  • Approaches for ExploratoryInvestigationsParticipant observationFilm, photographsProjective techniquesPsychological testingCase studiesEthnographyExpert interviewsDocument analysisProxemics and KinesicsEstablished range and scope ofpossible managementdecisionsEstablished range and scope ofpossible managementdecisionsEstablished majordimensions of researchtaskEstablished majordimensions of researchtaskDefined a set of subsidiaryquestions that can guideresearch designDefined a set of subsidiaryquestions that can guideresearch designDesired Outcomes of ExploratoryStudies
  • Desired Outcomes of Exploratory Studies (cont.)Develop hypotheses about possiblecauses of management dilemmaDevelop hypotheses about possiblecauses of management dilemmaLearn which hypothesescan be safely ignoredLearn which hypothesescan be safely ignoredConclude additional research isnot needed or not feasibleConclude additional research isnot needed or not feasible
  • Commonly Used Exploratory TechniquesSecondary DataAnalysisSecondary DataAnalysisFocus GroupsFocus GroupsExperience SurveysExperience Surveys
  • Experience Surveys• What is being done?• What has been tried in the past with or without success?• How have things changed?• Who is involved in the decisions?• What problem areas can be seen?• Whom can we count on to assist or participate in the research?
  • Focus GroupsGroup discussion6-10 participantsModerator-led90 minutes-2 hours
  • Data Collection MethodMonitoring Communication
  • The Time Dimension (Trends)Cross-sectionalLongitudinal (Panel studies w/same respondents)
  • The Research EnvironmentField conditionsLab conditionsSimulations
  • ExperimentalEffectsPerceptualAwarenessPerceptualAwarenessResearchEnvironmentResearchEnvironmentDescriptorsDescriptorsQuestionCrystallization Data CollectionMethodData CollectionMethodTime DimensionTime DimensionTopical ScopePurpose ofStudyResearch Design Descriptors
  • Purpose of the StudyReporting DescriptiveCasual-ExplanatoryCausal-Predictive
  • Descriptive Studies
  • Descriptive StudiesDescriptions ofpopulation characteristicsDescriptions ofpopulation characteristicsEstimates of frequency ofcharacteristicsEstimates of frequency ofcharacteristicsDiscovery of associationsamong variablesDiscovery of associationsamong variables
  • Research Design DescriptorsExperimentalEffectsPerceptualAwarenessPerceptualAwarenessResearchEnvironmentResearchEnvironmentDescriptorsDescriptorsQuestionCrystallization Data CollectionMethodData CollectionMethodTime DimensionTime DimensionTopical ScopePurpose ofStudy
  • Experimental EffectsExperimentStudy involving themanipulation or control of oneor more variables to determinethe effect on another variableEx Post Facto StudyAfter-the-fact report on whathappened to the measuredvariable
  • Ex Post Facto DesignEmployment absenteeism results by age of head of household and clubmembership.
  • Understanding Casual RelationshipsExhibit 8-3• A stimulus (X): an event or force (e.g., drop in temperature, crash ofstock market, product recall, or explosion in factory).• A response (Y): a decision or reaction, or outcomes.• A property: an enduring characteristic of a subject that does notdepend on circumstances for its activation (e.g., age, gender, familystatus, religious affiliation, ethnic group, or physical condition).• A disposition: a tendency to respond in a certain way under certaincircumstances (e.g., attitudes, opinions, habits, values, and drives).• A behavior: an action (e.g., consumption habits, work performance,interpersonal acts, and other kinds of performance).
  • Types of Asymmetrical Causal RelationshipsRelationshipTypeNature ofRelationshipExamplesStimulus-responseAn event or change a response fromsome object.• A change in work rules leads to a higher levelof worker output.• A change in government economic policyrestricts corporate financial decisions.• A price increase results in fewer unit sales.Property-dispositionAn existingproperty  adisposition.• Age and attitudes about saving.• Gender attitudes toward social issues.• Social class and opinions about taxation.Disposition-behaviorA disposition  aspecific behavior.• Opinions about a brand and its purchase.• Job satisfaction and work output.• Moral values and tax cheating.Property-behaviorAn existingproperty  aspecific behavior.• Stage of the family life cycle and purchases offurniture.• Social class and family savings patterns.• Age and sports participation.
  • Evidence of CausalityCovariation between A & B↔Covariation between A & B↔Time order of eventsT1  T2Time order of eventsT1  T2No other possiblecauses of BNo other possiblecauses of B
  • Research Design DescriptorsExperimentalEffectsPerceptualAwarenessPerceptualAwarenessResearchEnvironmentResearchEnvironmentDescriptorsDescriptorsQuestionCrystallization Data CollectionMethodData CollectionMethodTime DimensionTime DimensionTopical ScopePurpose ofStudy
  • Participants’ Level of Perceptional AwarenessThe 1920s disguised study:1.Participants’ perceptions  the outcomes of research.2.Participants of Hawthorne plant of the Western ElectricCompany reacted favorably to receiving attention
  • Chapter 11 MeasurementMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • Learning ObjectivesUnderstand . . . The distinction between measuring objects, properties, and indicantsof properties. The similarities and differences between the four scale types used inmeasurement and when each is used. The four major sources of measurement error. The criteria for evaluating good measurement.
  • Measurements Will Vary Over Time
  • Key Terms Internal validity Interval scale Mapping rules Measurement Nominal scale Objects Ordinal scale Practicality Properties• Ratio scale• Reliability1. Equivalence2. Internal consistency3. Stability• Validity1. Construct2. Contents3. Criterion-related
  • PulsePoint:Research Revelation32.5 The percent of corporationsusing or planning to use cloudcomputing—using softwareand server space via Internetsources.
  • MeasurementSelectmeasurable phenomenaDevelop a set ofmapping rulesApply the mapping ruleto each phenomenon
  • Characteristics of Measurement
  • Levels of MeasurementOrdinalintervalRatioNominal
  • Nominal Scales1. Mutually exclusive2. Collectively exhaustivecategories3. Exhibits only classification
  • Levels of MeasurementOrdinalOrdinalintervalintervalRatioRatioNominalNominal ClassificationClassificationOrderOrderClassificationClassificationOrdinal data require conformity to a logical postulate, which states:1. If a is greater than b, and2. b is greater than c, then3. a is greater than c.Rankings are examples of ordinal scales. Attitude and preferencescales are also ordinal.
  • Levels of MeasurementOrdinalOrdinalintervalintervalRatioRatioNominalNominal ClassificationClassificationOrderOrderClassificationClassificationOrderOrderClassificationClassification DistanceDistance1. Distance: Equality of interval. Equal distance between numbers2. Origin means that the number series has a unique origin indicated by thenumber zero.3. Combinations of these characteristics provide four widely usedclassifications of measurement scales: nominal, ordinal, interval, ratio.
  • Levels of MeasurementOrdinalOrdinalintervalintervalRatioRatioNominalNominal ClassificationClassificationOrderOrderClassificationClassificationOrderOrderClassificationClassification DistanceDistanceNatural OriginNatural OriginOrderOrderClassificationClassification DistanceDistance
  • Ratio ScalesCharacteristics ofnominal, ordinal,interval scalesAbsolute zero
  • From Investigative toMeasurement Questions
  • Sources of ErrorRespondentInstrumentMeasurerSituation1. Interviewer’s demographics2. The interviewer can distort responsesby rewording, paraphrasing, orreordering questions.-Stereotypes in appearance andaction also introduce bias.3. Measurement4. Sampling
  • Evaluating Measurement ToolsCriteriaCriteria
  • Validity DeterminantsContentConstructCriterion
  • Increasing Content ValidityContentLiteratureSearchLiteratureSearchExpertInterviewsExpertInterviewsGroupInterviewsGroupInterviewsQuestionDatabaseQuestionDatabaseEtc.Etc.
  • Validity DeterminantsContentConstruct
  • Increasing Construct ValidityNew measure of trustNew measure of trustKnown measure of trustKnown measure of trustEmpathyEmpathyCredibilityCredibility
  • Validity DeterminantsContentConstructCriterion
  • Judging Criterion ValidityRelevanceRelevanceFreedom from biasFreedom from biasReliabilityReliabilityAvailabilityAvailabilityCriterion
  • Understanding Validity and Reliability
  • Reliability EstimatesStabilityInternalConsistencyEquivalence
  • Chapter 3Thinking Like a ResearcherThinking Like a ResearcherMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • Learning Objectives Understand . . . The terminology used by professional researchers employingscientific thinking. What you need to formulate a solid research hypothesis. The need for sound reasoning to enhance research results.
  • Research and Attitudes“Brand communities play a pivotal role for a brand connectingwith its consumers, and as one of our Never Ending Friendingfocus group respondent notes: “I want brands to be my friends,”which means that consumers would like to have common ideas,conversations and benefits delivered to them on their own terms.”Judit Nagyvice president, consumer insightsMySpace/Fox Interactive Media
  • PulsePoint:Research Revelations55 The percent of executives whoadmitted that their companies do nothave an official policy for socialnetworks.
  • Key TermsArgumentCaseConceptConceptual schemeConstructDeductionEmpiricismExpositionHypothesisCorrelationalDescriptiveExplanatoryRelationalHypothetical constructInductionModelOperational definitionPropositionSound reasoningTheoryVariableControlConfounding (CFV)Dependent (DV)Extraneous (EV)Independent (IV)Intervening (IVV)Moderating (MV)
  • Language of ResearchVariablesModelsModelsTerms usedin researchTerms usedin researchConstructsOperationaldefinitionsOperationaldefinitionsPropositions/HypothesesPropositions/HypothesesConceptualschemesConceptualschemesConceptsConcepts
  • Language of ResearchClear conceptualizationof conceptsShared understandingof conceptsSuccessofResearch
  • Job RedesignConstructs and Concepts
  • Operational DefinitionsFreshmanSophomoreJuniorSenior< 30 credit hours30-50 credit hours60-89 credit hours> 90 credit hoursHow can we define the variable“class level of students”?
  • A Variable Is the Property BeingStudiedVariableVariableEventEvent ActActCharacteristicCharacteristic TraitTraitAttributeAttribute
  • Types of Variables
  • Independent and Dependent VariableSynonymsIndependent Variable (IV)PredictorPresumed causeStimulusPredicted from…AntecedentManipulatedDependent Variable (DV)CriterionPresumed effectResponsePredicted to….ConsequenceMeasured outcome
  • Relationships Among Variable Types
  • Relationships Among Variable Types
  • Relationships Among Variable Types
  • Moderating Variables (MV)• The introduction of a four-day week (IV) will lead to higherproductivity (DV), especially among younger workers (MV)• The switch to commission from a salary compensation system (IV)will lead to increased sales (DV) per worker, especially moreexperienced workers (MV).• The loss of mining jobs (IV) leads to acceptance of higher-riskbehaviors to earn a family-supporting income (DV) – particularlyamong those with a limited education (MV).
  • Extraneous Variables (EV)• With new customers (EV-control), a switch to commission from asalary compensation system (IV) will lead to increased salesproductivity (DV) per worker, especially among younger workers (MV).• Among residents with less than a high school education (EV-control),the loss of jobs (IV) leads to high-risk behaviors (DV), especially due tothe proximity of the firing range (MV).
  • Intervening Variables (IVV)• The switch to a commission compensation system (IV) will lead tohigher sales (DV) by increasing overall compensation (IVV).• A promotion campaign (IV) will increase savings activity (DV),especially when free prizes are offered (MV), but chiefly amongsmaller savers (EV-control). The results come from enhancing themotivation to save (IVV).
  • Propositions and HypothesesBrand Manager Jones (case) has a higher-than-average achievement motivation (variable).Brand managers in Company Z (cases) have ahigher-than-average achievement motivation(variable). Generalization
  • Hypothesis FormatsDescriptive HypothesisIn Detroit, our potato chip marketshare stands at 13.7%.American cities are experiencingbudget difficulties.Research QuestionWhat is the market share for ourpotato chips in Detroit?Are American cities experiencingbudget difficulties?
  • Relational HypothesesCorrelationalYoung women (under 35)purchase fewer units of ourproduct than women who areolder than 35.The number of suits sold variesdirectly with the level of thebusiness cycle.CausalAn increase in family incomeleads to an increase in thepercentage of income saved.Loyalty to a grocery storeincreases the probability ofpurchasing that store’s privatebrand products.
  • The Role of HypothesesGuide the direction of the studyGuide the direction of the studyIdentify relevant factsIdentify relevant factsSuggest most appropriate research designSuggest most appropriate research designProvide framework for organizing resultingconclusionsProvide framework for organizing resultingconclusions
  • Characteristics ofStrong HypothesesAStrongHypothesisIsAStrongHypothesisIsAdequateAdequateTestableTestableBetterthan rivalsBetterthan rivals
  • Theory within Research
  • The Role of Reasoning
  • A Model within Research
  • The Scientific Method
  • ResearchersEncounter problemsState problemsPropose hypothesesDeduce outcomesFormulate rival hypothesesDevise and conduct empiricaltestsDraw conclusions
  • Sound ReasoningExposition ArgumentInductionDeductionTypes of Discourse
  • Deductive ReasoningInner-city householdinterviewing is especially difficultand expensiveInner-city householdinterviewing is especially difficultand expensiveThis survey involvessubstantial inner-cityhousehold interviewingThis survey involvessubstantial inner-cityhousehold interviewingThe interviewing in this surveywill be especially difficult andexpensiveThe interviewing in this surveywill be especially difficult andexpensive
  • Inductive ReasoningWhy didn’t sales increase during our promotionalevent?Regional retailers did not have sufficient stock to fillcustomer requests during the promotional periodA strike by employees prevented stock from arriving intime for promotion to be effectiveA hurricane closed retail outlets in the region for 10days during the promotion
  • Why Didn’t Sales Increase?
  • Tracy’s Performance