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MKGT 368 Review for Exam 1 Spring 2011 Role of Marketing Research in Managerial Decision-Making Chapter 1 What is Marketing? • American Marketing Association Definition: • Marketing is an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders. • In sum, marketing is about… • meeting needs • delivering value to all people affected by a transaction • getting the right product to the right folks at the right time/place for the right price using an appropriate combination of promotional techniques (the four Ps) What is Marketing Research? • American Marketing Association (p. 4 in your book): • …the function that links an organization to its market through the gathering of information. This information allows for the identification and definition of market-driven opportunities and problems and allows for the generation, refinement and evaluation of marketing actions. It allows for the monitoring of marketing performance and improved understanding of marketing as a business process. • Malhotra & Peterson (2006, p. 5): • …the systematic and objective identification, collection, analysis, dissemination, and use of information that is undertaken to improve decision making related to identifying and solving problems (also known as opportunities) in marketing. • Feinberg et al. (2008, p. 4): • … the systematic process of using formal research and consistent data gathering to improve the marketing function within an organization. This information is used to identify opportunities and problems, monitor performance, and link marketing inputs with outputs of interest, such as awareness, satisfaction, sales, share and profitability. The “Marketing Concept” • Need for marketing research based on “marketing concept” • Idea introduced in 1952, GE’s Annual Report: • The (marketing) concept introduces the marketer at the beginning rather than at the end of the production cycle and integrates marketing into each phase of the business. Thus, marketing, through its studies and research, will establish for the engineer, the designer, and manufacturer, what the customer wants in a given product, what price he (or she) is willing to pay, and where and when it will be wanted. Marketing will have authority in product planning, production scheduling, and inventory control, as well as in sales, distribution, and servicing of the product. • Gave rise to the “Marketing System” • Conceptual model linking Independent Variables (causes) to Dependent Variables (outcomes) • Understanding the link between IVs and DVs (and reducing uncertainty) is a key function of marketing research Marketing System Independent Variables Dependent Variables Understanding relationship Behavior Marketing Mix (controllable) between IVs and DVs is a key function of MR Awareness Knowledge Pricing Liking Promotion Preference Product Intent to buy Distribution Purchase Situational Factors (uncontrollable) Performance Measures Demand Competition Sales Legal/political Market share Economic climate Profit Technology ROI Gov regulation Image From Feinberg et al. (2008) The Decision-Making Process 1. Recognize a unique marketing problem or opportunity 2. Clarify the decision (what do we need to know?) 3. Identify alternative courses of action 4. Evaluate the alternatives 5. Select a course of action 6. Implement selected course of action and monitor results From Feinberg et al. (2008) Common Questions Addressed by Marketing Researchers • Where are new market opportunities (based on macroenvironmental trends)? • How should we segment the market (based on customer characteristics)? • How are we doing (compared to the competition)? Are consumers satisfied with our product or service? If not, what should we improve? • How should we position our product (relative to the competition)? • How will people respond to a new product concept? Test marketing… • If our product is priced at $100, what will be the expected demand? • How effective is our advertising? Promotions? Sales force? • What’s in store for the future, and how should we adapt? Marketing Research Process: Transforming Data into Information Chapter 2 Overview • Types of Marketing Research Firms • When is Marketing Research Needed? • Decision-Makers vs. Researchers • Iceberg Principle: Symptoms vs. Underlying Problems • Steps in Marketing Research • Elements in a Marketing Research Proposal • Unethical Activities in Marketing Research Marketing Research Industry Research Supplier Internal External Full Service Limited Service Syndicated Customized Internet Field Data Coding Data Services and Entry Analysis AC Nielsen Synovate Greenfield On-Line Field Work Davis SDR Chicago Coding Atlanta Group Malhotra & Peterson (2006) Exhibit 2.3 When is Marketing Research Needed? Type of Can decision problem be resolved YES information Decision with subjective information? Maker NO NO Don’t undertake the Nature of Is problem of strategic importance? decision Info research process YES Availability Is secondary data inadequate for NO of data addressing the problem? YES Marketing Researcher Time NO Is there enough time to collect constraints data for managerial decision? Bring in YES NO Resources Are there enough resources required ($, people) to carry out the study? YES Cost/Benefit Does value of research NO Ratio outweigh costs of research? Do undertake the YES Info research process When NOT to conduct research… 1. Sufficient information for a decision already exists 2. Insufficient time for research – must make an immediate decision 3. Insufficient resources for research 4. When costs of research are greater than its benefits Components of the Research Proposal 1. Purpose of proposed research plan (problem, objectives) 2. Type of study (e.g., exploratory, causal, primary, secondary etc.) 3. Define target population and sample size 4. Describe sampling technique and actual data collection methods to be used 5. Research instruments to be used 6. Possible managerial benefits 7. Proposed cost of whole project 8. Describe primary researchers and research firm 9. Proposed tables (how data might be presented) Researchers vs. Decision-Makers Researchers Decision-Makers • Like to explore new questions • Want info to confirm decision • Can tolerate long investigations • Want quick information • Not concerned about cost • Less willing to pay for more info • Enjoy surprises • Dislike & reject surprises • Tentative; speak in probabilities • Decision- and results-oriented • Interested in past behavior • Interested in future performance Iceberg Principle: Symptoms vs. Problems Four Broad Phases in Information Research Ten steps Ten Steps in Information Research Unethical Activities… • by Client (End User) • Solicit proposals, but choose none. Use proposals as a guideline for how to conduct one’s own study. • Promise a long-term relationship to get a low introductory rate, but then never follow through with more projects • by Researcher • Unethical pricing: promise low price, then jack it up • Fail to provide (promised) incentives to research subjects • Abuse respondents; promise short survey that turns into an hour; pass along information without permission; collect information without permission • Selling useless research services • Interviewers make up data (“curbstoning” or “rocking chair” interviewing) • Interviewers create “phantom” data (duplicate actual data to boost sample) • Change or fail to report results in an effort to reach a certain conclusion • by Respondent • Give misleading responses (can include “socially desirable” responding) Qualitative Methods: Interviews and Focus Groups Chapters 6-7 Qualitative vs. Quantitative Methods • Qualitative • Used in exploratory designs to gain prelminary insights into decision problems and opportunities • Quantitative • Using formalized standard questions and predetermined response options (yes, no) in questionnaires or surveys administered to large numbers of respondents • Differences Between Qualitative and Quantitative Approaches Focus Groups • Focus Groups • Formalized small group of people have an interactive, spontaneous discussion on one topic or concept • Can… • help identify root problem underlying symptoms • help identify questions to ask in a survey • provide insights into quantitative results • uncover hidden needs, wants, attitudes, feelings, perceptions and motives regarding products/services • lead to new ideas for products/services • help develop new measures for quantitative survey • provide insights into how people “experience” products/services (what they mean to them) Composing a Focus Group • Selecting Participants • Select a good group of participants (relatively homogenous groups make people feel comfortable, but should have some variability in views) • Potential group members should have enough knowledge to contribute • Try to incorporate some randomization in selection (within a target group) • Size should be between 8-12 people with a moderator • Use a friendly invite and provide incentives (typically between $75-100) • Pick a comfortable location Some Additional Interview Techniques • Case Study • Analyze in depth one or more situations similar to the problem you are trying to solve • Experience Interviews • Interview people believed to be knowledgeable about the problem you are trying to solve • Protocol Interviews • Ask people to verbalize the thought processes and activities they would go through in a given situation (e.g., buying a car) • Articulative Interviews • Listening to people in order to identify value conflicts they may have (e.g., want to buy a nice bike but also be frugal) Projective Techniques • Definition • Techniques that allow a person to “project” their thoughts, feelings or motives onto others, a situation, or an object • Types • Word association: When I say “GO” you say_____ • Sentence completion: Students at WSU are _____ • Picture tests: Write or tell a story in response to a picture • Thematic apperception test: series of pictures; you tell the story • Cartoon (balloon) test: fill in the dialogue of a cartoon • Role playing in a given situation Analyzing Qualitative Data • Inductive Approach • Goal is understanding why people do what they do and what products/service mean to them • Insights and theory-development are “bottom up” • They emerge as researchers read and interpret responses • Insights are “contextualized” within a culture/subculture (thick description) Nisbett & Wilson (1977, Psych Review) Telling More Than We Can Know: Verbal Reports on Mental Processes 1. Not aware of a response (snake phobics) 2. Not aware of a stimulus (the cord puzzle) 3. Not aware of a connection between stimulus and response (nylons) 4. So what? So this: we may need more creative interview techniques to get into people’s minds. 5. Enter Dr. Clotaire Rapaille Dr. Clotaire Rapaille Archetype Discoveries Worldwide http://www.rapailleinstitute.com/ I don’t care what you’re going to tell me intellectually. I don’t care. Give me the reptilian. Why? Because the reptilian always wins. Dr. Clotaire Rapaille • Internationally known expert in Archetype Discoveries and Creativity • Archetype: In psychology, according to the theory of psychologist Carl Jung, an idea or way of thinking that has been inherited from the experience of the race and remains in the consciousness of the individual, influencing his perception of the world. (Webster’s) • Dr. Rapaille's technique for market research based on his work in the areas of psychiatry, psychology, and cultural anthropology. • Dr. Rapaille searches for the “code” behind certain words and ideas (e.g., luxury), and uses these insights to help marketers promote their products. Dr. Clotaire Rapaille • On the Limitations of Traditional Marketing Researchers: • “They are too cortex, which means that they think too much, and then they ask people to think and to tell them what they think. Now, my experience is that most of the time, people have no idea why they’re doing what they’re doing. They have no idea, so they’re going to try to make up something that makes sense. Why do you need a Hummer to go shopping? “Well, you see, because in case there is a snowstorm.” No. Why [do] you buy four wheel drive? “Well, you know, in case I need to go off-road.” Well, you live in Manhattan; why do you need four wheel drive in Manhattan? “Well, you know, sometime[s] I go out, and I go—” You don’t need to be a rocket scientist to understand that this is disconnected. This is nothing to do with what the real reason is for people to do what they do. So there are many limits in traditional market research.” • Dr. Rapaille in action: Finding the code for “luxury” (42:20) The Reptilian Brain Reptilian Oldest part of brain from an evolutionary perspective Paul D. MacLean (1913 - 2007) Triune Brain Theory American physician • Reptilian brain (instincts) Neuroscientist • Limbic system (emotion) Yale, NIMH • Neocortex (higher order thought) Descriptive Designs: Surveys & Observations Chapter 8 Is X related to Y? When Are Descriptive Designs Appropriate? 1. Want to describe current characteristics of a market (e.g., attitudes toward an existing product or certain aspects of the marketing mix) 2. Want to understand your target market’s characteristics (e.g., demographics, psychographics) 3. Want to understand relationships between variables (e.g., price and purchase) or differences between groups (e.g., attitudes toward water filters between hikers and backpackers) Sampling vs. Nonsampling Errors • Sampling Error • Statistically speaking, the difference between the sample results and the population parameter • Assuming perfect survey, sampling frame, execution, and respondents, we will still have error due to sampling • Sampling error becomes smaller with larger sample • Nonsampling (or Systematic) Error • A variety of errors that are not related to sampling error and/or sample size Four Characteristics of Systematic Error • Nonsampling (Systematic) Error … 1. Leads to “systematic variation” in responses (e.g., skewed toward more socially desirable responses) 2. Is controllable (e.g., via good survey design and procedures) 3. Can not be estimated (whereas sampling error can be estimated; margin of error in a poll) 4. Are interdependent (i.e., one type of systematic error can lead to another) • Conceptual breakdown (Exhibit 7.2) – Handout in class Non-Response Errors • Non-response error occurs when… • The final sample differs from the planned sample • Often happens when you can’t contact those in the planned sample or they refuse to participate • Those who choose not to respond often of lower income, education, and more likely to be male • Non-response can limit generalizability of findings to broader population • Strategies for reducing non-response error – Create good rapport, respect respondent’s time, enhance credibility of research sponsor, use shorter questionnaires Response Error (Bias) • Response error occurs when… • The responses people give are not accurate • May occur due to • Deliberate falsification (e.g., social desirability, hostility) • Unconscious misrepresentation (e.g., faulty memory, desire to please researcher) • Might be able to detect with reaction times • Very fast or very slow RTs may tell you something Sampling Errors • Population specification (frame) error • Your population is all Republicans, but you define your population as Republicans in WA • Sample selection error • When an inappropriate sample is selected from the desired population • May be due to either poor sampling procedures or intentionally excluding certain people from the sample • Sample frame error • Sample frame = list of potential people in your target population • Sample frame error = when the sample frame is not representative of your population (e.g., only those with email addresses) Four Broad Categories of Survey Methods • Person Administered • In-home, executive, mall-intercept, purchase-intercept • Telephone Administered • Either by a person or completely automated • Self Administered • Panels, drop off, mailed survey • Computer Assisted • Fax, email, internet Person Administered Surveys Advantages Disadvantages • Interviewer can adapt to respondent • Can be slow • Interviewer can create good rapport • Interviewers may incorrectly interpret with respondents response (selective listening) • Interviewer can clarify questions and • Interviewers may give off “clues” to the get insight via non-verbal responses “correct” response • Interviewers can ensure they are • Can be expensive sampling the correct people Telephone Surveys Advantages Disadvantages • Can monitor interviewers for quality • Can’t use visual stimuli (though might control be possible with cell phones) • Less expensive than person administered • Can be hard to keep a large amount of info in memory during interview • Following up if respondent not available first time is inexpensive • People bail on long phone interviews • People who don’t agree to person administered (e.g., due to time • Public is distrusting; can limit sample constraints) may be more willing to do a telephone interview Self Administered Surveys Advantages Disadvantages • Low cost (no need for interviewer) • Can’t obtain any information beyond what is presented on survey (no follow up questions or probing possible) • Respondents not rushed, can take time if they want to • Low response rates • If respondent doesn’t understand, can’t • Interviewer can’t bias response ask an interviewer; may lead to response errors • Anonymity can lead to more truthful responses • Data comes in slowly; may require several re-contacts Paco Underhill The “King” of Observation www.envirosell.com When to Use Observation • When the respondent may not be able to accurately recall the frequency of a behavior, and/or may be inclined to give misleading answers • When the response in question is a behavior (rather than a feeling) • When the behavior in question is relatively frequent and occurs within a limited time frame • When the behavior in question can be observed (e.g., in public) Observation Advantages Disadvantages • Gain data on actual behavior (rather • Generalizing from a limited number of than self-reported behavior which observations can be difficult may be biased) • May be difficult to understand why the behavior occurred • If doing observation in person (not recorded), possible to miss important behaviors (or other people) Causal Designs Chapter 9 Understanding when (and why) XY Theories and Hypotheses • Theory • A body of interconnected propositions about how a phenomenon works (recall animosity model) • Hypothesis Testing • Null (Dull) Hypothesis (Ho): • Nothing interesting is going on • Any differences we are observing are completely due to chance • Alternative Hypothesis (H1) • Something interesting is going on • Differences in DV are due to IV Experiments: Some Key Definitions • Independent Variable (X, the cause, the predictor) • The variable you “manipulate” (good vs. bad aroma in store) • Dependent Variable (Y, the outcome, the criterion) • What happens after you manipulate the IV (sales of a product) • Control variables • Variables that you don’t allow to vary along with the IV • If any variable covaries with the IV, then there is a confound (e.g., if music systematically varies along with aroma, then you can’t tell if it’s the aroma or the music that influences sales) • Extraneous variables (or noise) • “Stuff happens” during an experiment, but it evens out across the levels of the independent variable (e.g., different music at different times, but it doesn’t systematically vary by the level of the IV) Factorial Designs • Factorial Design • When the researcher is examining the impact of two independent (or predictor) variables on a DV • Can have two main effects (overall impact of each IV) and an interaction (combined effect of two IVs) No Service Service Failure Failure Row Means Low Hostility 2 3 2.5 Main Effect of High Hostility 2 5 3.5 Hostility Column Means 2 4 Main Effect of Service Failure Interaction (Moderation) Moderation • Moderation (under what conditions is a relationship stronger/weaker) • When the effect of one IV (service failure) on the DV (negative word of mouth) depends on the level of another IV (trait hostility) Service Failure NWOM Trait Hostility 6 Trait Hostility Negative Word of Mouth 5 (IV 2) 4 Low Hostility (DV) 3 2 High Hostility 1 0 No Failure Service Failure Service Failure (IV 1) Mediation • Mediation (like a combination shot in pool) • When the effect of one IV on the DV occurs through an “intermediary” variable (think cue ball hits one ball hits eight ball) • For example, assume a person experiences a service failure • They infer a negative motive, feel angry, and spread NWOM • Here, anger is the mediator between Inference of Negative Motive and NWOM Inference of Anger Negative Word Negative Motive Of Mouth Validity: Some Key Definitions • Validity (in general) • The extent to which conclusions drawn from a study are true • Internal Validity • When a researcher can clearly identify cause and effect relationships (i.e., there are no confounds) • External Validity • The extent to which what you find in your study can be generalized to your target population • Construct Validity • Extent to which your constructs of interest (e.g., sensation seeking) are accurately and completely identified (measured) • In other words, the extent to which you are actually measuring what you say you are measuring (your sensation seeking scale really does measure the true construct of sensation seeking) Threats to Internal Validity • History Effect • When something (an historical event) happens during the course of a study that affects the dependent variable • Maturation Effect • Similar to a history effect; something happens over time (changes in the individual) that affects the DV • Testing Effect • In a pretest-posttest design, you affect the time 2 DV by pretesting at time 1; the simple act of measuring the DV at time 1 changes the DV at time 2 • Instrumentation Effect • The mere fact that you are measuring something (e.g., observing behavior) changes the behavior • Statistical Regression • When you select groups based on extreme scores, they regress toward the mean, changing your groups • Selection Bias • When groups (control, experimental) differ before experimental manipulation; creates unequal groups (a confound) • Mortality • Some drop out or die (attrition), and these drop-outs change scores in the condition; those who stick around may be different than those who drop out Experimental Research Designs • Terminology • X = subjects are exposed to a treatment (independent variable) • O = the outcome (dependent variable) • [R] = random assignment of subjects to conditions • EG = experimental group • CG = control group • = time • Pre-experimental (“Crude” experimental) designs • Either have no control group or non-random assignment to groups • Suffer from low internal validity because it is not possible to compare groups without the possibility of confounding factors • Types: One-shot, One-group, and Static Group comparisons Pre-Experimental Research Designs - 1 • One Shot Study • X O1 • How do customers respond (O) to a single product like gatorade (X)? • Problem? No control group. Response could be driven by many factors that covary along with the product (e.g., lighting, context). That is, there are many opportunities for extraneous variables to “confound” the manipulation of the IV. • Internal validity low Pre-Experimental Research Designs - 2 • One Group Pre-test Post-Test • O1 X O2 • How do sales of sweaters at time 1 (O1) change at time 2 (to O2) after the introduction of a new product display (X)? • Problem? No control group. History and Testing Effects. Response could again be driven by many factors that covary along with the manipulation of the product display, that change over time with the introduction of the new product display (e.g., changes in store music or changes in the economy), or are related to testing at time 1. In other words, there are many opportunities for extraneous variables to “confound” the manipulation of the IV. • Internal validity low Pre-Experimental Research Designs - 3 • Static Group Comparison (Non-random assignment to groups) • Experimental Group (EG): X O1 • Control Group (CG): O2 • Compare two stores. In Store 1 (EG), use a promo display for nose strips. In Store 2 (CG), don’t use a promo display. Compare sales. • Problem? Non-random assignment to groups. This again allows factors other than the promo display to affect sales. For example, in Store 1, it could be that the pharmacists are more friendly and more likely to recommend nose strips to their weary-eyed customers. • Internal validity low True Experimental Research Designs - 1 • Pre-test, Post-test Control Group (subjects randomly assigned to groups) • Also called a mixed design (one within-subject variable, time; and one between-subject variable, the experimental manipulation) • Experimental Group (EG): [R] O1 X O2 • Control Group (CG): [R] O3 …….. O4 • Treatment effect = (O2-O1) – (O4-O3): The difference between differences • Test all subjects (O1, O3), then randomly assign to experimental or control group, then test again (O2, O4) • Eliminates testing effects, maturation, and (with good control over experimental conditions) confounding factors. • Internal validity higher than earlier designs, but if not careful (low control over conditions), internal validity could be threatened True Experimental Research Designs - 2 • Post-test Only (but with subjects randomly assigned to groups) • Experimental Group (EG): [R] X O1 • Control Group (CG): [R] O2 • Randomly assign to experimental group or control group, then compare levels of dependent variable (O) • No testing effects. With good control over experimental conditions, eliminates confounding factors. • Internal validity higher than static group comparison, but if not careful (low control over conditions), internal validity could be threatened. True Experimental Research Designs - 3 • Solomon Four Group Design • Design 1 (Pre-test, post-test control group design) • Experimental Group (EG): [R] O1 X O2 • Control Group (CG): [R] O3 …….. O4 • Design 2 (Post-test only design) • Experimental Group (EG): [R] X O5 • Control Group (CG): [R] O6 • Let’s say that O = anger with waiting in line, and X = pleasant fragrances. • If [O2 < O1], [O2 < O4], [O5 < O6], [O5 < O3], strong internal validity! Quasi-Experimental Research Designs - 1 • Non-equivalent control group • Like pre-test, post-test control group, but it is groups of subjects (not individual subjects) who are randomly assigned to conditions. Hence there is no [R] shown below. For example, you could randomly assign stores to conditions (experimental vs. control), but you can’t randomly assign people to conditions, and you can’t control everything about the stores that may be confounded with the experimental manipulation. • Experimental Group (EG): O1 X O2 • Control Group (CG): O3 …….. O4 • Treatment effect = (O2-O1) – (O4-O3): The difference between differences • Equivalence of groups prior to treatment: (O3-O1) • Eliminates testing effects, maturation, and (with good control over experimental conditions) confounding factors. Scores at pre-test (O1 and O3) can be used as a control variable in data analysis. If in field, external validity is heightened over straight lab studies. Quasi-Experimental Research Designs - 2 • Separate sample pretest-posttest • Some folks (Sample 1) are tested before an advertising campaign (O1) • Then an advertising campaign occurs (X) • Then another group is tested after the campaign (O2) • Can’t be sure people were exposed to treatment (X), which is why it’s in parentheses • Sample 1: O1 (X) • Sample 2: (X) O2 • Some problems with internal validity (history, maturation), but external validity is high due to its naturalistic setting Sampling It is often said that without water, life would be impossible. Similarly, without sampling, marketing research as we know it would be impossible. Feinberg, Kinnear, & Taylor (2008, p. 290) Probability vs. Nonprobability Sampling • Probability Sampling • Each sampling unit has a known probability of being included in the sample • Nonprobability sampling • When the probability of selecting each sampling unit is unknown Probability Sampling Procedures • Simple Random Sampling • A sampling approach in which each sampling unit in a target population has a known and equal probability of being included • Advantage: Good generalizability and unbiased estimates • Disadvantage: must be able to identify all sampling units within a given population; often, this is not feasible • Systematic Random Sampling • Similar to random sampling, but work with a list of sampling units that is ordered in some way (e.g., alphabetically). • Select a starting point at random, then survey each nth person where the “skip interval” = (population size/desired sample size) • Advantage: quicker and easier than SRS • Disadvantage: may be hidden “patterns” in the data Probability Sampling Procedures • Stratified Random Sampling • Break up population into meaningful groups (e.g., men, women), then sample within each “strata”, then combine • Proportionate stratified sampling: here you sample based on the size of the populations (i.e., sample more from the bigger strata: e.g., Caucasians) • Disproportionate stratified sampling: sample the same number of units from each strata, regardless of the strata’s size in the pop. • A variant is optimal allocation: here you use smaller sample sizes for strata within which there is low variability (as the lower variability will give you more precision with lower N). • Advantages: more representative; can compare strata • Disadvantages: Can be hard to figure out what to base strata on (Gender? Ethnicity? Political party?) Probability Sampling Procedures • Cluster Sampling • Similar to stratified random sampling, but with stratified random sampling, the strata are thought to possibly differ between strata (men vs. women), but be homogeneous within strata. • In cluster sampling, you divide overall population into subpopulations (like SRS), but each of those subpopulations (called “clusters”) are assumed to be mini-representations of the population (e.g., survey customers at 10 Red Robins in WA). • Area sampling: clusters based on geographic region Probability Sampling Procedures • Cluster Sampling • One-step clustering: just select one cluster (e.g., one store); problem = may not be representative of population • Two-step cluster sampling: break into meaningful subgroups (Red Robins in big cities vs. Red Robins in suburbs), then randomly sample within each of those clusters • Advantages: easy to generate sampling frame; cost efficient; representative; can compare clusters • Disadvantages: must be careful in selecting the basis for clusters; also, within clusters, often little variability (they’re homogeneous), and this lack of variability leads to less precise estimates Nonprobability Sampling Procedures • Convenience Sample • Survey people based on convenience (e.g., college students) • Advantage: is fast and easy • Disadvantage: may not be representative • Judgment Sampling • Use your judgment about who is best to survey • Advantage: Can be better than convenience if judgment is right • Disadvantage: but if judgment wrong, may not be representative/generalizable Nonprobability Sampling Procedures • Quota Sampling • Sample fixed number of people from each of X categories, possibly based on their relative prevalence in the population • Advantage: Can ensure that certain groups are included • Disadvantage: but b/c you aren’t using random sampling, generalizability may be questionable • Snowball Sampling • You contact one person, they contact a friend (e.g., one cancer survivor is in contact with other survivors, and so recruits them) • Advantages: can make it easier to contact people in hard to reach groups • Disadvantage: there may be bias in the way people recruit others Factors Affecting Choice of Sampling Procedure • Use some type of random sampling if: • You are collecting quantitative data that you want to use to arrive at accurate generalizations about population • You have sufficient resources and time • You have a good sense for the population • You are sampling over a broader range (e.g., of states, nations) Computing the Sample Size Based on Usable Rates • Several factors can reduce your sample size • Thus, you may want to plan for more than your final sample size (i.e., use a higher “number of contacts” to achieve your final sample size). You adjust using the following three factors: • RR = reachable rate (e.g., how many people on a telephone list will you actually be able to reach?) • OIR = overall incidence rate (i.e., % of target population that will qualify for inclusion; e.g., can’t use people over 40) • ECR = expected completion rate (i.e., some folks won’t complete your survey) • For example, - Computing the Sample Size Based on Usable Rates • You want a sample size of n = 500 • You figure you can reach 95% of the folks on your list (RR = .95) • You think 60% will be 40 or younger (OIR = .60) • You predict that 70% will complete your survey (ECR = .70) • Based on these numbers, you should contact 1,253 people n 500 Number of contacts 1,253 (RR) x (OIR) x (ECR) (. 95 )(. 60 )(. 70 ) Some Key Terms • Sampling • Selection of a small number of elements from a larger defined target group of elements and expecting that the information gathered from the small group will allow judgments to be made about the larger group • Population • The identifiable set of elements of interest to the researcher and pertinent to the information problem • Defined Target Population • The complete set of elements identified for investigation • Element • A person or object (e.g., a firm) from the defined target population from which information is sought • Sampling Units • The target population elements available for selection during the sampling process • Sampling Frame • The list of all eligible sampling units Some Key Terms • Total Error = Sampling Error + Nonsampling Error • Sampling Error • Any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size • Nonsampling Error (controllable) • A bias that occurs in a reesearch study regardless of whether a sample or a census is used (recall all the different types of errors we discussed) • Respondent Errors (non response, response errors) • Researcher’s measurement/design errors (survey, data analysis) • Problem definition errors • Administrative errors (data input errors, interview errors, poor sample design) Central Limit Theorem • A theory that states that, regardless of the shape of the population from which we sample (e.g., positively skewed), as long as our sample size is > 30, the sampling distribution of the mean (x-bar) will be normally distributed with the following characteristics: x The mean of the sampling distribution of the mean will equal the mean in the population. s The standard error of the sampling distribution of sx the mean will equal sample standard deviation (s) n divided by sample size (n). This is a sample estimate of the true standard error in population. The larger the sample size, the more precise we can get about our estimate of the true mean in the population (e.g., in our confidence interval). variance Note: Dr. Joireman does not put a “bar” above s or s2. Computing Standard Deviation Assume your data are continuous (i.e., are not just yes/no data). For example, let’s say we want to know how much people would be willing to pay for a tennis racquet. We sample 7 folks and wish to generalize to the population…. Results Formulas for Variance and Standard Deviation Sum of Squared POPULATION Deviations SS PopulationVariance 2 N SS Population Standard Deviation N SAMPLE SS Sample Variance s 2 N 1 SS Sample Standard Deviation s N 1 The Sum of Squared Deviations (SS) Conc eptualFormula SS ( X i X ) 2 Highlights Concept Tells a Story Raw Score Formula ( X ) 2 SS X 2 N “Crank it Out” Faster, Less Meaningful • Both Formulas Give Identical Answers! • SS = NUMERATOR of the Variance • Examples on board… Example of Computing Standard Deviation (for a Sample) Xi Mean Xi-Mean (Xi-Mean)2 X2 60 75 -15 225 3600 65 75 -10 100 4225 70 75 -5 25 4900 75 75 0 0 5625 80 75 5 25 6400 85 75 10 100 7225 90 75 -15 225 8100 ΣX = 525 Σ(X-M) = 0 ! Σ(X-M)2 = 700 ΣX2 = 40075 ConceptualFormula for SS SS ( X i X ) 2 700 Raw ScoreFormula for SS (X ) 2 (525) 2 SS X 2 40075 40074 39375 700 N 7 SS 700 Sample Variance s 2 116.67 N 1 7 1 SS 700 Sample Standard Deviation s 10 .82 N 1 7 1 s 10 .82 Standard Error of Mean s X 4.09 n 7 This is the “standard deviation” of the sampling distribution of means. This (4.09) will naturally be smaller than our sample standard deviation (10.82) based on our single sample of scores, and it will become smaller as n increases. Confidence Intervals A confidence interval is the statistical range of values within which the true value of the target population parameter is expected to lie. Common Z-Critical Values • To be 90% confident, you use a z-critical value of 1.65 • To be 95% confident, you use a z-critical value of 1.96 • To be 99% confident, you use a z-critical value of 2.58 An example… Z-critical values for 95% confidence (put ½ of .05 on each side) .025 .025 -1.96 +1.96 Computing Confidence Intervals • 95% Confidence Interval: • We are 95% confident that the mean of the population from which we took our sample has a mean between these lower and upper limits. • To compute, we need: Critical Z-value for our Standard error desired level of confidence Mean of our sample of mean (see next page for Z-critical values) Confidence Interval.95 CI x (sx )(Zb, cl ) 75 (4.09)(1.96) 75 8.02 Restated, Confidence Interval.95 66.98 X 83.02 Based on these results, we are 95% confident that the mean in the population from which we sampled is between 66.98 and 83.02. Cool beans!
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