INTRODUCTION AND THE MARKET RESEARCH PROCESS !Marketing concept “Philosophy which holds that achieving organisational goals depends on determining the needs and wants of target markets and delivering the desired satisfactions more effectively and efficiently than competitors in a way that maintains or improves the consumers and society’s wellbeing.” !Definition of marketing research

• Marketing research is the systematic and objective: - Identification - Collection - Analysis - Dissemination - Use of information

• For the purpose of improving decision making related to the identification and solution of problems and opportunities in marketing.

• “Market research is the business function that links an organisation to its markets through the generation of information. The information facilitates optimal solutions to decision problems”

• “The principle tasks involved in conducting market research include establishing the parameters of the research, designing the research, executing the research, and communicating the results.”

• The American Marketing Association (AMA) redefined marketing research as: “The function that links the consumer, the customer, and the public to the marketer through INFORMATION.”

• Market research should produce information that is: - Relevant - Accurate - Reliable - Valid - Timely !The knowledge pyramid !!!!!!!!Research insight

• Unique and valuable pieces of data/information that provide in-depth knowledge and understanding about a research issue (e.g. market, stakeholders etc.) and that when used strategically can provide a source of competitive advantage or value. !

Classification of marketing research

!Marketing research process

!!!!!!!!!• A systematic progression beginning with questions, curiosity or speculation

combining with cognitive thought and objective analysis, facilitated by activities which conclude with reasoning and understanding. !

Types of market research • Primary research: Requires the gathering of specific reactions to your product

or service. It is information collected for a specific purpose/project. It is used for measurement purposes.

• Secondary research: Involves a close look at industry trends and information about what your market is doing. Refers to data that has been previously gathered for a purpose other than your project. It is like doing research in the library for a term paper.

• Quantitative research: Deals with numbers and statistics and answers questions about how many, how much or how often.

• Qualitative research: Deals primarily with the feelings and attitudes that drive behaviours. !!

PROBLEM DEFINITION AND RESEARCH OBJECTIVES !• “Problem definition is the most important stage of the research process.” • “The formulation of a problem is often more essential than its solution.” • “Problem definition is at the heart of the research.”

PROBLEM IDENTIFICATION RESEARCH

PROBLEM SOLVING RESEARCH

• Identifies problems not yet apparent.

• Used once the problem has been identified.

• Often undertaken for survival and long-term growth of the company.

• Undertaken to arrive at a solution.

• Used in making decisions to solve problems.

• “Managers spend 50% of their time dealing with the consequences of bad decisions.” !

Defining the decision problem • Problem definition involves stating the general problem and identifying the

specific components of the marketing research problem. • Critical in setting the directions for all subsequent phases of the marketing

research process. • Inadequate problem definition is a leading cause of failure of marketing

research projects. !The problem definition process !!!!!!!!!!!!!Collect the background information

1. Discussion with decision makers. 2. Interviews with industry experts. 3. Reviewing existing information/secondary data analysis (desk research). 4. Exploratory qualitative research. !

The problem or opportunity audit • The problem audit is a comprehensive examination of a marketing problem

with the purpose of understanding its origin and nature. • Management decision problems and marketing research problems encompass

both problems and opportunities. • A problem audit should uncover:

- History of the problem - Alternative course of action - Criteria for making a decision - Possible solutions or actions - Information needed for specific decisions - How information will be used - Corporate culture of the organisations !Common marketing problems

!!!!!!!!!!Factors to be considered in the environmental context of the problem !!!!!!!!!!!MDP and MRP

• Management Decision Problem (MDP): What the decision maker needs to do, action oriented, e.g. should the advertising campaign be changed; focuses on symptoms.

• Marketing Research Problem (MRP): What information is needed and how should it be obtained; information oriented, e.g. how can effectiveness of the current advertising campaign be measured; focuses on the underlying causes. !

MDP MRP

• Should a new product be introduced?

• To determine consumer preferences and purchase intentions for the proposed new product.

• Should the advertising campaign be changed?

• To determine the effectiveness of the current advertising campaign.

!Management decision

• Should the price be cut in response to a price-cut by a competitor? • Should the product ‘X’ be introduced in the market? • Should an advertising campaign that has run for three years be changed • Should an in-store promotion for an existing product line be increased • What price strategy should be adopted for a new product • Should the compensation package be changed to motivate the sales force

better? !!!!!Defining the market research problem

• Broad statement: To understand the decision making process of potential university students in their choice of university.

• Specific components: To determine the factors potential students consider to be important in selecting a university; to determine when decisions regarding university selections are made; to determine sources of information and people who influence potential student’s decision regarding choice of university. !

Good sample objectives • Identify areas of satisfaction and dissatisfaction with children’s programming. • Define customer requirements for large-format printing capability. • Explore reactions to proposed discontinuation of government document

depositary status. • Evaluate support for in-library food service. • Select a theme for this year’s fundraising program. • Test user and non-user preferences for Web-based audio download services. • Monitor trends in customer replacement of automobile tape and CD players

with MP-3. • Track customer awareness of online holds service. !

Development of research questions and hypotheses !!!!

• Should the price of the brand be increased?

• To determine the price elasticity of demand and the impact on sales and profits of various levels of price changes.

!!!Research questions and hypotheses

• Research questions (RQ): Refined statements of the specific components of the problem

• Hypothesis (H): An unproven statement or proposition about a factor or phenomenon that is of interest to the researcher. Often, a hypothesis is a possible answer to the research question. !!

SECONDARY DATA !• Primary data: Data that is collected by the researcher in order to answer their

research question(s). • Secondary data: Data that has already been collected by someone else for

some other purpose. • Lots of secondary data and information is freely available. • Secondary data generally requires further processing prior to use. • Some information is public but not free, but can often be cheaper than

gathering it yourself. • Care needs to be taken in order to assess quality and usefulness of secondary

data or information. !!RESEARCH DESIGN !Research design comprises

1. Define info needed. 2. Design exploratory, descriptive and/or causal phases. 3. Specify measurement and scaling procedures. 4. Construct and test instrument(s) e.g. questionnaire and choose modes of

recruitment and response. 5. Specify sampling process and sample size. 6. Develop a data analysis plan. !

Fundamental issues (1) • The best way to find out how customers (and non-customers) behave is to

observe them, ideally without their knowledge because people are likely to act differently if they know they are being observed.

• However this may not be ethical (right to privacy), possibly (practically), affordable (time and money) or valid (any given behaviour may have more than one cause).

• Compromises often must be made. !Fundamental issues (2)

• The next best way to find out how consumers behave is to ask them both how they behave and why.

• People may not be willing to help; they may not be able to accurately describe either actions or reasons.

• What we say vs. what we actually do, often we don’t know why we behave, or we may give a reason that isn’t the “real” one.

• We may not ask our questions in a way that can be understood. !Dealing with the issues

1. Talk to people (and observe them) “in depth” i.e. for a long time and maybe multiple times.

• Most valid strategy, however, time consuming and expensive so can only study a small number of people.

• Small sample means that findings may not be generalizable. • Lots of feedback necessary over extended period of time. 2. Communicate with large numbers of people (sample surveys). • Use pre-defined questions and responses. • One-shot communication so careful planning is required. !!!

Styles of research • Strategy 1 (in-depth, small samples, words and interpretation) is called

Qualitative Research. • Strategy 2 (large samples, measuring and counting) is called Quantitative

Research. !Types of research

• Objectives – exploratory, descriptive, causal. • Time – cross-sectional, longitudinal. • Samples – single, multiple. !

Exploratory research • Objective: To provide insight and understanding, generate ideas. • Used: When RQ is vague, need to generate hypotheses or alternatives for

action, or plan future (conclusive) research. • Characteristics: Flexible research design, qualitative data, small and non-

random samples, often first stage in larger research programme. • Methods: Expert surveys, case studies, secondary data, qualitative research. !

Descriptive research • Objective: Describe market characteristics or functions. • Used: Quantification of marketing variables, correlation between variables,

making predictions, assessing brand image, attributes of marketing entities (consumer, competitors, partners).

• Characteristics: Prior specification of hypotheses, planned and structured design, random samples, quantitative.

• Methods: Surveys, panels, observation, secondary data. !Causal research

• Objective: Identify cause-and-effect relationships. • Used: Test hypotheses, predict outcomes of alternative actions (so can choose

between them). • Characteristics: Manipulate one or more causal variables, measure effect on

dependent variable(s), control for other variables, random or non-random samples.

• Methods: Experiments (lab and field), test markets. !Types of data

1. Type of “measure” – qualitative or quantitative. 2. Time – cross-sectional or longitudinal. 3. Level of measurement – nominal, ordinal, interval and ratio. !

Quantitative • Quantitative = numbers. • Measures of actual or self-reported behaviours, demographic characteristics,

attitude scales, personality traits etc. • Sourced from questionnaires, experiments and direct observation. • Sometimes a rather blunt instrument, but necessary for pragmatic (cost, time)

reasons. • Most common way of doing commercial research and academic research. !

Qualitative • Qualitative = not numbers (usually words, sometimes sounds and/or images). • Sourced from in-depth interviews, focus groups, projective methods, case

studies, secondary reports. • Can describe motivations, feelings, barriers and obstacles etc. more richly than

e.g. “I like this brand” = 4 (on a 5-point scale). !Time

• Observation/measurement over time is necessary to identify causal relationships (multiple observations per sampling unit).

• Expensive and slow - most ad-hoc markets research is cross-sectional. • Cohort studies: Same research design applied to different sub-populations

(often age groups) – usually descriptive e.g. consumer confidence surveys, political opinion polls, readership surveys etc. !

What could go wrong? • Managerial, researcher and respondent mistakes. • Manager-researcher interface. • Researcher-respondent interface.

• Role of intermediaries (field workers, data analysts, data entry operators etc.). !!!!!!!!!!!Budgets

• Information is gathered in order to make a decision. • Marketing decisions have cost and potential revenue implications. • Budgets are often set historically (if you want to spend more, you need

justification). • What is the cost of obtaining the information and the profit projection of the

action(s) being considered? !!!Justifying spending

• What is the value of the information? Can it help us reduce costs? Can it help us increase revenue?

• How? 1. Identify likely costs and revenue of an action more precisely. 2. Estimate likelihood of success of an action. !

Schedules • Generic planning – the completion of backwards principle. • When do we need the “answer”? • What has to be done to provide it? • How long will each of those things take? • Can any of them be done in parallel? – Which one has to be finished in order

for the others to start? !!DATA !Fundamentals

• A datum is a unit of observed information e.g. the sex of a respondent to a survey.

• It is sometimes useful to distinguish data from information – information is derived from (analysis of) data.

• Data is analysed to produce information that is then interpreted to produce knowledge. !

Why use secondary data? • Collecting data is expensive in time, money, and other resources – always

check to see whether you need to! • Someone else may have already collected either the exact data you need, or

something similar to what you need. • Quick and cheap, but if you haven’t collected the data yourself you don’t

know how trustworthy it is. • “Examination of available secondary data is a pre-requisite to the collection

of primary data. Start with secondary data. Proceed to primary data only when the secondary data sources have been exhausted or yield marginal returns.” !

Sources of secondary data • Internal: Company records e.g. sales and cost data, customer enquiries, CRM

databases, sales force feedback etc. • External: Public (anything freely available) and private (stuff you have to pay

for). • Public sources include the Government and media (news, magazines, blogs)

academic lit, and practitioner lit. • Private sources include specialised market research providers. !!

Public sources • Heaps of freely available data and information useful for marketing purposes. • Free stuff mainly from Governments and International NGOs e.g. StatsNZ,

World Bank, UN etc. • Paid (but public) stuff mainly from publications by industry organisations. • Academic sources (e.g. journal articles, books) are free to students but not to

businesses (but still public). !Private sources

• Household or consumer data from “omnibus” surveys e.g. Roy Morgan. • Consumer panels e.g. Smile City. • Standardised sources – worldwide global sources of market information e.g.

Nielsen, EuroMonitor. • “Syndicated” sources (aggregate and sell data sourced from) – EFTPOS and

scanner data, loyalty programmes, web tracking etc. !Ethical issues

• Many people around the world don’t like businesses gathering, selling, buying and using data about them.

• New Zealand Privacy Act: “Data should not be used for a purpose other than for which it was gathered, or divulged to third parties without consent.”

• Basic issues – the rights we have to privacy/anonymity, no harm and informed consent.

• Also issues relating to client and research provider. !Evaluating secondary data

1. Specifications/methodology: • How was the data collected? • If from a survey, what was the response rate? Is the sample representative of

our population of interest? • What was the sampling technique and sample size? • How were the questions asked? (Can we see the instrument?) • How was the fieldwork conducted?/How was the data analysed? 2. Errors/accuracy: • Evaluate research approach, research design, sampling, data collection, data

analysis and reporting. 3. Currency: • When was the data gathered?/Is it still valid or relevant today? 4. Objective: • Why was the data gathered? 5. Nature: • Definition of variables/concepts, units of measurement, categories, and

relationships examined. 6. Dependability: • Reputation (expertise, credibility).

Metric vs. non-metric measurements • One way to distinguish between a quantity and a quality is to ask, “Can it be

mapped onto the number line sensibly?” • If the different values in question don’t have natural order and known distance

between them, we can’t. • Variables that can be mapped to the number line are metric and those that can’t

are non-metric. !Levels of measurement

• Categorical (non-metric) vs. continuous (metric) – nominal, ordinal, interval and ratio.

• Nominal: Means “of names” i.e. a description. The values of nominal variables give information about the qualities/attributes of objects (what type of thing are they?) Those categories have no unambiguous and objective ordering. E.g. sex, town of residence, ethnicity.

• Ordinal: Values of ordinal variables give information about order (greater/lesser, bigger/smaller). Classic example is frequency of [whatever] – never, sometimes, often, always. Values have unambiguous and objective ordering (never < sometimes < often < always). No information on how much difference between values.

• Interval and ratio: Values of interval variables give information about distance i.e. not just greater, but how much greater. Classic example is net income or

net wealth. Ratio variables have all the properties of interval variables, plus negative values are not possible/meaningless. Classic examples are age, height and weight. May be called “scale” variables. !

LoM and their properties • The properties of description, order, distance and

origin are cumulative e.g. interval has description, order and distance (but not origin) properties.

• Implication: Any analysis method that is valid for a lower level of measurement is also valid for higher ones. !

Rating scales • In the social sciences, it is acceptable to treat them as interval. • You can choose either ordinal or interval, but be consistent. !!

QUESTIONNAIRE DESIGNS !Scaling – comparisons

• Comparative: Comparing alternative explicitly e.g. Which do you prefer, Freeview or Sky?

• Non-comparative: Rating something on some scale e.g. like, would buy, would recommend.

• Vast majority of commercial and academic market research is non-comparative. !

Types of questions • Closed (i.e. pre-defined answers) vs. open questions, e.g. “Are you male or

female? Please write M or F in the box below” vs. “What do you think of Microsoft? Please write your answer in the space below.”

• Can have semi-open questions e.g. “Which TV channels do you mainly watch for news – TV1, TV3, Sky News, Other (please specify.” !

Open questions • Pros: Can provide much richer and more useful information. • Cons: Easy to write questions, but increased possibility of ambiguity. Requires

more effort from respondents. Requires much more work to code responses for analysis.

• Open questions are rare in quantitative research. !Closed questions

• Can be single response (i.e. pick one from a list) or multiple (i.e. tick all that apply).

• Pros: Easier for respondents. Minimal effort for data entry. • Cons: Pre-defined responses must be mutually exclusive and exhaustive, but

not always clear how to provide that.

• Closed questions are almost never used in qualitative research. !Rating scales

• Most common form of scale in market research. • Usually five or seven points. • Two common types: 1. Semantic differential scale: A scale with antonyms at each end e.g. “not

important” on one end and “very important” on the other. 2. Likert scale: A scale with “strongly agree” on one end and “strongly disagree”

on the other. • How many points? Preferable to have an odd number of points so there is a

middle number that represents ambivalence. Usual options are 5 and 7, 9 and 11 are sometimes used.

• Include a “don’t know” or “no opinion” category. !Difficulty of questions

• Seemingly simple questions can be hard to answer e.g. "How many hours per week do you normally watch television?”

• Does the respondent have a ready-made answer to your question? • To what extent do they have to perform? 1) Recall, 2) Calculation. !

Model of cognitive response !!!!!Question wording

• The single biggest source of “error” in sample surveys.

• Seemingly simple and unambiguous questions often aren’t. • Usually must assume a very low level of literacy. • Watch for “double-barrelled” questions (those posed as one question but they

have more than one answer) and “leading” questions (those that you may get a different answer if the question was asked in a different or more neutral way). !

Emotive words • Some seemingly innocuous words can provoke a powerful subconscious effect

– “Should a smack as part of good parental correction be a criminal offence in New Zealand?”

• Version 1: “Do you think the US should allow public speeches against democracy?”

• Version 2: “Do you think the US should forbid public speeches against democracy?” !

Other problems • Implicit assumptions: 1) Are you in favour of legislation to reduce carbon

emissions? 2) Would you be in favour of legislation to reduce carbon emissions if businesses passed the costs to consumers, raising average prices of food by 10%?

• Implicit comparisons/alternatives: 1) Do you intend to vote for Labour at the next election? 2) Do you intend to vote for Labour, for another party, or to not vote at all?

• Ambiguity: “How often do you eat out?” 1) Never, sometimes, often, very often, almost always. 2) Never, at least once a month, at least once a week, almost everyday, everyday. What does ‘eat out’ mean?

• Sensitive questions: Some topics are sensitive e.g. sexual behaviour, illegal and recreational drug use, racial issues, and unpopular political or ethical views. Tricks – use income ranges as broad as possible; same with age or ask year of birth. !

Question order effects • Put interesting and/or easy questions first and harder/boring/sensitive

questions last – always put demographic questions (e.g. age, income) last. • Try to make the sequence and/or sections logical. • Can move from specific to general or vice versa, depend on situation. !

Priming and anchoring • Priming: When thinking about a previous question affects the answer to a

subsequent question 1. Specific ! general gives a frame of reference (start with precise questions). 2. General ! specific combats reaction effects. • Anchoring: When something is suggested as “normal”, “good”, “bad” etc. by

a previous question (made into a reference point). Response set

• Response set: The tendency for a person to respond to a set of questions in a certain way, regardless of the content of the questions.

• Examples: Using the extremes of rating scales, always responding positively/agreeing (acquiescence bias).

• Combat this by mixing positive and negative word statements. !Size of order effects

• An “order” effect is the % difference between answers between the same questions asked in a different place.

• Order effects can be large (up to 20%). • Can occur especially in general or summary questions. • Also when respondents are ambivalent. !

Answer set effects • In an unordered list the first one or two items tend to get picked up most often. • In an ordered list the middle items are picked more often.

• The most well known object will usually be rated highest on all attributes. • The most liked object will generally be rated highest on all attributes. !

Implication – testing • Testing is essential no matter how much effort and care was put into

preparation, mistakes are (seemingly) inevitable. • Test the full and final version of materials. • Consider cognitive interviews where you ask testers to “think aloud” as

they’re answering the questions. !Interviewer administration

• Questionnaires can be self-completion of interviewer administered. • Interviewer effects: The appearance, tone of voice etc. may affect the answers. • Balances with possibility of helping respondent to understand questions and

choose responses. Modes of administration

• Mail: Slow, good for difficult to answer questions.

• Telephone: Fast, best for only simple questions.

• Interviewer: Best for difficult questions, possibility of interviewer bias. • Internet: Cheap, issues of coverage and computer literacy. !

Response rates • Typical response rates vary by medium of contact and response. • Telephone response rates are declining. • Personal interviews most expensive per contact. • Mail and Internet now most preferred; both if you can. • Low response rates are a threat to validity. • Biggest factor affecting response is interestingness to respondent. • Care and attention to detail can boost rates considerably (10-20%+). • Pre-notification letter: Say why it’s important and ask for help. • Token of appreciation: Give it whether people participate or not. • Appearance of materials: Clear, clean and aesthetically appealing. • Following up non-response: Respectful reminder letter. !

Visual formatting • It must be readable with no smaller than 9-point fonts. • Use one, or at most two different fonts. Serified fonts are better than sans-

serif. • Use bold and italic sparingly, and generous white space. • Limit colours to those that are readable to the colour-blind. • Some evidence that aesthetically pleasing design can boost response rates by a

small amount (1-2%). !

Pros and cons of questionnaires • Pros: No interviewer effects. Standard stimulus (“objective”). • Cons: Wording of questions can affect the answers you will get. Response

format and order of questions can also affect answers. Number of questions can affect answers and whether people will choose to help you with your research. !

Ethical issues – client/respondents • Construction: Writing biased questions is deliberately unethical. • Reporting: Sometimes the seemingly obvious needs to be pointed out

explicitly e.g. self-selection bias. • It is unlikely that your questions will harm people, but possible – emotional

distress cause by sensitive topics. • More likely to mislead respondents when recruiting – length of questionnaire,

topic etc. • Harder to anticipate, yet another reason why testing is essential. !!

DESCRIPTIVE STATISTICS AND GRAPHICS !Descriptive statistics

• Numbers that describe aspects of a bunch of other numbers. • Location/central tendency: Mean, mode and median. • Dispersion/variation: Variances, standard deviation, range, IQ range. • Shape: Skewness or kurtosis. !

Measures of location • Mean: What is commonly called “average” (sum of values/no. of values). • Median: The middle value when the cases are arranged in order. • Mode: The most frequently occurring value. !

Measures of dispersion • Range: Highest minus lowest value. • Quartiles: Values that chop the data into equal quarters – 25% of cases are

below the first quartile, 50% below the second etc. • Inter-quartile range: The middle 50% of the values. • Variance: Average (squared) deviation from the mean. !

Measurements of shape • “Bell curve” = normal distribution. • Symmetric = bell-shaped. • All normal distributions are bell-shaped, but not all bell-shaped distributions

are normal. • Skewness: Measure of asymmetry. • Kurtosis: Measure of how many more or fewer “extreme” (or “typical”)

values than normal distribution.

!!!!!!!!!• Skewness: Positive values indicate there are more cases smaller than the mean. • Kurtosis: Positive values indicate more cases closer to the mean i.e. peaked. !

Appropriate measures • Any measure that involves arithmetic can only be used for metric (continuous)

variables. • All measures can be used for metric variables. • Sensitivity to extreme values (skewness and outliers) – median more robust

than mean. • Common mistake – calculating mean of nominal or ordinal variable. !

Graphical presentation • Pie charts: Shows frequency of distribution of a categorical variable. Not

useful for analysis purposes. • Bar charts: For categorical variables. • Histograms: For continuous variables. • Box plots: Shows distribution of one or more continuous or ordinal variables.

More useful as multiple plots. !General principles

• Always plot every variable that you’re analysing. • Never rely solely on numerical measures. !!

INFERENTIAL STATISTICS !Definitions

• Population: All the objects that we are interested in. • Sample: Subset of population. • Parameter: Property of population e.g. mean, median. • Statistic: Property of sample e.g. mean, median. • Sample mean: Estimation of population parameter.

Notation !!

!!!Logic and inference

• Logic: Rules for inference; a premise for making conclusions. • Inference: Drawing conclusions from combinations of premises. • Deduction: Inference from general statements to particular cases, e.g. all men

are mortal and Socrates is a man, therefore Socrates is mortal. • Induction: Inference from particular cases to general statements, e.g. all of the

swans I’ve seen are white, and therefore all swans are white. • We live our lives by the process of induction, e.g. in court, task is to prove the

statement beyond reasonable doubt. • Statistical inference is inductive i.e. sample ! population. • Scandal of induction: Nothing of the form “All X are Y” can be proved by

induction (unless all X are observed). • Statistic never proves anything! !

Probability • Frequentist: Expressed as proportions “in the long run.” • Bayesian: Degree of belief, e.g. probability of a tossed coin coming up heads

vs. probability of rain tomorrow. • Probability distributions tell us what will happen in the long run i.e. of all the

things that could possibly happen, how likely is each one to happen? !The normal distribution

• A mathematical curve that often approximates real data. • If samples data looks approximately normal, we can use the Normal to make

inferences about the population. • Standard normal: a normal distribution with μ=0 and σ=1. !

The x • Even if data is not normally distributed, many statistics calculate from them

are. • Central limit theorem applies when: 1. Sampling is random. 2. Each sample is of size 30 or more. • No matter what the distribution of the variable, under repeated sampling the

closer a histogram will move towards a Normal Distribution. !Confidence

1. Calculate a statistic from sample data. 2. Use that to make an inference about the parameter. 3. Need to state how confident we are about that estimate. !

Precision and sample size • Narrow confidence intervals are desirable.

• Two ways to this: 1) Decrease variance, 2) Increase sample size. • Easiest to increase sample size (how could you decrease variance??).

Possible outcomes !!!!!!The p-value approach

• The p-value is the (maximum) probability of making a type I error; you want this to be a small value.

• One is not limited to pre-defined confidence levels. • Consider “the null hypothesis was rejected at the 5% level” vs. “the p-value is

0.049.” !Cross tabulation

• Shows relationship between two categorical variables, e.g. income and taking longer showers. !

Observed and expected • Are the counts consistent with hypothesis of no association between the

variables? • What would we expect the counts to be under H0? • Example: If income were unrelated to sex, then the count of females in each

income bracket would be approximately the same as the number of males in that bracket.

• Expected count: In general, the expected count in each cell is proportional to the marginal counts i.e. the total count in the row and column to which each cell belongs.

• Residual: Difference between observed count (O) and expected count (E) for each cell – negative values mean “fewer than expected”, and vice versa. !

The Pearson x2 test • What does the sample data tell us about the population? • H0: Variables are independent (implies O-E = 0). • HA: Variables are not independent. !

Adjusted residuals • Scaled to have mean = 0 and s = 1, and be approximately normally distributed. • Absolute values > 2 indicate “significant” contribution to x2 at the 95%

confidence interval. !E < 5 assumption

• Cells with E < 5 bias the x2 statistic (p-values are not valid).

• You can deal with this by collapsing adjacent cells. • More than 10% of cells with E < 5 invalidates p-values. !!

CROSS TABULATION AND THE X2 TEST !Statistical significance

• Mantra: If p < α then reject H0. !!!!!!!Interpreting crosstabs

• Not sufficient to say “there’s evidence that A and B are not independent.” • Interpret cross tabulation if the x2 test is significant. 1. Patterns or proportions. 2. Patterns of residuals. 3. Strength of relationship. • If only a few cells influence size of x2, look for larger residuals. • Adjusted standardised almost always more useful than raw. !

Interpreting proportions • Of people who don’t already take

longer showers, 19% have incomes under $60k.

• If income has no influence on behaviour, income distribution should be the same within each behavioural category. !!

• Of people with incomes under $60k, 70% already take shorter showers.

• If income has no influence on behaviour, proportion of people in any behavioural category should be the same in each income bracket.

• Need to scan only one row (three cells). !

• Easiest (less cognitive effort) to calculate % in direction of causal factor.

• In the example above, the causal factor is the columns, so calculate % by column and compare across rows.

• Think about which variable is the cause and which is the effect before calculating %. !

Measures of association • The x2 statistic is not a measure of “strength” of association. • The chi-square test tells us if the null hypothesis is false or not, but does not

show the magnitude or strength of relationship between variables. • Many measures of specific situations, only some for ordinal, others for

nominal. • Magnitude of residuals depends on magnitude of counts. • All are normed to lie in the interval [0,1] or [-1, 1]. !

Adjusting for ties • If two ordinal variables are positively related, as the value of one goes up, so

does the value of the other. • Measures of ordinal association rely on notion of concordant pairs: - A pair of observation are concordant if they are “telling the same story” about

the association between the variables. - And discordant if telling the opposite story. !

Concordant pairs • Consider a pair of two cases, each with an observation on two variables: {(X1,

Y1), (X2, Y2)}. • If X and Y are positively associated, then: 1. If Y1 > Y2, then X1 > X2 also. 2. If Y1 < Y2, then X1 < X2 also. • Example: Let X be education and Y be income. If Jane has more education

than Dave (X1 > X2), then Jane should have higher income than Dave (Y1 > Y2).

• Left hand table: In each table, the cases in the grey cells are concordant with the cases in the black cell.

• Right hand table: In each table, the cases in the grey cells are discordant with the cases in the black cell.

!Illustration of ties

• The cases with values (1,1) are

tied with: -Each other on

both variables. - All the cases in the top row on Education. - All the cases in the left column on Income. • They are neither concordant nor discordant.

!Interpretation of measures

• Interpret similar to correlation coefficients: - Less than 0.3 is “low.” - 0.3-0.6 is “moderate.” - Above 0.6 is “high.” • Negative values: As one variable increases the other decreases. • 0 indicates no association, 1 or -1 “perfect association, i.e. if we know the

value of one variable we can predict the value of the other. !Caution

• The fact that A and B are correlate is not enough to prove anything about the causal relationship between variables.

• Three conditions for causation: 1. Temporal precedence (the cause precedes the effect). 2. Correlation. 3. Lack of alternative explanations. • Mantra: “Correlation is not causation.” !!

COMPARING MEANS !Student’s t-test

• Compares the difference between two means. • One-sample test: Compares the mean of one variable with a hypothesised

value. - May have a rating scale and you want to compare the mean with a different value from the hypothesised.

• Independent samples: Compares the mean of one variable between two groups of cases.

- The people in one group don’t influence the people in another group. • Dependent samples: Compares the mean of two variables within cases.

- Relatively correlated to some degree. • H0: Differences in means is zero. !

Mean shift model

• Unless an extremely large sample is taken, you can’t get Normal Distribution.

• Just looking at the histogram doesn’t give a huge amount of information for comparison.

• T-test ! difference between the means of two variables. !

Directionality • One-tailed test: For cases where one mean can

only be greater/lesser than the other. • Two-tailed test: Where one mean could be either greater or lesser than the

other. • One-tailed tests are more powerful than two-tailed tests – need very strong

justification (almost never justifiable in practice). !Assumptions of t-tests

1. Each mean is from a Normally Distributed population 2. The variances of both populations are equal • The second assumption can be tested using Levene’s test and if the assumption

is not met, an adjusted t-statistic can be used. • SPSS produces unadjusted and adjusted tests in one table – you choose which

to interpret based on significance of Levene’s test. !Interpreting SPSS output !!!!!!!!!!!!!

• Results of three separate hypothesis tests in one table. • Levene’s test is significant, so interpret bottom row of t-test table. • “The mean of the lower income group is 2.6, and the mean of the higher

income group is 2.2. This difference in means is significant at the 5% level (t-3.56, p < 0.000). Therefore there is strong evidence that lower income people agree more with this statement than do higher income people.” !

Analysis of variance (ANOVA) • ANOVA: Compares more than two means. • Test statistic (F) is the ratio of between-group variation to within-group

variation. • Assumes roughly equal sample sizes in each group (no more than an order of

magnitude inequality). • Each group should have n=30 or greater. • Also assumes equal variances.

Violation of assumptions • If equal variance assumption is violated, can use an adjusted F statistic.

• SPSS doesn’t produce an adjusted F statistic by fault, so we’ll use the Welch adjustment.

• If group size assumption is violated, collapse or remove categories. • The F test is extremely robust to violation of assumptions. !

The ANOVA table • Test statistic: F ratio. • H0: All the means are equal. • HA: At least one of the means is different from at least one of the other means. • If it’s significant we need to find out which means are different from which

other mean(s). !Interpreting ANOVA tables !!!!!!

• Only one really interesting number and that is the p-value (“Sig.”). • It is traditional when reporting results to also quote the value of the test

statistic. !Post hoc tests

• The F test does not tell us which means are different from which other means. • We need to know if each pair of means shows a significant difference. • Testing multiple hypotheses on the same data leads to “α inflation.” • Type I error rates increase, hence p-values become inaccurate. !

Which post-hoc test? • For equal variances: Tukey’s HSD test is preferred because it is the most

powerful. • Assumption: Equal n in each group (more critical than for F test). • Can be used when the ns are not too different – rule of thumb is = when

harmonic mean sample size ±25% encompasses all ns. • Bonferonni is used if the assumption of Tukey’s HSD is violated. • For unequal variances: Use Tamhane’s T2 test (it doesn’t matter if the ns are

unequal). • So procedure is: 1. Check equality of variance; if unequal use T2. Otherwise go to step 2. 2. Check ns. If within ±25% of harmonic mean sample size, use HSD. Otherwise

go to step 3. 3. Use Bonferonni.

Effect size • Recall measures of association from crosstabs; similar thing for ANOVA.

• Same range: [0,1]. • Guidelines for applying adjectives to ranges: - [0.2,0.5] “low” - [0.5,0.8] “moderate” - [0.8,1] “high” !

The normality assumption • We can test for homogeneity of variance assumption and can adjust t and F

statistics if variances are unequal. • What about assumption of Normal populations? 1. Already know how to construct confidence intervals for skewness and

kurtosis. 2. There are formal (null hypothesis) tests. !!

NON-PARAMETRIC STATISTICS !Parametric statistics

• “Parametric” statistical tests assume that variables are sampled from a specific distribution e.g. Normal, Poisson, Binomial.

• Such distributions are completely described by one or two parameters e.g. mean, variance, degrees of freedom.

• Examples: t tests, ANOVA, Pearson’s r. !Non-parametric statistics

• “Non-parametric” statistics do not assume any distribution e.g. Spearman’s, Pearson’s x2.

• Non-parametric tests are in general less powerful than parametric alternatives. • Often used when parametric assumptions are not met.

The Mann-Whitney U test

• Non-parametric alternative to t-test. • Used for comparing two distributions. • Assumption: Two distributions have same shape; this tests

whether they have different locations. • Used for variables that are: 1) Ordinal, 2) Metric but not

Normally Distributed. !The Kruskall-Wallis H test

• Non-parametric alternative to ANOVA. • No post-hoc tests available (can run multiple Mann-Whitney U tests). • Same assumptions: Distributions have same shape but different locations. !!!

The concept of rank tests

• Both Mann-Whitney U and Kruskall-Wallis H are based on analysis of ranks. • Example: Assignment marks – What is level of measurement? Would we

expect them to be Normally Distributed? • Appropriate test to use would be a non-parametric test to compare males vs.

females etc. !Analysis of ranked data

• Females have higher mean rank, so they performed better.

• Interpretation: Males are thick. • Alternative interpretation: Assessment is sex-biased. • SPSS signs low values of ranks to low values of the variables. !

Assessing normality • Cannot assess Normality by looking at a histogram. • Confidence interval approach: Normal distributions have kurtosis = 0 and

skewness = 0. • Can construct confidence intervals. • Easier to use a formal null hypothesis significance test. • H0: There is no difference between the distribution of the variable and the

Normal Distribution. !!SAMPLING !Sampling plans

• A sampling plan is a key component of a research plan. • A “Sampling Plan” is a definition of: 1. The target population 2. The sampling frame 3. The sampling method 4. The sample size 5. The procedures to implement the plan !

Sampling frames • We need some way to choose things to sample. • Sampling frame: “List” of things to be sampled i.e. something to pick from;

can also be a rule or algorithm, e.g. people walking past a shop entrance, random digit dialling.

• In market research it is often difficult or impossible to obtain an actual list. !Sampling units

• Sampling units are the objects “listed” in the sampling frame. • The sampling unit may or may not correspond directly to the elements of the

target population e.g. interested in people but sample households or telephones.

• Sometimes have two levels of sampling – the “top level” units are called Primary Sampling Units (PSUs) e.g. sample households then one householder in each, house is PSU. !

Unit and item response • All human sampling (at least in marketing) is voluntary – people can choose

not to respond. • They can choose not to participate at all (unit non-response) or answer some

questions but not others (item non-response). • Can be a problem – choice of whether to respond may be related to aims of

study e.g. estimating use of illegal drugs. !Dealing with non-response

• People are more likely to respond if they find the survey topic interesting – can “bias” results if the goal is to estimate something in the wider population ! self-selection bias.

• Can try to increase response by: - Multiple contacts – requests (“call-backs”) and reminders. - Mixed modes (mail, phone etc.). • Can assess impact of non-response by trying really hard to contact small

sample of non-responders. • Classic blunder: If you’ve got a large sample it must be better. !

Sources of error • Any statistic will differ from the corresponding parameter; the difference is

called “error.” • Some difference is due solely to the fact that we have a sample (as opposed to

a census). • Some is due to “coverage error” – mismatch between target population and

sample frame. !Non-sampling error

• Non-sampling error is generally agreed to be much larger than sampling error in most marketing research.

• Major component is “measurement error” e.g. questionnaire design. • Statistics can’t help us with that! !

Sampling methods • Probabilistic vs. non-probabilistic: Distinction of whether units are chosen at

random. • The logic of statistical inference depends on observations being made at

random i.e. object chosen to be observed. • Never use non-probabilistic sampling if you want to use statistical inference. • Probabilistic also helps eliminate “bias”, but “random” does not imply

“representative” i.e. because sampling is random, may not get a good representation of population.

!Why different methods?

• Gathering data costs money and other resources. • What is the value of information that could be obtained? • Trade-off cost against: 1. Possibility of inference to population. 2. Precision of estimates. • Costs have fixed and marginal components (easy to quantify), but what about

“value”? !Non-probabilistic

• Convenience sampling: Any method that is convenient. • Snowball: Ask one or more people to participate and ask them to recruit

additional participants. • Judgemental aka “purposive”: Researcher decides who/what to include and

tries to achieve that. !Probabilistic

• Key feature: Choice of whether to include a sampling unit is made at random e.g. flip a coin.

• Three main variations: 1. Simple Random Sampling (SRS) 2. Stratified Sampling 3. Cluster Sampling • Can mix and match e.g. stratified cluster sampling. !

Simple random sampling • You have a sampling frame where the sampling units are the same as the

sampling elements – you pick n of them at random. • By far the most commonly used method. • Gathering data costs time and money and there may be better ways to increase

precision or decrease cost. !Stratified sampling

• If the variable(s) of interest differs across groups, sample within each group separately.

• Optionally, have sample sizes of each group proportional to group size in population.

• Will result in smaller margin of error for same n as SRS, or same precision for smaller n (cheaper!).

• Aimed at improving accuracy while reducing costs. • Procedures for calculating statistics are different. !!!

Cluster sampling

• Pick a sample of groups at random, and then observe all the objects of interest in that group.

• Used when cost of assessing individuals is high but they’re grouped in some way that reduces that cost.

• If clusters are different, precision is lower than SRS. !Which to use?

• Trade-off precision vs. cost. • Stratified sampling is best if within-group variation of

variable(s) of interest is low and between-group variation is high.

• Cluster sampling is only justified if within-group variance is high and between-group variance is low.

• If variation in each of them is large, better to use a large sample. • Stratified sampling produces tighter confidence intervals and thus more

precise estimates which are a greater representation of overall general population.

• Possible to “post-stratify” a SRS. !Sample size

• We know that more is better, but how much is enough? Depends on: 1. What is being estimated? 2. Variability of the quantity in the population. 3. Desired precision. 4. Desired statistical power. • Bigger sample size is not necessarily better, but depend on how many

observations you need for a reliable estimate. !Precision

• Confidence interval: A range of numbers defined by its two end-points. • Confidence intervals are asymmetric – an estimate ± its margin of error: - “This survey has a margin of error of plus or minus 5%.” - “Margin of error” is half the size of the confidence interval. • Example: 0.05-0.15, 0.42-0.52, 0.80-0.90 are three difference confidence

intervals with the same margin of error. !Margin of error • m ≡ Zασa!• Z is the value of the Standard Normal distribution for the desired level of

significance. • Standard error = standard deviation divided by the square root of sample size. • Sample size and margin of error relationship is non-linear. • Small m (high precision) is more desirable but costs more – there is a tradeoff

between desired precision and actual cost of obtaining it. !!

Precision and sample size !! !!!!!!!Sample size for proportions !!!!!!!!!Sample size and precision

!!!!!!!!!!

CORRELATION !Meaning of correlation

• Colloquial usage: Two things are related somehow e.g. “binge drinking and violence are highly correlated”, “education and income are correlated.”

• More precise meanings: 1) If X occurs then Y occurs also, and vice versa. 2) If X changes then Y changes also, and vice versa. !

Co-variance • Co-variance: Average variance of x and y taken together.

!!

• If one is negative then the other is negative also, and vice versa.

• Add up the cases that support the hypothesis of positive relationship and subtract those that don’t.

• Symmetric: The correlation of x with y is the same as the correlation of y with x.

• The magnitude of cov(x,y) depends on: 1. The proportion of cases in each quadrant. 2. Whether if x is far from its mean, then y is also. 3. The magnitudes (e.g. range) of x and y. • Desirable to have a measure that doesn’t have the last property. !

Pearson’s r • Pearson’s r: One way to measure correlation (but not the only way). • Scaling of cov(x,y) by sx and sy gives a measure that lies in the interval

[-1,1] regardless of the magnitudes of x and y. !Correlation coefficients

• Correlation coefficients lie in the interval [-1,1]. • Note: Square brackets means “up to and including” while round brackets

means “up to but not including” in interval notation. • (0,1]: As one variable increases, so does the other. • [-1,0): As one variable increases, the other decreases. • The higher the absolute value, the “stronger” the relationship. • r < 0.3 ! “low”. • 0.3 < r < 0.6 ! “moderate”. • r > 0.6 ! “high”. • No solid statistical theory just social conventions that have arisen. !

Different measures 1. Pearson’s r for metric, symmetrically distributed variables. 2. Spearman’s p for ordinal variables with few ties. 3. Kendalls t for ordinal variables with many ties. • Both p and t are also used for skewed metric variables. !

Spearman’s p • Based on the concept of ranks: The rank of the value of a variable is 1 for

lowest value, 2 for the next lowest, and so on. • Example: Let x = (7, 9, 2) and y = (44, 15, 6). Then the ranks are xr = (2, 3, 1)

and yr = (3, 2, 1).

• Spearman’s p is cor(xr, yr), not cor(x,y).

• Makes no assumptions of the distribution of the variables. !Kendall’s t-b

• Measures the relationship between two ordinal variables. • t-b adjusts for “ties” i.e. when two or more cases have the same rank. • Preferred over Spearman’s p if there are a large proportion of ties (more likely

when variables have few values). !!More on ties

• Spearman’s p was designed for unique rankings i.e. n objects ranked 1, 2, ……. , n.

• A “tie” is when more than one case has the same rank e.g. third equal. • If a large proportion of the ranks are tied, Spearman’s p is biased. • If in doubt, use Kendall’s t. !

Graphical representation • Scatter plots: Graphs that show the values of two metric variables.

!!!!! !• Positive: As one variable increases, so does the other. • Negative: As one variable increases, the other decreases.

!!!!!!• Remember: Pearson’s r measures the strength

of the linear relationship between two metric variables. !!!

Inference • Correlation coefficients are descriptive statistics e.g. Pearson’s r is just the

slope of a straight line drawn through a scatterplot.

• It is possible to make inferences based on correlations. • Can test H0: “Coefficient is 0 in the population from which the sample was

drawn.” - Only valid for Pearson’s r if both variables are Normally Distributed. - No Normality assumption for p and t (non-parametric), all that is needed is for

them to be ordinal. !Correlation matrices

• Correlation matrices: Removing irrelevant information. • Show correlation of several pairs of variables. - Correlation between anything and itself is 1, thus could remove these diagonal

cells. - Correlation between A and B is the same as between B and A. • Can reduce the amount of numerical information by more than half. • Remember not to take values out that may change other values significantly. • Removing redundant information generates a much cleaner table that is easier

to interpret. • The p values tell you whether the coefficient is probably not zero in the

population – statistical significance vs. substantiative significance. !Causality

• Three conditions for causality: 1. Temporal precedence – the cause occurs before the effect. 2. Correlation – whenever the cause varies, the effect varies also. 3. Lack of alternative explanations. • Correlation is a necessary but not sufficient condition for causality. • Some people say causality can never be proven. !

Correlation is not causation • X might cause Y. • Y might cause X. • X might cause Y and Y might case X ! reciprocal relationship. • Could be a third variable affecting both of two unrelated variables. • Correlation coefficients give no information about which of these possibilities

is likely. • If in doubt it is useful to produce a plot. !!

REGRESSION !Common features

• Can be viewed as problems of prediction, or problems of explanation. • It is possible to have a prediction with no explanation, but the other way round

doesn’t make sense. • There are almost certainly other causal factors at work.

• One metric variable is presumably caused by one or more other metric variables. !

A mathematical model • As a particular variable (e.g. advertising spending) changes, what happened to

another variable? • It is reasonable to expect some variation in the response i.e. no perfectly

deterministic relationship. • So, what happens on average in response to changes in the explanatory

variable(s): !The jargon

• Y = dependent variable/response. • X = independent variable/explanatory/predictor. • The symbol ε is the lower-case Greek letter Epsilon, and denotes something

“small” in mathematical usage. A better model

• This model implies that if all the X vars are 0 then Y is 0 also – this may not always be true.

• Β0 is called in intercept: the mean of Y when all X variables are zero. !Calculating the betas

• It is desirable to have an estimation technique that: 1. Under-estimates as often as it over-estimates (is unbiased). 2. Keeps the size of errors the constant at all levels of the “fitted” values

(homoscedastic). 3. Keeps the size of errors unrelated to each other (no “runs”) or to the X

variables (independent). !Method of least squares

• Fits a straight line through a scatterplot with minimum “error.” • The line won’t intersect all points. • Distance between predicted and actual values of Y is called the residual. • Properties of residuals are the key to establishing the validity of the mode.

!!!!!!!! !!

!Inference

• If three conditions are met we can make inferences: 1. The errors are Normally Distributed. 2. Average size of errors (variance) must be unrelated to levels of any of the Y

variable or X variables (homoscedastic). 3. The errors should be uncorrelated with each other i.e. no “runs” of under- or

over-estimation (independent). • Need to check all assumptions for inferences to be valid. !

Checking assumptions • Homoscedastic: Roughly constant variance at every level of the fitted values. • Heteroscedastic: As residuals increase, variation becomes greater. • Looks like heteroscedasticity, but do “outliers” influence

our perception? Probably, but not enough to impact on decision.

• The most important assumption is that of Normality – check it first, using a formal test if plots are ambiguous.

• Residual plots for Homoscedasticity and Linearity are often ambiguous – when writing interpretation, if in doubt note possible violations. !

Significance of fit • ANOVA table compares the R2 of the estimated model with that of the “null

model” i.e. a model with no independent variables, just the intercept. • H0: The estimated model explains no more variance than the null model. • Sometimes models with very small Adjusted R2 are significant. !

Multiple regression • Example research question: Is gasoline consumption price-elastic? What does

the evidence say? !!!!!!!!!!!

FORECASTING !

Forecasting methods • Judgemental methods: Expert opinions, Delphi. • Statistical methods: Univariate (moving average, exponential smoothing,

seasonal decomposition); Multivariate (regression). !Motivation

• Classic marketing example: Sales over time. • Classic marketing need: Forecast sales in order to plan production. • Most marketing needs are for relatively short-term forecasts i.e. next month of

quarter, however, budgeting often needs 12 or 15 months ahead. Use by firms

• The simple methods are the most widely used (Opinion, Naïve, Analogies).

• Less accurate than extrapolation methods. !Accuracy of forecasts

• Comparing forecast with what actually happened to analyse accuracy. • We need to quantify: 1. How accurate a particular method is. 2. Whether it’s better or worse than another method. • Need some notation: We now say it’s t = 1, 2, ……., n to emphasise the

ordered nature of the data. • Yt-1 is the observation before Yt and Yt+1 is the observation after Yt. • ft is the forecast for time t. • et = Yt – ft is the forecast error. • Alternative measures of error are commonly used: - Mean Error (MSE): Useful for assessing statistical properties of methods, less

so for accuracy of forecasts. - Mean Absolute Deviation (MAD): In some cases the absolute deviation or % is

meaningful for decision making, in other cases not. - Mean Absolute Percentage Error (MAPE). !

Judgemental methods • Usually involve consulting “experts” i.e. people who should know. • Classic marketing method: To produce a sales forecast, ask a few salespeople

what they expect to happen in the next period. !The Delphi method

• As a bunch of experts for their prediction: - Produce some kind of average. - Send result, and forecasts of others, back to each expert. - Ask them whether they want to revise their estimate. - Rinse and repeat. • Can sometimes work well if it forces people to consider things they hadn’t

originally thought of.

!Mathematical methods

• Simple mathematical methods are sometimes used. • Naïve: Forecast for next period is same as current period. Tempted to think

about opportunity costs. • Moving average: Mean calculated over a short “window” of time, typically

three or so periods. How the average is increasing over time and extrapolate into the future.

• Exponential smoothing: Forecast is a weighted sum of the current and previous observations. !

Jargon • Time series: A variable that is ordered by time. • Cycles: Patterns that repeat at regular intervals. • Some methods use past values of the time series – univariate e.g. exponential

smoothing. • Others use additional variables – multivariate e.g. regression. !

Moving average • Simple moving average: The mean of the previous k observations. • Exponential smoothing: A weighted sum where the

weight is 0 < w < 1. !Regression for prediction

• Regression is developed for a situation with observed data and then summarising the relationship between variables with a line of best fit.

• Possible, but slightly dodgy. - Not really valid to use regression to forecast outside the range of available

data on the X vars. - A relationship can be (approximately) linear within one range of X, but non-

linear elsewhere. • Can include variables that influence the forecasted variable (use all available

information), but need forecasts of those “predictor” variables. • There is no one true way of forecasting that can fit for every situation. !

Seasonal data • Seasonal decomposition: Splitting a seasonal time series into components. 1. Trend – on average going up or down? 2. Seasonal effect. 3. (Optionally) Cyclic effect – longer-term effects. 4. Random variation. • “Seasonal” means any cycle with a regular period. • “Cyclic” means cycles that don’t have regular periods. !

Periodicity

• The unit of measurement of time series business data is typically weekly, monthly, quarterly or yearly.

• The time for a cycle to return to its starting point is called its period or periodicity.

• The “business cycle” (in NZ) would have period 4 in yearly data and 48 in monthly data. !

Seasonal decomposition • Simplest model: Yt = Tt + St + ε i.e. additive effects of trend and seasonality. • First, remove the trend; what’s left must be seasonal + random. • Could also de-trend by using a moving average of span equal to periodicity. !

Estimating season effect • Simplest method: Average all the de-trended e.g. Januaries, all the winters etc. • Then centre it by subtracting the seasonal effects from their mean, result is St. • “Seasonally adjusted” series is Yt – St. • Forecast is therefore forecast trend + seasonal effect (ft+1 = Tt+1 + St+1). !!!

SEGMENTATION !STP

• STP: Segmentation, Targeting and Positioning. • Basic ideas: 1. People are different. 2. Businesses can’t create unique products/services to cater to each customer

individually. !Overview of STP

1. Split the overall market into groups of customers who are similar to each other and different from those in other groups.

2. Pick one or more of those groups and try to serve their wants and needs. 3. Establish an image in their minds of us as being different from the

competition. !Segmentation – why?

1. People are unique. 2. But some are similar to others (in some ways). 3. We want to group people according to some variables of interest. • This allows us to serve markets efficiently. • Ideally we’d like to adjust our offerings to serve each customer individually,

but this is usually too expensive. 4. Can serve more than one segment with differing marketing mixes. !

Segmentation – what?

• Any two people may be the same in one respect and different in others. • Really quite different people can have identical wants and needs. • What are the variables that matter? - Depends on the purpose and situation. - Geographic, demographic, psychographic, behavioural, needs based. - One basis may be useful in one context but not in another. !

Segmentation – how? • Many different quantitative techniques. • Most important distinction: Theory driven vs. data driven. • Cluster analysis (clustering) most widely used data-driven method. - Explanatory technique: No hypothesis testing. - “Cluster analysis” is really a group of methods. !

Effective segmentation 1. Definable: Measurable (variables that we can measure and get data on) and

differentiated (genuine differences between groups). 2. Reachable: Are there cost, legal or ethical constraints? 3. Actionable: Can we develop marketing mixes to serve them? !

Effective targeting • Targeted segment(s) should be profitable – size, ability and willingness to pay. • Many businesses have one (or more) primary target segments and one or more

secondary. • If more than one segment is targeted, the marketing mix for each will be

different. • Basic idea: Spend money serving people who are likely to respond. • Talk to them through channels that they attend to, and that people we don’t

want to talk that don’t attend to. • Any other action that avoids spending money that is wasted – reaching people

we don’t want to serve. !“Actionable”

• Do the segments have differing response to managerially controllable actions (marketing mix)?

• If not, what’s the point of segmenting the market? • Idiocy of demographic segmentation, primary of behavioural. • Idiocy: Only rare products genuinely appeal to only certain demographic

segments. • Demographic variables are usually proxy variables for what we’re actually

interested in (wants and needs). • Demographic often used only because it’s easy/cheap to get data (to estimate

segment sizes). • However, rarely is it sensible to base segmentation on demographics i.e. some

products only appeal to men or only appeal to women. !

Grouping • We need a way to group people together based on several variables

simultaneously. • The people in each group should be similar (low within-group variance). • Each group should be quite different from the others (high between-group

variance). !Cluster analysis

• Two types of cluster analysis methods: Hierarchical and non-hierarchical. • Core concepts relate to distance: 1) Between objects. 2) Between groups of

objects (clusters, segments). !Hierarchical clustering

• Two types: Splitting and merging. • Splitting methods start with all object in one group then splits them into k

groups. Each group is then split into more k groups. • Merging methods start with each object in a group by itself. Each object is

then merged with the object “nearest” to it. !Distance between points

• Euclidean. • Squared Euclidean. • City Clock (travelling from A to B at right angles). • Squared Euclidean or Euclidean most commonly used – shortest distance

between two points. • Easy to see in 2D, generalizable to any number of dimensions. !

Distance between clusters • Options and synonyms: - Nearest neighbour (single linkage) - Furthest neighbour (complete linkage) - Average distance (average linkage) - Centroid distance (centroid linkage) - Ward’s method (uses within-group variance) • Each are designed for when the objects are dispersed in a certain way. • Ward’s method is generally regarded as best. !

Ward’s method • Uses within-group variance (ESS). • Shown to be most reliable of hierarchical methods. • Starts with all in a “group” by itself. • Joins the two groups with minimum ESS. • Keep going until all observations are in one big group. !

Dendrograms • Dendrograms show the distance at which each object

(or group of objects) merges with others. - Centroids of groups of observations are represented

by a horizontal line. - The horizontal axis is the distance at which groups

are merged. • Look for “large” increases in distance to suggest k

(number of clusters). • “Large” is relative to other distances. !

Agglomeration schedule • Shows each step of the merging process. • At first stage, objects 3 and 4 merged into a cluster which is now labelled “3”. • This cluster does not merge with another until the third stage. • At the third stage the first cluster being merged (3) first appeared at the first

stage. !How many segments?

• Agglomeration methods start with each customer in a “segment of size one” then start merging.

• Somewhere between everyone in a segment by themselves, and everyone in the same segment, we have to stop merging.

• Dendrograms and agglomeration schedules help us to make that decision (when to stop).

CLUSTER ANALYSIS !Interpreting SPSS output

• Use dendrogram or agglomeration schedule (or both) to decide how many clusters to “retain.”

• They give the same information but sometimes one is easier to interpret than the other; choose on a case-by-case basis.

• For practical marketing purposes, no more than 7-9 segments are usually considered.

• Dendrograms show the distance at which each object (or group of objects) merges with others. !

The big picture • Dendrograms can be large (scales with n). • There are almost always two clearly separated clusters. • Need to look carefully to see if there’re more. • Mark the relatively large distances. • Don’t merge the “descendants” of that line with any other objects that you

could get to by traversing across and down.

• Count the number of resulting groups. !Agglomeration schedule

• The only relevant column is “Coefficients.” • Go to the bottom and look for large changes; this tells you

where to stop merging. • The last row is the distance at which the last two clusters

joined i.e. how far apart they are. !Deciding on k

• Interpret either or both of: 1) Dendrogram. 2) Agglomeration schedule.

• Look for “large” distances as a clue to know when to stop merging/splitting.

• “Large” is relative to other values. • Look for large jumps, abrupt change in angle or “elbow”. !

Non-hierarchical clustering • Weakness of hierarchical methods: Once an object is “n” in a cluster it can

never get out. • Non-hierarchical methods address this; most popular is “k-means.” • Has been found to be best overall at allocating objects to a pre-defined number

of groups (k). • We estimate k from a hierarchical method. !

k-means details • k-means procedure is as follows: 1. Pick k points at random as “seeds.” 2. Allocate each object to closest seed. 3. Calculate centroids and overall ESS. - Reallocate observations to clusters to reduce ESS. - Keep reallocating until change in ESS is zero, or very small. • Final cluster centres need to be interpreted: They are the means of the base

variables for the objects in that cluster. !Refining an initial solution

• Including irrelevant base variables can cause problems. • SPSS will print a handy ANOVA so you can eliminate variables that don’t

differ between clusters. !Profiling clusters

• Base variables: Variables used to cluster people. • Useful to describe clusters using additional descriptor variables. • Usually need demographic descriptions of target segments for media-buying

purposes. !

Validity • Cluster analysis is not an inferential technique i.e. no p values. • Split-half: Use half the data to estimate number of groups, then use other half

and compare results. • Can test robustness by resampling: Estimate k with a random sample of x% of

data, rinse and repeat. • Non-hierarchical methods considered superior – objects can shift between

clusters and this compensates for bad initial guess, outliers etc. !Steps in cluster analysis

1. Estimate number of clusters (k) using hierarchical method. 2. Refine allocation of observations to clusters using k-means. 3. Profile clusters on base and descriptor variables that are significantly different

across clusters. 4. Apply face validity test: Are profiles useful for marketing purposes? !!

ETHICS !What is ethics?

• A set of principles of right conduct. • A theory or a system of moral values. • The rules or standards governing the conduct of a person or the members of a

profession. • Code of behaviour, especially of a particular group, profession, or individual. • The process of evaluating and addressing whether a particular action is right

or wrong, good or bad. !!!Ethical theory

• Deontology: Focus on means or behaviours; concerned with rules and regulation. Doesn’t necessarily determine what is right or wrong.

• Consequentialism: Focus on the consequences as opposed to the behaviours; we can’t predict correct consequences – utilitarianism.

• Business, markets and marketing tends to use utilitarian rules. • Utilitarianism may look unethical if they make the wrong prediction. !

Ethics in marketing research • Basic question to ask when research thinks about ethics: “Will any physical or

physiological harm come to anyone as a result of my research?” • Basically to carry out with project with respect and concern for the dignity and

welfare of the people who participate. • There is more ethical responsibility for the researcher. • Responsibilities and rights of the 4 stakeholders. !

Research supplier ethics • Low-ball pricing: Quoting unrealistically low price then manipulating to

increase price over time. • Allowing subjectivity into the research process: Using biased samples,

misusing statistics, ignoring relevant data, have to make methodology transparent.

• Abusing respondents’ rights. • Selling unnecessary research. !

Client ethics • Requesting bids when a supplier has been predetermined. • Requesting bids to obtain free advice and methodology. • Making false promises: Firms often agree to use particular supplier, but use

other ideas from elsewhere. • Issuing unauthorised RFPs (Request For Proposal). !

Public/respondents’ rights 1. The right to choose: Everyone has the right to choose to participate. 2. The right to safety: Psychological harm e.g. deception, putting people under

undue stress. 3. The right to be informed: Knowing what is involved, how long it is going to

take, what is going to be done with the data. 4. The right to privacy: Income, age etc. alone are quite sensitive questions. !

The right to choose • The right to self-determination: Means that prospective participants have the

right to decide voluntarily whether to participate in the study, without risking any penalty or prejudicial treatment.

• The right to full disclosure: The participants should be fully informed of the dangers involved.

• Subjects may be unaware of their right to refuse due to poor education. • Subjects may lack sufficient knowledge to make an informed choice i.e. they

are not given sufficient information to start with. • Subjects may not be given the opportunity to choose; unintentional act of

missing information or disguising the source of information. • Some research techniques impinge on the right to choose. !

Informed consent • Means that participants have adequate information regarding the research, are

capable of comprehending the information, and have the power of free choice, enabling them to consent to or decline participation voluntarily. !

The right to safety • The Principle of Beneficence: a) Freedom from harm b) Freedom from exploitation

c) The risk/benefit ratio. • The Principle of Justice a) The right to fair treatment b) The right to privacy • Protection of anonymity: I you have said you will protect anonymity you must

e.g. original questionnaires left lying around may be read by anyone, forwarded to clients etc.

• Respondent stress. • Deception: Might be violation of subject’s right to make an informed choice;

common in marketing and social science research. !Right to be informed

• Debriefing: Best way of addressing situations and bringing relationships back into balance.

• Style of debriefing is really important in giving a sense of power back to the respondent.

• Research findings: Gives the respondents the data you have gathered. !Vulnerable subjects

1. Children – more vulnerable, have fewer legal rights, and often do not understand the meaning of “informed consent”.

2. Mentally or emotionally disabled people. 3. Severely ill or physically disabled people. 4. The terminally ill. 5. Institutionalised people. 6. Pregnant women. !

Example of unethical practices • Problem definition: - Using surveys as a guide for selling or fundraising. - Following personal agendas of the researcher or client. - Conducting unnecessary research. • Conducting research: - Soliciting proposals to gain research expertise without paying. - Using findings and models from specific clients or projects for other project. - Inaccurate reporting. • Research design: - Choosing methods suited to the researcher’s rather than the client’s needs. - Using secondary data that is not applicable. - Disguising the purpose of the research. - Misleading or biasing questions. - Using measurement scales of questionable reliability and validity. - Not maintaining anonymity or privacy of respondents. - Embarrassing or putting stress on respondents. - Designing overly long questionnaires. - Using convenient sampling procedures without disclosure.

• Fieldwork: - Increasing the discomfort level of respondents. - Accepting poor quality fieldwork procedures. • Data preparation and analysis: - Identifying and discarding unsatisfactory respondents. - Using statistical techniques when the underlying assumptions are violated. - Interpreting the results and making incorrect conclusions and

recommendations. • Report preparation and presentation: - Using incomplete, biased and inaccurate reporting. !

Ethical theory perspective - Golden rule: Never behave in a way you would not accept e.g. “I shouldn’t

offer to write the brief unless I would accept someone doing it for me.” !!INTERVIEWS AND FOCUS GROUPS !In-depth interviews

• Interview: A conversation with a purpose other than the satisfaction of a conversation itself.

• In-depth interview: An unstructured personal interview which uses extensive probing to get a single respondent to talk freely and to express detailed beliefs and feelings on a topic.”

• Unstructured, direct, personal interview. • Single respondent. • Uncover underlying motivations, beliefs, attitudes and feelings on a topic. • 30 minutes – 1 hour. !

Advantages of in-depth interviews • Can attribute the responses directly to the respondent. • Free exchange of information, no social pressure. • Uniquely applicable for sensitive or personal topics. • Opportunity to build close rapport and trust. • Greater control over respondent selection. • Depth and comprehensiveness of information. !

Disadvantages of in-depth interviews • Skilled interviewers required so training required. • Lack of structure tends to lead to lack of generalizability, reliability. • Potential biases; interviewer and respondent may not gel or respondent’s

characteristics may not be well understood by interviewer. • Data obtained is difficult to analyse and interpret. • Costly and time consuming. • Not an observable technique. !

Applications of in-depth interviews • Where detailed probing is required. • Confidential, sensitive, or embarrassing topics. • Situations where strong social norms exist. • Detailed understanding of complicated behaviour. • Interviews with professional or business people. • Interviews with competitors. • Situations where the product consumption experience is sensory in nature. !

Interviewers’ roles • Selecting respondents who fit sampling requirements. • Translate contacts to interviews. • Conducting interview – probing and securing valid/reliable answers. • Recording of responses. !

Nuances of probing !!!!!!!!!!!!!!!!Interviewer effects

• Own attitudes to the topic: Social desirability (respondent feels they have to answer in a way that looks good to the researcher) – alcohol, conversation, politics.

• Personality characteristics. • Demographics: Sex, ethnicity, age, identification with respondent – research

suggests that interviews between some ethnicities produces more efficient responses. !

Laddering • When making purchasing decisions, customers evaluate products from the

perspective of the benefits they provide and values that they hold. • Laddering: Uncovering motives of consumption at the level of values.

• Products are a bundle of attributes that are important because they provide a specific consequence that relates back to values. !

Focus groups • An interview conducted by a trained moderator in a non-structured and natural

manner with a small group of respondents. • Most widely used of all techniques in market research because it is relatively

quick, cheap and easy to run. • Should only be used for exploratory research. • Key advantage is that there is interplay between people talking about different

aspects. • Should be homogenous: Specifically choosing a group of people from whom

you will get insight. !Advantages of focus groups

• Synergy: Wider range of information. • Snowballing: Chain reactions. • Stimulation: Respondents want to join

in. • Security: Comfortable surrounds. • Spontaneous: Creative. • Specialisation: Moderators. • Scrutiny. • Speed. • Interplay of various people discussing one topic. • One person may trigger a thought or idea that leads to further insight. • People will have demographic and socioeconomic similarities so comfortable

situation. • More likely to respond with true emotional things in spontaneous manner. • Can afford to use highly trained managers. !!!

Disadvantages of focus groups 1. Misuse: Not representative of general population. 2. Misjudge: Susceptible to interviewer’s bias, general feeling of the group etc. 3. Moderation: Unstructured so analysing stage of process can be difficult. 4. Messy: People may feel inhibited in group situations. 5. Misrepresentation: Social pressures can force people to say things they don’t

actually agree with. Not appropriate for some topics. !Application of focus groups

• Understanding consumers’ perceptions, preferences and behaviour concerning a product category.

• Obtaining impressions of new product concepts. • Generating new ideas about older products.

• Developing creative concepts and copy material for advertisements. • Securing price (and value) impressions. • Obtaining preliminary consumer reaction to specific marketing programs. !

Typical focus group structure • Behaviour or knowledge: “What was the last … you bought? Where did you

buy it? What brands of … are you aware of? – Gives frame of reference and everyone involved.

• Start with something broad that people will be willing to talk about. • Evaluations: Likes and dislikes, satisfaction. • Social norms. • Developments: Hypothetical scenarios. !

Moderator roles • Specify the objectives. • State the questions to be answered from the focus group. • Write a screening questionnaire. • Develop a Moderator’s Outline. • Conduct the interview. • Review tapes and analyse data. • Critical to the success of a focus group. • Takes distinct personality characteristics and a lot of experience in running

them. !Planning and conducting

• The success group session depends on the moderator and his/her communication, interpersonal, probing, observation and interpretative skills.

• Key characteristics: Kindness with firmness, permissiveness (let the group go its own way), involvement (make the respondents positive and enthusiastic), show incomplete understanding to probe, encouragement, flexibility, sensitivity.

• Need to be detached but also quite empathetic. !Variations in focus groups

• Two-way focus group: This allows one target group to listen to and learn from a related group.

• Dual-moderator group: A focus group conducted by two moderators. • Dueling-moderator group: There are two moderators, but they deliberately

take opposite positions on the issues to be discussed. !New approaches to focus groups

• Online focus groups advantages: Removes geographical constraints, recontacting respondents is possible, better for hard-to-get respondents, coordination between moderators and client for probing is possible, people less inhibited.

• Disadvantages: Need to use computer, verification of respondents difficult, little control over respondent’s environment, stimuli limited to audio and visual. !!

PROJECTIVE TECHNIQUES Levels of consciousness

• Our true attitudes and emotions may not even be conscious to ourselves.

• Useful when people have difficulty expressing their personal opinions. !

Projective techniques • Based loosely on the psychoanalytic concept of projection, the assumption

being that respondents project unconscious aspects of their personalities onto the test items and reveal them in their responses.

• An unstructured, indirect form of questioning that encourages respondents to project their underlying motivations, beliefs, attitudes or feelings regarding the issues of concern.

• Projection: Attributing negative qualities to other people we see in ourselves. !Psychoanalysis

• Dichter: Sex and other deep-seated motives as a basis for consumption; underlying motives for consumption that could be drawn out of people.

• Travel: ‘Return to the womb.’ • Sports cars: ‘Substitute for sexual gratification.’ • Garters: ‘Castration complex.’ • Making cakes: ‘Substitute for giving birth.’ !

Association techniques • Association techniques: An individual is presented with a stimulus and asked

to respond with the first thing that comes to mind. • Word association: Respondent is presented with a list of words and asked to

respond to each with the first word that comes to mind. - Responses are calculated by: The frequency of a given response, the amount

of time that lapses before a response is given, the number of respondents who do not respond at all to a test word within a reasonable time period.

- Pluses and problems: Fun and insightful associations BUT unpredictable and useless responses.

- Uses: Testing brand names, qualities of competing goods, before and after advertising to test new learning, mapping memory networks (link to product attributes and use for positioning decisions).

• Picture associations: Asked to match pictures. - Given a brand and asked to match pictures of users, competitors, substitutes,

complementary products (brand portfolios).

- Given a picture of consumer and asked to match products. - Pictures need to be ambiguous enough that people can go off on their own

tangent, but still directed so the topic of matter is defined. - Uses: Brand profiling. !

Completion techniques • A projective technique in which respondents are asked to complete a sentence

or story. • Sentence completion: Respondents are given incomplete sentences and asked

to complete them; generally they are asked to use the first word or phrase that comes to mind.

- Two types: 1) Usage situations, motives. 2) Knowledge, understanding. • Story completion: Respondents are given part of a story – enough to direct

attention to a particular topic but not to hint at the ending. !Construction techniques

1. Picture response 2. Cartoon tests • Thematic Apperception Tests (TATs): Projective personality test consisting of

31 ambiguous pictures that depict a variety of social and interpersonal situations.

- The subject is asked to tell a story about each picture to the examiner. - Themes (thematic) are elicited on the basis of the perceptual-interpretive

(apperception) use of the pictures. - Market research often uses a variant on the original TAT framework for

consumption motives. • Bubble tests: 24 cartoon drawings with a defence response (pressurised

questioning) and an aggression direction (inwards, outwards, or repressed). - Cartoon or sketch of scene. - Bubble with words of one character and blank bubble for completion. - Variant has words for both characters but bubbles for thoughts. !

Expressive techniques • Role-playing: Respondents asked to play the role or assume the behaviours of

someone else. • Third person technique: Respondent is asked to relate the beliefs/attitudes/

behaviours of a third person. - Do you think your friend would stop at Postie Plus? - Who would drink Drambuie? - Depersonalises i.e. allows for individual attitudes to be projected onto a

neutral situation. !Psychodrama

• An important role-play activity to act out a situation. • Applications in training, education, healing, spiritual life, business, and

performing arts.

• Common business applications are group efforts, team building etc. • Market research: Usage patterns, new uses, and children consumer behaviour. • Can be observed, get people playing off each other, get opportunities for

straight use of products, can move roles into particular areas. • Stimulating emotional response to things so people are less inhibited. !

Advantages of projective techniques • Elicit responses that respondents would be unwilling or unable to give if they

knew the purpose of the study. • Useful when issues are personal, sensitive or subject to strong social norms. • When underlying motivations and beliefs are operating at the subconscious

level. !Disadvantages of projective techniques

• Highly trained interviewers are needed. • Expensive. • Serious risk of interviewer bias. • Difficulty in interpretation – may be as much a projection of the researcher as

the respondent. !Usefulness of projective techniques

• Used for exploratory research to gain initial insights and understanding. • Used to test brand names and attitudes. • Projective techniques should be used because the required information cannot

be accurately obtained by direct methods. • Given their complexity, projective techniques should not be used naively.

!!!!!!!!!

OBSERVATION AND ETHNOGRAPHY !Observation

• The recording of behavioural patterns of people, objects, and events in a systematic manner to obtain information about the phenomenon of interest.

• The needed information must be either observable or inferable.

• The behaviour should be repetitive, frequent, or in some manner predictable. • The behaviour must be relatively short in duration. • Observation is used widely as a complementary technique to other data

analysis methods. !Types of observation

• Structured observation: The researcher specifies in detail what is to be observed and how the measurements are to be recorded.

• Unstructured observation: The observer monitors all aspects of the phenomenon that seem relevant to the problem at hand.

• Disguised observation: The respondents are unaware that they are being observed - most would opt for this because respondents are more likely to act naturally.

• Undisguised observation: The respondents are aware that they are under observation.

• Natural observation: Involves observing behaviour as it takes place in the environment e.g. one could observe the behaviour of respondents eating fast food at Burger King – if you always depend on this, you may spend a huge amount of time waiting.

• Contrived observation: Respondents’ behaviour is observed in an artificial environment, such as a test kitchen.

!!!! !!Personal observation

• A researcher observes actual behaviour as it occurs. • The observed does not attempt to manipulate the phenomenon being observed

but merely records what takes place. • Example: A researcher might record traffic counts and observe traffic flows in

a department store. !Mechanical observation

• Do not require respondents’ direct participation: Turnstiles that record the number of people entering or leaving the building, on-site cameras, optical scanners in supermarkets.

• Do require respondent involvement: Eye-tracking monitors, pupilometers, psychogalvanometers, voice pitch analysers, devices measuring response latency.

• Mechanical observation is much more accurate than personal observation and may measure things that humans cannot.

• All of these things are based on our physiological responses, but this doesn’t necessarily mean we can predict anything from this.

!Audit

• The researcher collects data by examining physical records or performing inventory analysis.

• Examples: Basic stock take can be a good indicator of sales at local and national levels; looking at wear and tear of library books. !

Content analysis • The objective, systematic, and quantitative description of the manifest content

of a communication. !Trace analysis

• Data collection is based on physical traces, or evidence, of past behaviour. • Examples: The number of different fingerprints on a page was used to gauge

the readership of various advertisements in a magazine; the position of the radio dials in cars brought in for service was used to estimate share of listening audience of various radio stations. !

Comparative evaluation of observation methods !!!!!!!!!!Advantages of observation

• Permits measurement of actual behaviour. • Potential bias cause by the interviewer and the interviewing process is

eliminated and reduced. • Certain types of data can be collected only by observation. • May be cheaper and faster than survey methods. !

Disadvantages of observation • The reasons for the observed behaviour may not be determined. • Observational data is often time-consuming and expensive. • May be unethical.

Observer bias • “What we see depends mainly on what we look for.” • Any distortion in measurement that occurs from observer’s responses or

actions.

• Without communicating, no way of knowing if interpretation of observation is correct. !

Ethnography • Comes from anthropology. • Purpose is “deep understanding.” • Uses observation and other qualitative approaches – often ‘participative.’ • Limited in commercial market research but increasingly being used in

management research – not only observing but also doing interviews etc. • Common in academic research on consumer behaviour. • Generally much longer term and often involves a combination of different

methods. • Particularly good in looking at sub-cultures, ethnic groups, age etc. • Aims of ethnography: Seeing through the eyes of the people being studied,

description, contextualism, process, flexible research design, avoiding early use of theories or concepts. !

Ethnographic methods in consumer research • Participant observation. • Non-participant observations. • Interviews. • Information interviews and casual conversations. • Informant diaries. !

Ethnography in market research • Researcher essentially becomes a naïve visitor in that world by engaging

respondents during realistic product-usage situation in the course of daily life. • Ethnography combines observation and interviewing to help build a rich

picture of consumers. • Can be a discovery and evaluation tool. !

Tips for market ethnography • Look for the ordinary, not the extraordinary. • Nothing people do is “natural.” • Be the master of the obvious. • Don’t fear the details. • Marry observation with tradition qualitative methods. !!

ANALYSING QUALITATIVE DATA AND PRESENTING RESEARCH DETAILS !

• Firstly, data analysis is our attempt to summarise collected data in a dependable and accurate manner.

• Secondly, data interpretation is our attempt to find meaning in the data, or making sense of the data.

!!!!!!!!!!!!!!!!Suggested steps to analysing qualitative research data

1. Become familiar with the data. 2. Data reduction: Selecting, focusing, simplifying, categorising and coding

pieces of data and grouping them into themes. • Codes are tags or labels for assigning units of meaning, they are used to

retrieve and organise the chunks of information so you can quickly find, pull out, and cluster the segments relating to a particular topic.

3. Data display: Creating organised, compressed representations of information. 4. Conclusion drawing and verification: Deciding what things mean and testing

them for plausibility/validity. !The process of coding

1. Create a provisional “start list.” 2. Create code definitions. 3. Revise coding scheme – filling in, extension, bridging, surfacing. • Structure is key: Codes should relate to one another, they should be part of a

governing structure. !When analysing and interpreting qualitative data

• Challenge yourself to explore every possible angle and try to find patterns and seek out new understandings among the data.

• Remember it is your ability: Think, imagine, create, intuit, analyse. !Uses of qualitative software

• Data reduction: Retrieving text that has pre-determined significance. • Text exploration: Helping researcher recognise underlying themes of the text. !!

Advantages of CAQDAS

• Makes the sheer volume of data more manageable. • Helps to selectively retrieve information – can summarise results in structures

lists and tables. • Helps to evaluate the weight of supporting vs. non-supporting data – can

report results in comparative ways. • Helps to provide linkages to other types of data and perspectives – can

integrate qualitative and quantitative data. !Limitations of text retrieval programs

• Lack of natural language processing capabilities (ambiguous concepts, broader context is lost).

• Insensitivity to negation, irony, tone. • Inability of researcher to provide a completely exhaustive listing of key words. • Inability of software to resolve references back and forth to words elsewhere

in the text. • Can result in “word crunching” – transforming rich meanings into

meaningless numbers. !“Good qualitative analysis is able to document its claim to reflect some of the truth of a phenomenon by reference to systematically gathered data.”

IN CONTRAST “Poor qualitative analysis is anecdotal, unreflective, descriptive without being focused on a coherent line of inquiry.” !Importance of the research report

• Tangible output: Historical record of work. • Guides management decisions. • Most managers (client companies) only involved in report + presentation. • Repeat business: Closes service encounter. !

Report format

!!Report writing

• Readers: A report should be written for a specific reader or readers – the marketing managers would use the results.

• Easy to follow: The report should be easy to follow and should be structured logically and written clearly.

• Presentable and professional appearance: The look of a report is important. • Objective: Objectivity is a virtue that should guide report writing.

• Reinforce text with tables and graphs: It is important to reinforce key information in the text with tables, graphs, pictures, maps, and other visual devices.

• Terse: A report should be terse and concise, yet brevity should not be achieve at the expense of completeness.

!1. The consumer first becomes aware of the need. 2. Then the consumer simultaneously searches for

information from several sources; retailers, advertising, word of mouth and independent publications.

3. After a criterion is developed for evaluating the available brands in the marketplace

4. Based on this evaluation, the most preferred brand is selected. !

Graphs • Graphic aids should be employed wherever practical. • Types of graphs: Geographic and other maps, round or pie charts

(percentages), line charts (trends and changes over time), histograms (absolute and relative magnitudes, differences and change). !

Oral presentation • The key to an effective presentation is preparation. • A written script or detailed outline should be prepared following the format of

the written report. • The presentation must be geared to the audience. • The presentation should be rehearsed several times before it is made to the

management. • Visual aids, such as tables and graphs, should be displayed with a variety of

media. • It is important to maintain eye contact and interact with the audience during

the presentation. • “Tell ‘em principle: Effective for structuring presentation. • “Kiss ‘em principle: Keep it simple and straight forward. • Body language should be employed. • The speaker should vary the volume, pitch, voice quality, articulation, and rate

while speaking. • The presentation should terminate with a strong closing. !

Research follow-up • Assisting the client: Understand technical matters, discuss further research. • Evaluation of the research project: Could this project have been conducted

more effectively and efficiently? !

!INTERNATIONAL MARKETING RESEARCH !Marketing research – the global context

• Growth in industrialised markets is declining, thus there is a necessity in terms of international research.

• Need to be careful in terms of measurements being comparable from country to country.

• What types of research are appropriate given the level of education, wealth etc. in the particular country? !

Marketing environment • The variety and assortment of products available. • Pricing policies. • Government and control of media. • The public’s attitude toward advertising. • The efficiency of the distribution system. • The level of marketing effort undertaken. • The unsatisfied needs and behaviour of consumers. • Demand in development countries typically exceeds supply. !

Government environment • Public policy, regulatory agencies, government incentives and penalties, and

investment in government enterprises. • Tactical level – tax structures, tariffs, and product safety rules and regulations

and often imposed special rules and regulations on foreign multinationals and their marketing practices.

• Many Governments, particularly in developing countries, do not encourage foreign competition. !

Legal environment • Common law, code law, foreign law, international law, transaction law,

antitrust, bribery, taxes. • Product, pricing, promotion distribution. • In some countries, the laws around marketing are few and loosely enforced. !

Economic, structural, informational and technological environments • Economic size (GDP), level, source, and distribution of income, growth

trends, and sectoral trends. • Structural factors relate to transportation, communication, utilities, and

infrastructure. • Informational and technological environment include information and

communication systems, computerisation, use of electronic equipment, energy, production technology, science, and invention. !

Sociocultural environment

• Values, literacy, language, religion, communication patterns and family and social institutions. !

International qualitative research • Qualitative techniques are especially helpful in probing the contextual

embedding of attitudes and behaviour, providing deep understanding of situational and contextual factors, and providing inputs into interpreting observed differences between countries and cultures.

• There is no strict structure imposed so more flexibility and this makes it easier to make comparisons between countries. !

Focus groups • Trained moderators in technique and language/culture. • Cultural influences. !

International survey research • The method(s) which is/are best capable of meeting the data collection needs

in a given marketing research project should be selected. • Telephone interviewing and CATI: - In the US and Canada, telephone interviewing is the dominant mode of

questionnaire administration, and the same situation exists in some European countries.

- In many other European countries, telephone interviewing is not the most popular method.

- In developing countries, only a few households have telephones. • Due to high cost, the use of in-home personal interviews has declined in the

US and Canada, but this is the dominant mode of collecting survey data in many parts of Europe and the developing world.

• Mail intercepts constitute about 20% of the interviews in the US; they are not popular in Europe or developing countries.

• Because of low cost, mail interviews continue to be used in most developed countries where literacy is high and the postal system is well developed. !

A comparative evaluation of survey methods for international marketing research !!!!!!!!

Measurement and scaling • Construct

equivalence: Concerns

whether the marketing constructs have

the same meaning and significance in

different countries. •Conceptual

equivalence: Deals with the interpretation of brands, products, consumer behaviour, and marketing effort.

• Functional equivalence: Examines whether a given concept or behaviour serves the same role or function in different countries e.g. bicycles.

• Category equivalence: Refers to the category in which stimuli like products, brands, and behaviours are group e.g. principal shopper.

• Measurement equivalence: Concerns the comparability of responses to particular (sets of) items.

• Configural explanation: Concerns the relationship of measured items to the latent constructs; the patterns of factor loadings should be the same.

• Metric equivalence: Refers to the unit of measurement; the factor loadings should be the same.

• Scalar equivalence: Refers to both the unit of measurement and the constant in the equation between the construct and the items measuring the construct (the intercept).

• Operational equivalence: Concerns how theoretical constructs are operationalized to make measurements e.g. leisure.

• Linguistic equivalence: Refers to both the spoken and the written language forms used in scales, questionnaires, and interviewing. The scales and other verbal stimuli should be translated so that they are readily understood by respondents in different countries and have equivalent meaning. !

Questionnaire translation • Back translation: The questionnaire is translated from the base language. This

version is then retranslated back into the original language. • Parallel translation: A committee of translators, each of who is fluent in at

least two of the languages in which the questionnaire will be administered, discusses alternative versions of the questionnaire and makes modifications until consensus is reached. !

Response bias • Response sets refer to “tendencies to respond systematically to questionnaire

items on some basis other than what the items were specifically designed to measure.” !

!!!!!!!!!!EXPERIMENTS !Types of research

• Recall the types of research we’ve covered: 1. Exploratory 2. Descriptive 3. Causal • Experiments are about examining causation. • Marketing managers are most interest in the techniques in gathering and

analysing data in causal research – what is causing things to happen. !Why experiments?

• Many factors may influence a process or outcome; hardly anything only has one cause.

• What causes the outcome vs. what is just coincidence – manipulate the factors to see if it changes the effect.

• Experiment: Interference with “natural” processes (“manipulation”). • Observation vs. experiment: Observation is just looking at things, not

interacting like you do in experiments. !Examples in marketing

• Taste tests (New Coke!) • Advertising tests (recall, liking) • Packaging tents • Pricing experiments (e.g. TOU) • Audience reaction tests (movie endings) • Test marketing (geographically isolated areas) • Taste tests and advertising tests are the more common experiments in market

research. !“New Coke”

• A result of two years of market research, taste tests and new product development.

• Biggest business mistake/failure ever.

• Scenario: Pepsi was taking market share from Coke; Coke felt under threat, Pepsi was sweeter.

• Premise: “Scientific” taste-tests showed that consumers preferred Pepsi. • Conclusion: Coke’s taste needed to change, so formulated “New Coke.” • From here, “scientific” taste-tests showed that consumers preferred New

Coke. • Launched: Huge consumer backlash at the introduction of New Coke, lost

millions, so reversed launch (returned to original formula) three months later. !What went wrong?

• The researchers assumed that purchase decision was based solely on what the product tastes like.

• Essentially, the researchers had the right answer to the wrong question. • The brand is not the product: People don’t buy Coke for its taste alone; there

are particular associations that come with brand names. • Don’t mess with people’s daily, lifelong habits (or childhood memories). • Coca-Cola is a cultural icon (lovemark?). !

More specifically • Assumption: Decision to buy is caused primarily by taste. • Experiment: Manipulate taste (causal factor) and observe stated preference

(outcome). • Three levels of causal factor: Old Coke, New Coke and Pepsi. !

Causality • Three conditions for causality: 1. Correlation (whenever A happens, so does B) 2. Temporal precedence (if A causes B, A always occurs before B) 3. Lack of alternative explanations • Pure logic: There’s always an alternative explanation. • Pragmatic or common sense response: Some alternative explanations are silly. • I.e. want to set up experiments where the likelihood of an alternative

explanation is low. !Validity of experiments

• New Coke experiments tried to maximise internal validity: Change one factor, keep all others constant to “isolate” effects of that factor (OFAT).

• Ignored issues of external validity: That the “lab” setup is a reasonable simplification of the real process. !

Experimental designs • “Classical” vs. “Statistical”: 1. Classical: Pre-experimental, Quasi-experimental, True-experimental 2. Statistical: Completely randomised, randomised block, factorial, fractional

factorial. • Example: How to determine whether price of electricity affects demand.

!The lingo

• Dependent variable: The outcome of interest (effect), denoted Y. • Independent variable: A (possible) cause, denoted X. • Factor: Synonymous with X variable. • Level: Value of a factor. • Treatment: The level(s) of the factor(s) in the experiment. • Experimental unit: The thing being experimented upon. !

Decisions • Three classes of decisions when planning an experiment: 1. Experimental design 2. Lab or field setting 3. Validity !

Notation • O = observation (of the Y variable) • X = exposure (to the treatment) • EG = experimental group • R = random assignment of units to group • M = matching groups • Left to right = flow in time !

Pre-experimental designs (1) • Simplest possible design: Try something and see what happens.

!!• We recruit a sample of households (EG). • Raise the price of electricity at peak times during July (X). • Observe the change in kWh used compared to previous July (O). !

Flaws of post-test only • Say usage decreased (O is negative): How do we know if it’s due to the

treatment (X)? • What if it is warmer this year? I.e. less electricity consumed for heating. • What if householders across the country had become more saving-conscious

due to exposure to a campaign encouraging saving power? • What if people who agreed to participate wanted to reduce usage anyway? !

Extraneous variables • Extraneous variable: Influential factor not included in design. • History: What’s been happening in the environment while the experiment has

been running. • Maturation: What’s been happening to the units.

• Selection bias: Something special about people in the EG – in particular, self-selection bias.

• This is a factor that cannot be manipulated. !!Pre-experimental designs (2)

• Deal with history and maturation by using a control group (CG): A group that is not exposed to the treatment.

!!!• Effect of X (“experimental effect”) is then O1 – O2. !

Remaining flaws • If O differs between EG and CG then change could be due to X, but not

conclusively. • Self-selection bias: No specification of exactly how the units were selected

and then allocated to groups. • If EG is “special” try to make CG special in the same way – match them

according to key variables. !Pre-experimental designs (3)

• Households are matched according to factors affecting O (e.g. number of people, floor area, water heating method).

!!!• If O1 does not equal 0 we can be more sure it is due to X, but still not

completely sure. !Pre-experimental designs (4)

• The above designs can’t deal with pre-existing trends. !!

• Measure Y before and after applying treatment. • Can add a pre-test to any of the previous designs. !

Always use pre-tests? • Merely measuring/observing Y can, by itself, induce a change in the

measurement of Y. • Three types of extraneous effects (as opposed to variables): 1. Measurement effect: Act of measuring can cause changes in Y. • E.g. asking people questions about their beliefs or attitudes induces a change.

• People who know they’re being studied change behaviour because of that (“Hawthorne Effect”).

2. Instrumentation effect: Act of measuring can change the instrument. • Particularly relevant when “instrument” is a person e.g. interview or focus

group moderator. • Y doesn’t change but the measurement of it (O) does. 3. Mortality: Units (e.g. people) dropping out of the experiment. • Can be a problem if it’s because of the treatment (X). !

Dealing with extraneous effects • General strategy to eliminate instrumentation and measurement effects –

reduce number of measurements (observations). • Sometimes effects are so large that pre-tests are not advisable. !

True experimental design (1) • What makes them “true” is random assignment of units to groups.

!!!• Aim is to neutralise effects of extraneous variables. • Can also reduce or eliminate selection-bias. !

True experimental design (2) • The above design can’t totally eliminate measurement effects.

!!!!!!• Why not always use Solomon? • Cost, or measurement effects are negligible. !

Classical design problems • Factors challenging validity of experimental effects: 1. History 2. Maturation 3. Selection-bias 4. Instrumentation 5. Measurement 6. Mortality • Some of these can be reduced or eliminated by particular design, but mortality

can’t.

• No one design is best; it is usually a trade-off between likely problems that may be involved in implementation. !

Internal and external validity • Want to ensure that only the treatment varies (control extraneous factors) – are

we sure the results were due to the treatment? (Internal validity) • Want to ensure that lab results reflect “real world” situation (include relevant

factors) – can the results be generalised outside the lab? (External validity) • Can we have both? !

Trade-offs • Lab experiments try to eliminate extraneous variables to maximise internal

validity. • In doing so, can make process so artificial that it no longer resembles the

“natural” process – sacrifices external validity. • Can’t maximise both, need to decide which is more important (context-

specific). !“Lab” and “field” experiments

• How tightly do we control extraneous factors? - In the lab we try to keep as many factors as possible constant. - In the field we try to replicate “real-world” or “natural” levels of factors. • Replicating real world in the lab can be very expensive. • Analysing many factors at once is very complicated. !

Quasi experiments • “Quasi” means “not really” or “almost but not quite”, i.e. “almost an

experiment”. • Applies when experimenter can control some factors but cannot control: 1. Allocation of units to groups 2. Timing of application of treatment • But can control timing of observations. • Quasi experiments are observational studies of a naturally occurring

“experiment”.