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2ppMKTG2113 Lecture 5: Executing your research – Quantitative research methods (slides 40-57) Experiments (casual design) – Experiments are a research method in which conditions are controlled so that one or more independent variables can be manipulated to test a hypothesis about a dependent variable. – For example, influence of brand name identification and nutrition labels on consumers’ taste perception. – In an experiment, one variable (the independent variable, IV) is manipulated and its effect on another variable (the dependent variable, DV) is measured, while all other variables that may confound the relationship are eliminated or controlled.

Experimental Design Considerations

A field or laboratory Experiment – Field experiments are conducted in a natural setting in which complete control of extraneous variables is not possible (e.g. test markets). – E.g. fast food chains can conduct field experiments to test market a new flavour or product. – Laboratory experiments are conducted in artificial settings over which the researcher has almost complete control over the research setting. – E.g. viewing TV commercials for competing products and then allowing viewers to purchase in a simulated store environment. – Controlled store tests are a hybrid between a laboratory experiment and test market.

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– E.g. Test products are sold in selected stores to actual customers. Decisions must be made about several basic elements of an experiment, including: – manipulation of the independent variable/s. – selection and measurement of the dependent variable. – selection and assignment of subjects. – control over extraneous variables.

What am I measuring? – Manipulation of the independent variable/s (what do I change?): – The IV/s can be manipulated, changed or altered independently by the researcher. – Hypothesised to have a causal influence i.e. having a functional relationship between the independent and dependent variables. – Selection and measurement of the dependent variable (what do I observe?): – The criterion or standard by which the results of an experiment are judged (typically one dependent variable) – The value of a DV is expected to be dependent on the experimenter’s manipulation of the IV. – Experimental treatments are alternative manipulations of the independent variable being investigated. – There can be several experimental treatment levels. – Some examples: – Variations of advertising copy (e.g. message framing +/-) and graphic design (e.g. colour). – Variations of prices ($5.09; $5.59; $5.99) in pricing experiment. – Variations of package sizes (300ml; 400ml; 600ml) or type (tetra vs. bottled) in package design experiment. – Experimental group (EG): – Group of subjects exposed to the experimental treatment. – Control group (CG): – Group of subjects not exposed to the experimental treatment; compared to the experimental group to determine any causal effects. How do I select and assign test units? – Test units: – Subjects/entities whose responses to experimental treatments are observed or measured. 2

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Sample selection error: – Due to procedure used to assign subjects either to the experimental (EG) or control group (CG). – Self-selection bias: – Subjects included in the experiment not randomly selected; hence not possible to generalise the results to wider population. – Random sampling error: – Repetitions of the basic experiment at times favour one experimental condition or another on a chance basis. – Randomisation: – The random assignment of subjects e.g. to treatment and control groups. – Matching: – Assigning subject to groups that ensure the groups are matched based on pertinent characteristics.

Are there validity issues I need to be aware of? – Experimenter needs to hold conditions constant and manipulate the treatment in a consistent manner. – Experimenters may strive for constancy of conditions; e.g. use of priming to elicit certain condition. – Blinding – is used to control subjects’ knowledge of whether or not they have been given an experimental treatment. – Constant experimental error – occurs when extraneous variables (e.g. time, temperature, mood, health) are allowed to influence the dependent variable every time the experiment is repeated. This results in a systematic bias. • Variables that the researcher cannot control but should average out over a series of experiments. If not accounted for, they can have a confounding impact on the dependent variable measures that could weaken or invalidate the results of an experiment. Experimental validity: Internal Validity – Internal validity refers to whether an experimental treatment was the sole cause of observed changes in the dependent variable. – If the observed results were influenced or confounded by extraneous factors, the experiment is not internally valid. – Researcher’s balancing act between internal validity vs. external validity.

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Threats to internal validity: – History Effect – Caused by specific events in the external environment beyond the researcher’s control; occurring in-between measurements. – E.g. Change of marketing strategies by competitors during test marketing experiment. – Cohort effect. – Selection Effect – Sample bias that occurs from differential selection of respondents for the comparison groups. – Improper sample design or sampling procedure execution. – E.g. inappropriate assignment of test subjects to experimental treatment groups. – Maturation Effect – Caused by subjects maturing or changing in some way that will affect the experiment results. – Due to tiredness, boredom etc. – Experience gained over time. – Testing Effect – Also known as pre-testing effects. – Initial measurement alerts subjects to the nature of the experiment. – Results in them acting differently and affects the experiment’s validity. – Instrumentation Effect – Caused by a change in question wording, interviewers or procedures used to measure the dependent variable. – Guinea Pig Effect – Caused by subjects changing their usual behaviour or attitudes to cooperate with the experimenter. – Hawthorne effect – Caused by subjects being aware that they are participants of an experiment. – Mortality Effect – Caused by sample attrition (drop-outs). – Completing one treatment but not in another. Experimental Validity: External Validity – If the experimental situation is artificial (i.e. does not reflect the true setting and conditions in which the investigated behaviour takes place), then the experiment is not externally valid. – Simulated >>> Real World – Threats to internal validity jeopardise external validity. – Is internal validity needed to achieve external validity? 4

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Factors effecting external validity: – Student surrogates: The use of university students as experimental subjects. – Extraneous variables may have an impact on the DV, thereby distorting the experiment. – Issue: not always possible to control everything in marketing experiments.

Which Experimental Design to use? – Basic experimental designs allow a single independent variable to be manipulated to observe its effect on a single dependent variable – E.g. impact of price on sales. – Factorial experimental designs allow for an investigation of the interaction of two or more independent variables – E.g. impact of price and advertising on sales. – Repeated measures? – An experimental technique in which the same subjects are exposed to all experimental treatments to eliminate any problems due to subject differences. Common symbols: – X: Exposure of a group to an experimental treatment – O: Observation/measurement of the dependent variable – [R]: Random assignment of test subjects to different treatment groups – EG: Experimental group of test subjects – CG: Control group of test subjects – à : Represents a movement through time

Quasi-experimental Designs – Cannot be classified as a true experiment because it lacks adequate control of extraneous variables. – One-shot design – an after-only design in which a single measure is recorded after treatment is administered. – A single group of test subjects is exposed to the independent variable treatment X, and then a single measurement of the dependent variable is taken (O1). – (EG): X à O1 – E.g. A researcher wishes to measure all customer reactions to a new product display in a supermarket. – One-group, pretest-posttest design – Experimental group is measured before and after treatment is administered.

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– First a pre-treatment measure of the dependent variable is taken (O1), then the test subjects are exposed to the independent treatment X, and then a post-treatment. measure of the dependent variable is taken (O2). – Often used in marketing research. – (EG): O1 à X à O2 – E.g. A researcher wishes to measure advertising effects among consumers. Static Group Design – An after-only with control group design measuring group exposed to experimental treatment and control group without exposure to treatment. No pre-measure is taken. – There are two groups of test subjects: one group is the experimental group (EG) and is exposed to the independent treatment, and the second group is the control group (CG) and is not given the treatment. The dependent variable is measured in both groups after the treatment. – Group 1 (EG): X à O1 – Group 2 (CG): O2 – E.g. A researcher wishes to compare product sales performance across 2 store settings.

True Experimental Designs – Randomisation [R] of a subject assignment – Pretest–posttest, control group design (before-after with control) – Both experimental and control groups are measured before and after treatment administered on experimental group. – Test subjects are randomly assigned to either the experimental or control group, and each group receives a pre-treatment measure of the dependent variable. Then the independent treatment is exposed to the experimental group, after which both groups receive a post-treatment measure of the dependent variable. – EG: [R] O1 à X à O2 – CG: [R] O3 à O4 – A researcher wishes to measure brand awareness effects among consumers. – Posttest-only, control group design – An after-only with control design measuring both experimental and control groups. – Test subjects are randomly assigned to either the experimental or the control group. The experimental group is then exposed to the independent treatment, after which both

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groups receive a post-treatment measure of the dependent variable. – EG: [R] X à O1 – CG: [R] O2 – After-only with control. – Used when pre-test measurements are not possible. Solomon Four Group – Combines both experimental designs, providing a control for the interactive testing effect and other sources of extraneous variation – This design combines the ‘pretest–posttest, control group’ and ‘posttest-only, control group’ designs and provides both direct and reactive effects of testing. – Not often used in marketing research practices because of complexity and lengthy time requirements.

Time Series Designs – Experiments conducted over long periods of time to distinguish temporary and permanent changes in dependent variables. – The design is often used in political polls tracking candidates’ popularity. – Distinguishes temporary from permanent changes.

Test Marketing (Field experiment) – Test marketing is a controlled (field) experimental procedure that provides an opportunity to test a new product or a new marketing plan under realistic market conditions to measure sales or profit potential. – Offers the opportunity to estimate the outcomes of alternative courses of action. – Allows management to identify and correct weaknesses in either the product or its marketing plan before a national launch. 7

Key decisions involve: – whether to test market or not. – working out the functions of the test market. – deciding on the type of test market. – deciding on the length of the test market. – deciding on where to conduct the test market. – estimating and projecting the results of the test market.

Lecture 6: Analysing your data – Making sense of qualitative data Analysing Qualitative Data 8

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The goal of qualitative analysis is deep understanding. We need to extract meaning from the data we have collected, hence largely inductive. – Recall …

Several ways to analyse qualitative data that typically involve: – 1. Preparing to analyse the data – 2. Organising the data – 3. Analysing the data for meaning to generate insights Insights – “Deep truths” e.g. about consumers in relation to an issue of interest to the researcher. – It is not just recording & playing back what people have said. – It is to understand why, beyond than just what was actually said or how they behaved. – Components of Qualitative Data analysis Interactive model

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Largely inductive; let the data speaks for itself. – In some cases, pre-determined categories/codes are derived from existing literature or experience; known as apriori coding. – Prepare to analyse the data: – Start by re-reading (review) your research objectives. – Organise data so you can easily read and contemplate its meaning: – Write out data (transcripts) from fieldwork. – Fully typed transcripts are ideal.

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Include non-verbal information if possible. – Field notes/post interview notes. – Provide “thick descriptions” from observations. – Analyse the data: – Deconstruct the data looking for meaning/themes. – Reconstruct the data looking for patterns/answers. – Interpret the data looking for a story. 1. Preparing to analysis the data – Re-read (refresh in your mind) the aims and objectives of the research: – This dictates the areas on which you must find information. – Understand the research as a whole (take a holistic approach). – Keep in your mind: – What are they saying? – What category of behaviour is this this an example or definition of? – Example: “I don’t like Airline X because they are always late.” – This statement identifies that “lateness” is a property attributed to Airline X (and Airline service as a whole) and “lateness” is a property of why something is disliked. – It indicates how an opinion is formed. Hence, this property is used as a basis to form one’s opinion about Airline X and Airline service in general. 2. Organising the data – Organise the data so that it can be digested easily: – Write out a transcript of the audio tape or video, or any recording device you may have used. – Include your field notes, images, materials etc. you have collected. – Many computer programmes now available which help you organise large amounts of data. 3. Analysing the data – Deconstruct the transcript into units of meaning (codes). – Identifying themes. – Give it a label e.g. “Lateness” (Airline X example). – Write comments explaining why you choose this “unit of meaning” or “code”.

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– Explain what its means and why you think this is important. – Go through the full transcript in this way, but also be aware that what was said previously can affect how you interpret each piece of data later on. Reconstruct the data looking for patterns/answers. – Analyse the codes looking for categories of behaviour and answers to research questions/objectives. – Comparing what is said and done continually; looking for consistent patterns (e.g. frequency). – Look for occurrences and co-occurrences. – Coding question: What is this an example of? E.g. Service expectation? Operational issues? Product flaw? Interpret the codes as to the overall “story” and meaning of the research. Repeat the above steps done on each interview or data collection point and then comparing them with each other.

Analysing the data: Lecture examples/practice Transcript (I1) I have had my current phone for a while. A fair few months, I guess. It's a Samsung that I got- I think it was on contract. Basically, the story of it was that my mum gave it to me as a gift and she put me onto a plan and volunteered to pay for it. So I was pretty lucky there. So I am on a Optus’ service. No, it all came as a contract. (I2) I was with 3G before. I changed probably about five months ago. No, just as in 3 mobile, in response to the- when I used to watch the cricket. So I was with them. I just live in Lane Cove and for some reason we just get dead zones and things like that with that company. So it was just shocking. So I changed over. I am with Vodafone now. I have been with them in the past. Basically, 3G came out with pretty cheap phones and good deals at the time. So I switched to them and, yeah, changed back. It's before 1999. I had a silver flip phone. It had a camera but no net access or anything like that. (I3) I have got a Samsung Galaxy. I changed about six months ago because my plan was ending. My provider is Telstra. I went from Optus to Telstra. I just wanted the phone, actually. The new phone. Yeah, my plan was ending and also I was interested in the phone. That was the best plan between the providers at the time. That was before six months ago that I changed. (I4) I have a pretty bad history of losing phones. So at the moment I am not on a contract. I am prepaid with Optus. I used to be with Vodafone. Then I went overseas for a bit, came back and I didn't know where my phone was. I thought my contract is running out. I

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am swapping anyway, so I am prepaid at the moment. Prepaid with Optus. Right now, BlackBerry. (I5) I was with Vodafone for about 12 years and had several unsatisfactory experiences and the primary one was, I had barely any reception at my house: that deteriorated over time and then I got fed up. I switched to Telstra. Now I am on iPhone 6, $100 a month. I can't remember what I get for that, but I don't usually pay more than that. Objective-based analysis – Large sheet of paper or Word document set up in table form: – Listen to or review data records and fill in the cells with key quotes, observation interpretations, behaviour notes etc. according to the following organisation – Column headings: • Main information requirements/questions. – Row headings • Main issues expressed categorised into e.g. positive/negative etc. (depending on what is being studied). • Use a key to indicate status of respondent. – The result will be a structuring of results which highlights the main findings and produces a skeleton of findings for the final report.

Presenting findings/insights: Examples

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Key Notes while analysing your data – Interview by interview or by objectives. – Is the meaning/theme derived from the data or merely imposed upon data by the researcher? – Let the data speak to you. – Triangulate: – Helps to assess whether we are finding credible and/or consistent patterns/answers in the data. Be very careful about drawing conclusions from indicators that appear to be

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important opinions/thoughts, but only once or twice in your entire dataset. Establish consistent procedures and rules among researchers. What is the main story here? – How does the rest of the themes relate to this? Any negative case/s &/or off-topic (unexpected) worth highlighting? • Keep this grounded in your data & related to your objectives. – Are my findings consistent with or detract from past studies/findings? Qualitative analysis requires “data immersion” to discover its significance. This [iterative] process involves: – Your curiosity, patience, ambiguity/uncertainty tolerance, and attention to detail. – Line-by-line scrutiny and open coding, successive rounds of focused coding, abstraction etc. – Creating your own analytical notes/memos, asking questions about the data. – Consulting existing literature, compare and contrast your findings. – Drafting the story, re-writing and revising it as you uncover more.

Lecture 7: analysing your data – making sense of quantitative data Understanding relationships (effects) – Quantitative...