MKTG210- Quant - Summary Marketing Research PDF

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MKTG210 Slide Notes Week 11 Market Research is the application of the scientific method in searching for the truth about marketing phenomena. (Zikmund Babin, 2013) The need of market research: parallels the recognition of an increasingly complex business environment provides information to make stra...

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MKTG210 - Slide Notes Week 11 Market Research is the application of the scientific method in searching for the truth about marketing phenomena. (Zikmund & Babin, 2013) The need of market research: - parallels the recognition of an increasingly complex business environment - provides information to make strategic decisions - provides a greater understanding of new markets - as business is becoming more global (Ivy, 2016) Research applications - defining marketing opportunities and problems - generating and evaluating marketing idea - monitoring performance - and generally understanding the marketing process. (Zikmund & Babin, 2013) Process includes - ideas and theory development - problem definition - gathering information - analysing data - communicating the findings inc. potential implications (Zikmund & Babin, 2013) Classification of research

- Research conducted with the intention of applying

Applied marketing research

the results of its findings to solve specific problems - Practical problem solving emphasis

- Research that improves our understanding of

Basic marketing research

problems

- Theoretical problem solving - No direct impact on performance, action or policy The research process 1. Establish the need for information (Problem identification) 2. Specify the research objectives 3. Determine the research design 4. Develop the data collection procedure 5. Design a sample 6. Collect the data 7. Process the data 8. Analyse the data 9. Drafting the Report

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In defining a problem - researcher should take into account the purpose of the study Relevant background information secondary data sources what information is needed how this information will be used in decision making (for applied studies) or add to knowledge (in basic research studies)

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The problem definition process (p.41)

(Malhotra & Birks, 2007) Issues to consider with Problem definition P

Past information (Secondary data/ MIS)

R

Resources and constraints

O

Objectives

B

Behaviour (human and organisational)

L

Literature and environment

E

ethics

M

Manageability

Problem formulation This is sequential process starting with - meeting the decision maker/marketing team - Meeting with industry/sector experts - Analysing secondary data (existing data)

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The nature of problems - research provides problem, not solutions, solutions require managerial judgment - The researcher needs to understand the nature of the problem: - is it a: Red light problem (symptom) or problem choice 2. Specifying the research objectives Defining the research objective - The definition should: - allow the researcher to obtain all the information needed to address the problem - guide the researcher in proceeding with the project information orientated - This entails determining what information is needed and how it will be obtained in the most feasible way What is the purpose of the research project? - Research question (what should be done to….) - Research objectives (answer) Specifying information needs - list all the specific information needs of the decision maker - Each question in the data collection instrument should have a direct correspondence to an information need, and info need should have a direct correspondence to each objective B.A.D.I - Helps defining a problem - Behaviour: Past and intentions, motivations - Attitudes and personality - Demographics and socio-economic characteristics - Interests, media, activities

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Lecture 2 Sample is a subgroup of the population selected fro participation in study - Populations are normally too large to interview Census an investigation of all the individual elements that make up a population The main objective of most research is to obtain information about a population. Type of study

Sample

Census

1. Budget

Small

Large

2. Time available

Short

Long

3. Population size

Large

Small

4. Variance in the characteristic

Small

Large

5. Cost of sampling errors

Low

High

6. Cost of non sampling errors

High

Low

7. Nature of measurement

Destructive

Nondestructive

8. Attention to individual cases

Yes

No

Errors: Potential Sources of Error in Research Design

Random Sampling Error The difference between the sample result and the result of a census conducted using identical procedures Non-Sampling Error is a statistical error caused by human error to which a specific statistical analysis is exposed.

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Non-response error Occurs when some of the respondents included in the sample do not respond. Response error Respondents who do respond but who give inaccurate answers or whose answers are misrecorded or mis-analysed.

Researcher errors: Surrogate information error may be defined as the variation between the information needed for the marketing research problem and the information sought by the researcher. For example, instead of obtaining information on consumer choice of a new brand (needed for the marketing research problem), the researcher obtains informa- tion on consumer preferences because the choice process cannot be easily observed. Measurement error may be defined as the variation between the information sought and information generated by the measurement process employed by the researcher. While seeking to measure consumer preferences, the researcher employs a scale that measures perceptions rather than preferences. Population definition error may be defined as the variation between the actual population relevant to the problem at hand and the population as defined by the researcher. Sampling frame error may be defined as the variation between the population defined by the researcher and the population as implied by the sampling frame (list) used. For example, the telephone directory used to generate a list of telephone numbers does not accurately represent the population of potential landline consumers due to unlisted, disconnected and new numbers in service. It also misses out the great number of con- sumers that choose not to have landlines, exclusively using mobile telephones Data analysis error encompasses errors that occur while raw data from questionnaires are transformed into research findings. For example, an inappropriate statistical procedure is used, resulting in incorrect interpretation and findings.

Interviewer Errors: Respondent selection error occurs when interviewers select respondents other than those specified by the sampling design or in a manner inconsistent with the sampling design. For example, in a readership survey, a non-reader is selected for the interview but classified as a reader of Le Monde in the 15–19-year-old category in order to meet a difficult quota requirement. Questioning error denotes errors made in asking questions of the respondents or in not probing, when more information is needed.

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For example, while asking questions an interviewer does not use the exact wording or prompts as set out in the questionnaire. Recording error arises due to errors in hearing, interpreting and recording the answers given by the respondents. For example, a respondent indicates a neutral response (undecided) but the interviewer misinterprets that to mean a positive response (would buy the new brand). Cheating error arises when the interviewer fabricates answers to a part or the whole of the interview. For example, an interviewer does not ask the sensitive questions related to a respondent’s debt but later fills in the answers based on personal assessment.

Respondent Error: Inability error results from the respondent’s inability to provide accurate answers. Respondents may provide inaccurate answers because of unfamiliarity, fatigue, boredom, faulty recall, question format, question content and other factors. For example, a respondent cannot recall the brand of toothpaste purchased four weeks ago. Unwillingness error arises from the respondent’s unwillingness to provide accurate information. Respondents may intentionally misreport their answers because of a desire to provide socially acceptable answers, to avoid embarrassment, or to please the interviewer.33 For example, to impress the interviewer, a respondent intentionally says that they read The Economist magazine. The sampling design process 1. Define the population 2. Determine the sampling frame 3. Select sampling technique(s) 4. Determine the sample size 5. Execute the sampling process Define the target population Elements

male or female aged 18-55

Sampling units

households

Extent

Australia, Brazil, France, Germany, UK

Time

2005

Population the aggregate of all the elements, sharing some common characteristics, which compromises the universe for the purpose of the study

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Target population the collection of elements or objects that posses the information sought by the researcher and about which inferences are to be made Sampling frame a list of elements from which a sample may be drawn; also called target population Sampling techniques

Non-probability sampling: Sampling techniques that do not use chance selection procedures but rather rely on the personal judgement of the researcher. Probability sampling: A sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample Choosing non-probability vs probability sampling

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Non-probability sampling

Definition

Example

Convenience sampling

A non-probability sampling technique that attempts to obtain a sample of convenient elements. The selection of sampling units is left primarily to the interviewer.

(1) use of students, church groups and members of social organisations, (2) street interviews without qualifying the respondents, (3) some forms of email and Internet survey, (4) tear-out questionnaires included in a newspaper or magazine (5) journalists interviewing ‘people on the street’.

Judgmental sampling

A form of convenience sampling in which the population elements are purposely selected based on the judgement of the researcher.

(1) test markets selected to determine the potential of a new product, (2) purchase engineers selected in industrial marketing research because they are considered to be representative of the company, (3) product testing with individuals who may be particularly fussy or who hold extremely high expectations, (4) expert witnesses used in court, and (5) supermarkets selected to test a new merchandising display system.

Quota sampling

A non-probability sampling technique that is a two-stage restricted judgemental sampling. The first stage consists of developing control categories or quotas of population elements. In the second stage, sample elements are selected based on convenience or judgement.

The control characteristics were gender, age and propensity to donate to a charity

Snowball sampling

A non-probability sampling technique in which an initial group of respondents is selected randomly. Subsequent respondents are selected based on the referrals or information provided by the initial respondents. By obtaining referrals from referrals, this process may be carried out in waves.

Examples include users of particular government or social services, such as parents who use nurseries or child minders, whose names cannot be revealed; special census groups, such as widowed males under 35; and members of a scattered minority ethnic group.

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Probability Sampling

Definitions

Examples

Single random Sampling

A probability sampling technique in which each element has a known and equal probability of selection. Every element is selected independently of every other element, and the sample is drawn by a random procedure from a sampling frame.

the researcher first compiles a sampling frame in which each element is assigned a unique identification number. Then random numbers are generated to determine which elements to include in the sample. The random numbers may be generated with a computer routine or a table

Systematic Sampling

A probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.

a sample of 1,472 subscribers from the publication’s domestic circulation list. If we assume that the subscriber list had 1,472,000 names, the sampling interval would be 1,000 (1,472,000/1,472). A number from 1 to 1,000 was drawn at random. Beginning with that number, every subsequent 1,000th was selected.

Stratified Sampling

A probability sampling technique that uses a two- step process to partition the population into subsequent subpopulations, or strata. Elements are selected from each stratum by a random procedure.

A telephone survey was conducted of 659 full-time employees over the age of 18 with a quota of 80% to be participants in a retirement plan such as a pension. The sample was stratified by income and age because of differences in the use of the Internet and possible varying concerns about retirement services.

Cluster sampling

A two-step probability sampling technique where the target population is first divided into mutually exclusive and collectively exhaustive subpopulations called clusters, and then a random sample of clusters is selected based on a probability sampling technique such as SRS. For each selected cluster, either all the elements are included in the sample, or a sample of elements is drawn probabilistically.

natural" clusters (city blocks, schools, hospitals, etc.). For example, to conduct personal interviews of operating room nurses, it might make sense to randomly select a sample of hospitals (stage 1 of cluster sampling) and then interview all of the operating room nurses at that hospital. Using cluster sampling, the interviewer could conduct many interviews in a single day at a single hospital

Other

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Strengths and weaknesses of sampling techniques

Sample sizes in marketing studies Type of study

Minimum size

Typical range

Problem identification research

500

1000-2500

Problem-solving research (e.g. pricing)

200

300-500

Product tests

200

300-500

Test marketing studies

200

300-500

TV, radio, or print advertising

150

200-300

Test-market audits

10

10-20

Focus groups

2

6-15

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Improving response rates

Adjusting nonresponse rates

- Subsampling of Nonrespondents – the researcher contacts a subsample of the nonrespondents, usually by means of telephone or personal interviews.

- In replacement, the nonrespondents in the current survey are replaced with nonrespondents

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-

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from an earlier, similar survey. The researcher attempts to contact these nonrespondents from the earlier survey and administer the current survey questionnaire to them, possibly by offering a suitable incentive. In substitution, the researcher substitutes for nonrespondents other elements from the sampling frame that are expected to respond. The sampling frame is divided into subgroups that are internally homogeneous in terms of respondent characteristics but heterogeneous in terms of response rates. These subgroups are then used to identify substitutes who are similar to particular nonrespondents but dissimilar to respondents already in the sample. Subjective Estimates – When it is no longer feasible to increase the response rate by subsampling, replacement, or substitution, it may be possible to arrive at subjective estimates of the nature and effect of nonresponse bias. This involves evaluating the likely effects of nonresponse based on experience and available information. Trend analysis is an attempt to discern a trend between early and late respondents. This trend is projected to nonrespondents to estimate where they stand on the characteristic of interest. Weighting attempts to account for nonresponse by assigning differential weights to the data depending on the response rates. For example, in a survey the response rates were 85, 70, and 40%, respectively, for the high-, medium-, and low-income groups. In analyzing the data, these subgroups are assigned weights inversely proportional to their response rates. That is, the weights assigned would be (100/85), (100/70), and (100/40), respectively, for the high-, medium-, and low-income groups.

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Week 3 Questionnaire is a structured set of questions for obtaining information from participants. It is displayed in a standardised format to ensure that the data obtained are internally consistent and can be analysed in a uniform and coherent manner (Malhotra et al. 2013) Measurement is the process of describing some property of a phenomenon of interest, usually by assigning numbers in a reliable and valid way (Zikmund and Babin 2010) What do measures measure? Concepts

Variables

Constructs

A generalised idea that represents identifiable and distinct meanings.

The different values of a concept. Also called “measurement items”

Concepts that are measured with multiple variables. Also called “latent constructs”

Why use questionnaire/survey? Provides standardisation Easy to administer Gets “beneath the surface” Easy to analyse Reveal subgroup differences

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(Burns and Bush, 2014) Marketing research are interested in measuring both objective and subjective properties

- Objective: physically verifiable characteristics (e.g., age, income, sales) that are observable and verifiable

- Subjective: mental constructs which cannot be directly observable and intangible (e.g., attitude, satisfaction level, perception, preferences, motivation).

E.g. Measuring objective properties

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E.g. Measuring subjective properties

“Subjective properties are unobservable and intangible, and they must be translated onto a rating scale through the process of scale development”" Burns and Bush (2014) Scale Development Procedure: C-OAR-SE 1. Construct definition – definition of the phenomena/object, its components, and its rater 2. Object classification – is the construct singular or have multiple components? 3. Attribute classification – is the construct concrete or abstract? 4. Rater identification – who are the perceivers of the construct? Individuals/ groups/ experts 5. Scale formation – putting the scale together and pre-test the items in a survey 6. Enumeration – the rule for deriving a score from the scale (Rossiter 2002) Scales of Measurement & Type of analysis

Scale

Definition/characteristics

Examples

Descriptive statistics

Inferential statistics

Nominal scale

Values are assigned to an object for identification or classification purposes

Student registration numbers; numbers on athlete jerseys
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