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Full Course Flashcards

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1
Q

What is Market research?

A

The process of systematically gathering, recording, and analyzing data about customers, competitors, and the market. It helps businesses make informed marketing decisions by providing insights into customer needs, preferences, and behaviour, competitor strategies, and overall market trends.

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2
Q

What is the Market research process?

A

The 5 main steps are: 1. Define the problem and research objectives 2. Develop the research plan (decide on methodology, data collection, sample design) 3. Collect the data (fieldwork) 4. Analyze the data (using statistical techniques) 5. Interpret and report the findings (conclude and make recommendations).

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3
Q

What is Problem-Identification Research?

A

Seeks to uncover potential problems or opportunities before they become obvious (e.g., declining brand loyalty, emerging trends).

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4
Q

What is Problem-Solving Research?

A

Focuses on finding solutions to specific, clearly defined problems (e.g., why sales dropped, how to reposition a brand).

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5
Q

What are the Limitations of market research?

A

Market research cannot guarantee success — it reduces uncertainty but doesn’t eliminate it. It can be expensive and time-consuming. Results may be affected by poor research design or respondent bias. Market dynamics may change after the research is conducted, making findings outdated.

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6
Q

What is the Research Brief?

A

Purpose: Outline the problem and what information is needed. Components: Background information, research objectives, target audience, budget, timelines.

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7
Q

What is the Research Proposal?

A

Purpose: Respond to the brief by proposing how the research will be carried out. Components: Research design, methodology, sampling plan, data collection methods, timeline, costs.

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8
Q

What is Research design?

A

Blueprint for collecting, measuring, and analysing data.

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9
Q

What is the Exploratory research design?

A

Flexible, unstructured, used to gain insights.

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10
Q

What is the Conclusive research design?

A

Structured, formal, verifies hypotheses, supports decision-making.

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11
Q

What is the Descriptive research design?

A

Describes characteristics (e.g., who, what, when).

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12
Q

What is the Causal research design?

A

Identifies cause-effect relationships via experiments.

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13
Q

What is the Participants’ influence on research design?

A

Participants’ availability, willingness, and characteristics influence method choice (e.g., sensitive topics → qualitative methods).

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14
Q

What are the Strengths and weaknesses of exploratory research?

A

Positive: Insighful, Negative: Not conclusive.

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15
Q

What are the Strengths and weaknesses of descriptive research?

A

Positive: Generalisable, Negative: No causality.

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16
Q

What are the Strengths and weaknesses of causal research?

A

Positive: Proves causality, Negative: Costly, complex.

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17
Q

What is the Random sampling error?

A

Chance-based variability.

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18
Q

What are the Non-sampling errors?

A

Measurement error, interviewer bias, nonresponse bias.

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19
Q

What is the purpose of Internal databases?

A

Track customer behaviour over time.

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20
Q

What are the Geo-demographic information systems?

A

GIS tools integrate geographic, demographic, and purchasing data for better targeting.

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21
Q

What are the Behavioural and attitudinal profiles?

A

Combine transactional (purchase) data + attitudinal (surveys) data for full customer insights.

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22
Q

What are the Focus groups planning and conducting?

A

Plan: define objectives, recruit participants, prepare moderator guide. Conduct: skilled moderator, open-ended discussions, record insights.

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23
Q

What are the Advantages of focus groups?

A

Rich insights, flexible.

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24
Q

What are the Disadvantages of focus groups?

A

Not statistically representative, groupthink risk.

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25
What are the Advantages of Online focus groups?
Cost-effective, geographically flexible.
26
What are the Advantages of Traditional focus groups?
Better for non-verbal cues.
27
Name different Projective techniques?
Association: word association, Completion: sentence/story completion, Construction: creating stories from pictures, Expressive: role-playing scenarios.
28
Name different Survey techniques?
Telephone, personal interview, mail, online surveys.
29
What is the Criteria for evaluating surveys?
Speed, cost, response rate, flexibility, data quality.
30
What are Observation techniques?
Personal observation, mechanical observation (e.g., eye-tracking, scanner data).
31
What is the Criteria for evaluating observation?
Intrusiveness, cost, accuracy.
32
What is the Concept of causality?
Scientific causality: Stronger proof via experiments (not just correlations).
33
What are the Extraneous variables and control?
Confounding factors threaten validity; control using randomisation, matching, holding variables constant.
34
What are the Experimental designs?
Pre-experimental: No control group. True experimental: Random assignment. Quasi-experimental: No randomisation. Statistical designs: Multiple variables tested simultaneously.
35
What are the Characteristics defining measurement levels?
Description, Order, Distance, Origin.
36
What are the Primary scales?
Nominal, Ordinal, Interval, Ratio.
37
What are the Comparative scaling techniques?
Paired comparison: Choose between two. Rank order: Rank a list. Constant sum: Allocate 100 points across options. Non-comparative scaling techniques,Continuous: Mark position on a line. Itemised: Choose from fixed options. Likert scale: Agree-disagree statements. Semantic differential: Rate between two bipolar adjectives. Stapel scale: Rate on a +5 to -5 scale without a midpoint.
38
What is the Purpose and objectives of questionnaire design?
Get accurate, honest, and complete answers; keep participants engaged; minimise errors.
39
What are the Trade-offs in questionnaire design?
Detail vs respondent fatigue; open-ended vs closed questions.
40
What is the Questionnaire design process?
Define information needed → Draft questions → Sequence logically → Pre-test → Revise → Finalise.
41
What is the Sampling design process?
Define target population → Determine sampling frame → Select technique.
42
What are the Non-probability sampling techniques?
Convenience, Judgmental, Quota, Snowball.
43
What is the Probability sampling techniques?
Simple random, Systematic, Stratified, Cluster.
44
When do you use non-probability vs probability sampling?
Non-probability: Exploratory studies, quick insights. Probability: Descriptive and causal research needing generalisation.
45
What is Sampling distribution, statistical inference, and standard error?
Sampling distribution shows how sample stats vary. Inference uses samples to estimate population. Standard error measures variability.
46
What is are Sample size formulas?
For means and proportions based on desired confidence level and margin of error.
47
What is Non-response?
Can bias results; needs adjustment techniques like weighting.
48
What can Improve response rates?
Incentives, follow-ups, shorter surveys.
49
What are the Frequencies and distributions?
Measures of location (mean, median, mode), measures of dispersion (range, variance, standard deviation), measures of shape (skewness, kurtosis).
50
What is the Cross-tabulations?
Chi-square test, Phi coefficient, Contingency coefficient, Cramer's V, Lambda.
51
What is Parametric hypothesis testing?
t-tests for one sample, two independent samples, and paired samples.
52
What is One-way ANOVA?
Decomposes total variance into between-group and within-group variance; tests significance of group differences.
53
What is Two-way ANOVA?
Tests main effects and interaction effects between factors.
54
What is ANCOVA?
Controls for extraneous variables by including them as covariates.
55
How do you Interpret results in ANOVA?
Focus on significance of interaction and main effects.
56
What is the Product moment correlation?
Simple linear relationship.
57
What is Partial correlation?
Controlling for other variables.
58
What is Bivariate regression?
Simple regression line; test significance; evaluate prediction accuracy; residual analysis.
59
What is Multiple regression?
More than one predictor; interpret partial regression coefficients.
60
What is Stepwise regression?
Automatic variable selection.
61
What are Dummy variables?
Represent categories numerically.
62
What is Discriminant Analysis?
Classify cases into groups based on predictors.
63
What is Two-group discriminant analysis?
Binary classification.
64
What is Multiple discriminant analysis?
Three or more groups.
65
What is the Binary logit model?
Models probability of binary outcomes; advantage: handles non-normality better than discriminant analysis.
66
What is Factor Analysis?
Reduces data complexity by grouping correlated variables into factors.
67
What is the Factor analysis procedure?
Formulate problem → Correlation matrix → Choose method → Determine number of factors → Rotate → Interpret.
68
What are Surrogate variables?
Use strongest indicators for each factor.
69
What is the Model fit in factor analysis?
Compare observed vs reproduced correlations.
70
What is Cluster Analysis?
Groups similar objects without predefined categories.
71
What is Distance measures in clustering?
e.g., Euclidean, clustering criteria.
72
What is Conjoint Analysis?
Evaluates how consumers value different attributes.
73
What is Multidimensional Scaling (MDS)?
Visualise similarities/differences among brands/products on a map.
74
What are the Steps in MDS?
Formulate problem → Collect similarity/preference data → Select procedure → Decide dimensions → Interpret.
75
What is the Correspondence analysis?
Like MDS but for categorical data; summarises relationships.
76
What is the Conjoint Analysis procedure?
Formulate problem → Construct stimuli → Collect data → Analyse → Interpret → Validate.
77
What is the Cluster Analysis Procedure?
Define problem → Select distance and method → Decide number of clusters → Interpret → Profile.
78
How do you evaluate clustering?
Assess internal cohesion and external separation; test reliability.
79
What are the Discriminant Analysis Procedures?
Formulate problem → Estimate discriminant functions → Test significance → Interpret → Validate model.
80
What is the purpose of ANOVA?
Tests mean differences; regression predicts outcomes.
81
What are the 4Ps of Marketing Mix?
Product, Price, Place, Promotion.
82
What is ANOVA?
Analysis of variance (ANOVA).
83
What is Market research?
Studies markets.
84
What is Marketing research?
Studies the full marketing process.
85
What are the Five stages of the market research process?
Define problem, develop research design, collect data, analyze data, report findings.
86
When is Exploratory research used?
Used when little is known about the problem; to gain insights.
87
What is Secondary data?
Data collected for another purpose but reused for a new study.
88
What are the Advantages of secondary data?
Cost-effective and time-saving.
89
What are Qualitative research methods?
Focus groups and depth interviews.
90
What are the Types of measurement scales?
Nominal, Ordinal, Interval, Ratio.
91
What is Simple random sampling?
Every population member has an equal chance of selection.
92
What is Stratified sampling?
Dividing the population into groups (strata) and sampling from each group.
93
What are the Non-sampling errors?
Measurement error and nonresponse error.
94
What is Non-sampling error?
Error caused by factors other than the sampling method, like survey bias.
95
What is a Good survey question?
Clear, unbiased, asks about one issue only.
96
What are the Factors influencing sample size?
Confidence level, margin of error, variability.
97
What is the Null hypothesis (H₀)?
A statement of no effect or no difference.
98
What is One-sample t-test?
Tests whether a sample mean differs from a known value.
99
What is Chi-square test?
Measures association between categorical variables.
100
What is the purpose of ANOVA?
Tests differences between means of three or more groups.
101
When is Multiple regression used?
Used for predicting an outcome using several predictors.
102
What is Multicollinearity in regression?
Makes it hard to isolate effects of individual predictors.
103
What is the Goal of discriminant analysis?
Classify cases into known groups.
104
What is Cluster analysis?
Groups observations into clusters based on similarity.
105
What is Factor analysis?
Used to reduce a large number of variables into a smaller number of factors.
106
What is Cronbach's alpha?
Measures internal consistency or reliability of a scale.
107
What is Conjoint analysis?
Used for understanding how people value different attributes of a product.
108
What is Multidimensional scaling (MDS)?
Visually represents the similarity between items.
109
What is Correspondence analysis?
A technique for visualising relationships between categorical variables.
110
What is Causality in research?
The idea that one variable directly affects another.
111
What is the Credible interval?
(In Bayesian inference) The probability range where the parameter lies.
112
What is Stepwise regression?
Automated variable selection by adding/removing predictors based on criteria.
113
What is the F-statistic in ANOVA?
The ratio of between-group variance to within-group variance.
114
What is the Scree plot in factor analysis?
Helps decide how many factors to retain by showing eigenvalues.
115
What is Response bias?
When participants answer inaccurately or dishonestly.
116
What is Sample size determined?
Desired confidence level, margin of error, and variability (p).
117
What is the Sample size formula for proportions?
n = (Z² × p(1-p)) / E²
118
What is a t-test?
Testing whether a sample mean differs significantly from a known value or another mean.
119
What is Multicollinearity?
High correlation among independent variables in regression.
120
What is Discriminant analysis?
Classifies observations into predefined groups based on predictor variables.
121
What is Ward's method?
A hierarchical clustering technique minimising within-cluster variance.
122
What is Cronbach's alpha?
Internal consistency (reliability) of a scale.
123
What is a Good value for Cronbach's alpha?
Generally, α ≥ 0.7 is acceptable.
124
What does an Executive summary in a research report contain?
Key findings, conclusions, and recommendations.
125
What does a Good research report contain?
Objectives, methods, findings, conclusions, and actionable recommendations.

Statistical Methods (3 decks)

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