Week 7

• Sampling

1. Role in research process

• Def: Selecting a small number of participants from a larger group and generalising the result.

• Used when it’s unreasonable to do a census.

• Less time-consuming and cheap than census

• Plays an indirect role in designing questionnaires

2. Distinguish between non-probability and probability sampling

• Probability:

• Each sampling unit has the same probability of being picked.

• Unbiased sample.

• Proper sample representation.

• Generalisable results.

• Simple Random sampling:

• Every sampling unit has equal chance

• Systematic Random

• Organised into a list and skipping a certain number every time.

• Stratified sampling:

• Separates the target population into groups and uses random sampling to create a new single sample

• Cluster sampling:

• Divides groups into clusters, then uses random sampling.

• Non-Probability

• Probability of selecting sampling unit is unknown

• Sampling error is not known

• Selection based on researcher and may/may not be representative of target population

• Convenience:

• Draws sample based on convenience of researcher

• Judgement/purposive sampling:

• Selected because they fit a criteria set by researchers.

• Quota:

• Selection based on pre specified quotas for: eg demographics, behaviours, attitudes.

• Snowball:

• Participants are selected by researchers and then they refer people for the study.

3. Explain the factors determining sample size

• Based on:

• Research objective: eg qualitative/quantitative

• Degree of accuracy: Insights or inferences

• Resources: Budget?

• Time frame: When does it need to be done by?

• Knowledge of target population: Are there lists? How easy is it to develop a sampling frame?

• Scope of research: local, international etc

• Statistical Analysis needed: Hypothesis testing or projection

• Probability:

• Population standard deviation

• Level of Confidence desired

• Degree of precision (acceptable amount of error)

• Non-probability:

• Not be used for statistical inferences

• Determined by budget and previous studies

• Made on initiative decision of researchers

4. Sampling plan steps: ensure data represents population

• Step 1: Define target population

• Step 2: Select data collection method

• Step 3: Identify sampling frames needed

• Step 4: Select appropriate sampling methods

• Step 5: Determine necessary sample sizes

• Step 6: Creating a plan for selecting sampling units

• Step 7: Execute this plan

• Scales/Measurement

1. Role of measurement in marketing research

• Applies to abstract things such as people’s preferences and accurate measurements are essential to effective decision making.

• Measurement process consists of two steps

• Construct selection/development

• Determine what specific data solves research problem, pick out most relevant objects

• Done by sorting out objective and subjective properties of each object

• Scale measurement (how to measure each construct)

• Assigning set of descriptors to represent possible responses

• Combination of labels that determine degree of intensity (SCALE POINTS) eg strongly agree/disagree or 1-7

2. Explain the four basic levels of scales

• Nominal

• Uses labels to classify into groups

• Frequency distributions and mode

• Ordinal

• Ranked via preference

• Mean, mode, frequency distributions and scales can be applied.

• Interval

• Demonstrates absolute differences between each scale point

• Means and standard deviation

• Ratio

• Allows researcher to identify absolute differences but also make comparisons

• Enables true natural zero

• Means and standard deviation

3. Describe scale development and its importance in gathering primary data

• Designing a scale requires

• Understanding research problem

• Identifying / developing constructs

• Establishing detailed data requirements

• Understanding scale properties

• Selecting appropriate measure scale

• discriminatory power of scale descriptors is the scale’s ability to differentiate between the scale responses (more scale points = greater disciminiatory power)

• Balanced scale = just as many positive and negative responses

• Forced choice = no neutral descriptor

• 3 main scales to use

• Likert Strongly Agree/Disagree

• Semantic differential scale is a unique bipolar ordinal scale format that captures a person’s attitudes or feelings on a given object

• Behavioural intention probability/predictability

4. Discuss comparative and noncomparative scales.

• Comparative

• Discusses feelings/attitudes on the basis of another object

• Rank order scales

• Constant sum scales

• Non-comparative

• Discusses feelings of a singular object

Week 8

• Data prep/analysis/results

1. Describe process for data preparation and analysis

• Accurate data that helps with decision-making

• Step 1: Validation

• Surveys were done correctly and without bias

• Measurement bias

• Respondent bias

• Researcher bias

• Step 2: Editing

• Raw data is checked for mistakes

• 4 main concerns

• Asking proper questions

• Accurate recording of responses

• Correct screening of respondents

• Recording of open ended questions

• Step 3: Coding

• Involves grouping and assigning values to various responses

• 4 step method

• Generate list of as many responses as possible

• Consolidate responses

• Assign a numerical value as a code

• Assign a coded value to each response

• Step 4: Data entry

• Entering data into a file for analysis

• Error detection identifies errors from entry

• Errors can be identified through error edit routines

• Missing Values

• Data tabulation

• One way

• Shows responses for a single variable

• Descriptive statistic (Summary)

• Can spot missing values

• Two way tab

• Two or more variables

• Most commonly used with ordinal and nominal data

• Descriptive stat

Week 9

• Basic Data Analysis for Quantitative data

1. Explain basic tendencies and dispersion

• Central tendencies

• Describes a set of data by identifying the central position

• Mean (average) : Best for Interval/ratio variable (NOT SKEWED)

• Median (Middle number) : Best for ordinal (preference) and Interval/ratio skewed

• Mode (Most frequent) : Best for nominal (categorical)

• Dispersion

• Describes extent of variability

• Range, Interquartile range, Standard Deviation, Variance

• Standard deviation (average distance of distribution variables from the mean) for symmetrical numerical data (mean is middle)

• Ordinal or Skewed data median and interquartile range

2. Describe how to test hypotheses using univariate and bivariate statistics

• Form hypothesis regarding population characteristics. First you do frequency distributions and average etc. Then you test hypothesis

1. If you test one variable at the time it's a univariate test.  You test the variable against for example a mean.

• Null Hypothesis (H0 ): The null hypothesis is no difference in the group means

• Or Alternate Hypothesis (H1 ): Opposite of null hypothesis

• Significance level normally set at 0.05 (confidence level = 95%)

2. Bivariate tests: Testing two variables, how one variable influences another variable. (each sample is independent)

• Cross Tabs

• A frequency distribution of responses on two or more sets of variables

• To conduct cross tabulation, the responses for each of the groups are tabulated and compared

• Chi-square test

• Analyses nominal or ordinal data

• Enables analysis of statistical significance between frequency distributions between 2 or more variables

• How actual frequencies fit expected frequencies

• Null = no difference

• Alternative difference

• If level of confidence is lower than 0.05 we can reject null

• Only larger sample sizes

• Independent t-test

• Statistical difference between 2 means/interventions/change scores

• Dependent variable that is continuous eg interval/ratio

• Independent variable that is categorical eg gender

• Assumption: Normal distribution, no outliers (check with box plot), random sample, no relationship between samples.

• Paired T-test

• Determines if difference between means is 0

• Null hypothesis true mean difference = zero

• Assumptions

• dependent variable is continuous (interval/ratio), normally distributed and no outliers

• Variables have to be independent of one another.

• ANOVA

• Statistical difference between 3 or more means

• One-way anova the comparison involves means but with only one independent variable

• F-test tells us if tests are statistically significant

• Total variance = set of responses is made up between group and within group variance

• between-group variance measures how much the sample means of the groups differ from one another

• within-group variance measures how much responses within each group differ from one another.

• Does not identify which pairs of means are different

• N-way Anova

• Analyse several independent variables at the same time

• Multiple independent variables act together to affect the dependent variable group means (interaction effect)

• Used for experimental design

• Perceptual mapping

• Graphic representation from analysis

• Perception based of other brands in comparison to key attributes

• Parametric tests

• Normal distribution

• Interval/ratio data

• Results affected outliers

• More statistical power

• T-Test, Anova, Regression,

Week 10

1. Evaluate the types of relationships between variables

• Strength of association (weak, moderate, strong)

• Direction (positive or negative)

• Presence (systematic and consistency)

• Type of relationship (Linear and nonlinear)

• Linear: Changes in one variable changes the second variable

• Curvilinear: Increases to a certain extent then starts to decrease (curve)

2. Explain the concepts of association and covariation

• Association: Numerical measure of strength between relationships

• Covariation: Amount of change in one variable that consistently changes in another variable.

3. Discuss the differences between Pearson correlation and Spearman correlation

• Pearson correlation:

• Statistical measure of strength between two metric variables.

• Varies between -1 and 1 (0 is no correlation)

• Null hypothesis : is no association

• 0.81-1 = strong

• 0.00-0.20 = weak

• May be a non-consistent (nonlinear) relationship

• Significance level must be calculated: might falsely reject null

• Interval/ratio, normally distributed, linear relationship

• Spearman rank order

• Two variables used ordinal scales

• Either one is ordinal

4. Explain the concept of statistical vs. practical significance

• Statistical:

• Statistical significance is used to examine the coefficient for multiple regression

• Not all of them are statistically significant

• Because some of the procedures involved in determining the statistical significance of a statistical test include consideration of the sample size, it is possible to have a very low degree of association between two variables show up as statistically significant

• However, by considering the absolute strength of the relationship in addition to its statistical significance, the researcher is better able to draw the appropriate conclusion about the data and the population from which they were selected.

5. Explain when and how to use regression analysis

• Estimates relationship between one dependent variable and one or more independent variables

• Which of the variables have an impact?

• Most important variables

• How they interact with each other

• How certain are we about the variables

• Any point not on the line is unexplained variance

• Bivariate analysis focuses on one dependent and one independent variable

• Multiple regression analysis has multiple independent variables

• Each variable has a separate regression coefficient is calculated which describes its relationship with the dependent variable

• Linear relationship, Homoscedasticity and normal distribution

Week 11

1. List the objectives of a research report

• Clear concise interpretation (logical explanation, make complex information easy to understand) of project

• Accurate, easy to understand, credible (create believability through good quality and organisation)

• Guide future research and serve as an information source

• Logical recommendations

2. Describe the format of a marketing research report

• Title page

• table of contents

• Exec summary, introduction

• Research Method and procedures (research design, sampling, sampling process, primary data and secondary data)

• Data analysis and findings (the body, tables/figures/graphs,

• conclusions and recommendations

• Limitations

• appendix

3. Discuss the techniques of graphically displaying research results

• Frequencies: bar chart, pie chart or table

• Several thematic related variables can be presented in same table

• Bar charts are used to compare groups

• ANOVA/t-tests be presented in tables (include correlations)

• Regression analysis displays predictor and outcome with arrows

4. Address the problems encountered in preparing reports

• Lack of data interpretation

• Unnecessary use of complex statistics

• Lack of relevance

• Placing too much emphasis on few statistics

5. Appreciate the significance of presentations in marketing research

• Helps to make good decisions

• Seen by only those commissioning the report

• Seniors rely on short, concise presentations