Sampling Methods and Statistical Analysis in Market Research
Sampling Methods in Market Research
Defining the Population and Sample
In market research, the population refers to the entire group of people we want to study. A census surveys everyone in the population and collects data called parameters (e.g., age, gender, income). However, surveying an entire population is often impractical, so researchers use a smaller group called a sample and gather statistics from it.
The sampling frame includes all units that could be sampled. There are two main types of samples:
1) Non-Probability Sampling
In non-probability sampling, the chance of each member being included is unknown, making it impossible to calculate the random sampling error. Common types include:
- Convenience sampling: Surveying the first 10 people you encounter.
- Quota sampling: Ensuring the sample reflects the proportions of groups within the population (e.g., 1/3 Greek / non-Greek).
- Snowball sampling: Asking participants for referrals, suitable for hard-to-reach groups.
2) Probability Sampling
Probability sampling is generally preferred as each member has a known and non-zero chance of being included. Types include:
- Simple Random Sampling: Randomly selecting individuals from the population, giving everyone an equal chance of being chosen.
- Stratified Sampling: Dividing the population by a characteristic (e.g., Greek vs. non-Greek) and using simple random or systematic sampling within each stratum.
- Cluster Sampling: Selecting clusters (e.g., city blocks) that represent the whole population and then randomly sampling members within those clusters.
Determining Sample Size
The ideal sample size balances cost and accuracy. Too few participants lead to a large sampling error, while too many are expensive and unnecessary. Several methods can help determine sample size:
- Formula for Mean Data: n = (z^2(sigma)^2)/H^2, where n is the sample size, z is the z-score for the desired confidence level, sigma is the standard deviation, and H is the desired precision.
- Formula for Proportion Data: n = (z^2(pi(1-pi)))/H^2, where pi is the estimated proportion of the population with the characteristic of interest.
- Heuristic: A rule of thumb, such as 100 subjects per level of a dichotomous variable.
- Dummy Table Considerations: Creating a table of all possible combinations of variables and estimating the number of participants needed per cell.
- Company History/Experience: Using past data to estimate the appropriate sample size.
Data Cleaning and Analysis in SPSS
Cleaning Data
Before analysis, ensure your data is clean. Check for errors, such as missing values or invalid responses. SPSS can help identify and correct these issues. You may need to recode or collapse variables to ensure consistency.
Assessing Reliability
Cronbach’s Alpha measures the internal consistency of a group of items. A higher alpha (closer to 1) indicates greater reliability. SPSS can calculate Cronbach’s Alpha and help identify items that may be lowering the overall reliability.
Correlation Matrix
A correlation matrix shows the correlation coefficients between pairs of variables. This can help identify relationships between variables and potential issues affecting reliability.
Inferential Statistics
Inferential statistics allow us to make inferences about a population based on a sample. The choice of statistical test depends on the level of measurement of the variables:
- Nominal Data: Use Chi-Square to compare frequency counts between groups.
- Ordinal Data: Use Mann-Whitney U test for two groups or Kruskal-Wallis test for more than two groups.
- Interval/Ratio Data: Use t-test for two means or ANOVA for more than two means.
Examples of Statistical Tests
- Chi-Square: Determine if the likelihood of practicing safer sexual activities is dependent on major.
- Mann-Whitney U test: Compare the likelihood of recommending safer sexual practices between males and females.
- Kruskal-Wallis test: Analyze the dependence of recommending safer sexual practices on class standing.
Conclusion
Understanding sampling methods and statistical analysis techniques is crucial for conducting effective market research. By carefully selecting a sample, cleaning data, and choosing appropriate statistical tests, researchers can draw meaningful conclusions about a population and make informed decisions.