Business Studies Marketing Research Primary Data Collected for specific research activity. Usually used if there is insufficient secondary data available. ADVANTAGES Collected for specific research Data is current Methodology known & controlled Findings not known to competitors No conflicting data DISADVANTAGES Time consuming Expensive Information unavailable Perspective may be limited Secondary Data This is data that has been gathered for some other purpose. It can be internal or external to the company . It has its limitations in as much as it may not completely satisfy the research objective. ADVANTAGES Cheap Readily available Gives other perspectives Offers potentially higher quality data than would otherwise be used Independent source adds to credibility DISADVANTAGES May not be current or complete Too generalised Unknown research methodology Conflicting results Not all results made public giving misleading views Sampling Random sampling Based on the statistical probability that each member of the population as an equal chance of being selected in the sample. It is possible to calculate the level of confidence of the sample, ie., how accurate it will be. Deductive reasoning This means that the logic that exists in a general situation is applied to a specific situation. Inductive reasoning This means that we know everything about any given population and use this information to study the characteristics of a given sample and use this to compare with the known population. Elementary units The population possessing the characteristics we want to investigate. Business Studies Sampling frame The source of elementary units Sample Collection of results Quota sampling This is non random as the researcher has a set number of people to interview. All respondents are selected from a given criteria. Multi stage sampling There are stages to the sampling, in as much as a sample is taken from the original trawl. This is known as a cluster sample. Quantitative and Qualitative Methods Characteristics of populations may be distinguished in two ways, either numerical or by attributes. Nominal data This is a form of coding and does not affect the research itself. It is a form of classification. Ordinal data Used to rank the importance of data. Examples of ordinal scales are: Likert scale- a list of statements with five possible choices for example Strongly agree agree disagree strongly agree Semantic scale - measure differences between words they are usual bipolar, for example, Expensive Poor value Inexpensive Good value Interval data This data scale provides rankings with specific calculations. For example the degrees Celsius on a thermometer. Ratio data This uses basic arithmetic to develop relationships. For example gross profit to sales.