There are many and varied advanced analysis techniques some of the more common of which are listed below. They may at first appear complicated and potentially intimidating and in recognition of this we have prepared a layman’s guide for each technique. To find out more please click on the technique below.

    * Correlation analysis

    * Maximum Difference Scaling

    * Regression analysis

    * Conjoint analysis

    * Factor analysis

    * Cluster analysis also known as Segmentation analysis

    * CHAID analysis

    * Correspondence Analysis also known as Brand Mapping

By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to: define the research problem; develop and implement a sampling plan; conceptualize, operationalize and test your measures; and develop a design structure. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.

In most social research the data analysis involves three major steps, done in roughly this order:

    * Cleaning and organizing the data for analysis (Data Preparation)

    * Describing the data (Descriptive Statistics)

    * Testing Hypotheses and Models (Inferential Statistics)

Data Preparation involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.

Descriptive Statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data. With descriptive statistics you are simply describing what is, what the data shows.

Inferential Statistics investigate questions, models and hypotheses. In many cases, the conclusions from inferential statistics extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population thinks. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed. The descriptive statistics that you actually look at can be voluminous. In most write-ups, these are carefully selected and organized into summary tables and graphs that only show the most relevant or important information. Usually, the researcher links each of the inferential analyses to specific research questions or hypotheses that were raised in the introduction, or notes any models that were tested that emerged as part of the analysis. In most analysis write-ups it's especially critical to not "miss the forest for the trees." If you present too much detail, the reader may not be able to follow the central line of the results. Often extensive analysis details are appropriately relegated to appendices, reserving only the most critical analysis summaries for the body of the report itself.

Q. What is correlation analysis?

A. Correlation analysis looks at the indirect relationships in survey data

 Q. When would you use it?

A. To objectively establish which variables are most closely associated with a given action or mindset

 Q. What are the advantages?

A. It can provide a more discriminatory analysis than asking a direct question

 Q. Any disadvantages?

A. Has potential shortcomings when dealing with mixed scales

 Q. What is Maximum Difference Scaling?

A. A method of establishing the relative importance or preference of a number of items 

 Q. When would you use it?

A. Maximum Difference Scaling can be used in a range of settings e.g. determining the importance of features of a new product

 Q. What are the advantages?

A. It provides a more discriminatory analysis than asking a direct question especially when a large number (up to 30) of items are to be tested set

 Q. Any disadvantages?

A. Takes slightly longer to complete than direct questioning

 Q. What is regression analysis?

A. Regression is a technique used to predict the value of one variable based on results of one or more other variables

 Q. When would you use it?

A. To work out the simultaneous impact of more than one variable at a time

 Q. What are the advantages?

A. Allows you to work out ‘what if …’ scenarios

 Q. Any disadvantages?

A. Good predictive powers cannot be guaranteed. Interco relation of data can mean that not all variables are included in the regression model. Works best with binary variables (i.e. ‘yes’ / ‘no’ responses)

 Q. What is Conjoint Analysis?

A. In simple terms conjoint analysis (derivation considered jointly) requires respondents to trade off one set of features or attributes against another to determine their relative importance.   

Q. When would you use it?

A. When you need to predict respondent choice when selecting a product or service e.g. selecting a new mobile contract

Q. What are the advantages?

A. Conjoint is ideal if you need to test a complicated multi level product or service

Q. Any disadvantages?

A. Not ideally suited to a telephone fieldwork approach 

Q. What is Factor Analysis?

A. A technique used to examine the relationships among a set of variables in order to identify an underlying structure to those items  

Q. When would you use it?

A. Factor is widely used as an input into other analysis - for example cluster analysis/segmentation analysis.

Q. What are the advantages?

A. It can reduce a large number of variables that would be otherwise be cumbersome, time-consuming or simply impractical to analyse individually to a more manageable smaller number of factors 

Q. Any disadvantages?

A. The reduction of a large number of variables to a small group of factors can lead to some loss of sensitivity in analysis

  Q. What is cluster analysis?

A. Cluster analysis – also known as market segmentation – is a technique that is used to measure market composition

Q. When would you use it?

A. To provide an alternative, more focused profile to what would be possible using basic socio-demographics, or other single-dimensional measures

Q. What are the advantages?

A. It provides a classification that primarily describes the make-up of the market in attitudinal or behavioral terms

Q. Any disadvantages?

A. While members of each cluster group share the same characteristics, each member is not all necessarily identical to every other member

Q What is CHAID?

A CHAID stands for Chi-squared Automatic Interaction Detector. It is a technique that detects interaction between variables.

Q When would you use it?

A  It is used to identify discrete groups of consumer and predict how their responses to some variables affect other variables

Q What are the advantages?

A Highly visual output, no equations.

Q Any disadvantages

A Needs large sample sizes to work effectively

Q. What is correspondence analysis?

A. Correspondence analysis is a diagrammatic means of expressing the relationships between survey variables such as brands and attributes

Q. When would you use it?

A. To visually present brand image data, especially to non-researchers

Q. What are the advantages?

A. Easily to assimilate data and understand relationships, no complicated equations

 Q. Any disadvantages?

A. Maps give only a 2-dimensional representation of the data and should really be read in conjunction with the more detailed statistical analysis that goes into their construction