Correlational Research

                          Correlational Research

     Written by Ari Julianto

          Today posting discusses about correlational research. From the word correlation, we could understand the meaning of it. Most research falls into one of two categories: correlational and experimental.
      Understanding the differences between these two types of research is one of the major goals of any introductory research methods course.Correlational research tests for statistical relationships between variables.
      The researcher begins with the idea that there might be a relationship between two variables.  She or he then measures both variables for each of a large number of cases and checks to see if they are in fact related.  The relationship of interest could be either a D relationship or an R relationship, so this might involve making a bar graph and computing d or making a line graph or scatterplot and computingr.  It probably also involves null hypothesis testing to see if the observed relationship is statistically significant.
      Here the researcher focuses on naturally occurring patterns,measures specific variables, and generates statistics for clarification:
a. Focus on naturally occurring patterns: Complex real-world situations are the basis of the investigation. The idea is to clarify these through pattern-making,
b. Measurement of specific variables: the researcher simply measures the variables of interest and analyzes relations among them. These are always variables that can be measured and quantified in some way (the data is quantitative; as ‘solid’ as measurements or object counts, or more ‘abstract’, including people’s attitudes,meaning-making, or perceptions).

Two Types of Correlational Research:
a. Relationship: Here the specific focus is the predictive power of relationships between variables. Here the researcher knows what variable he/she wants to focus on, and then these are applied in some context to see how they relate. 
b. Causal-Comparative: this is an ‘intermediate’ position between the predictive orientation of relationship studies and the focus on causality  that characterizes experimental research. Here the purpose is to isolate factors that cause differences in variables between contexts. Here the first step is to identify a group of particular contexts. Variables are then selected as they seem to vary from context to context.

Data-gathering methods:
a. Survey questionnaire: This is perhaps the most frequently employed tool.It allows the researcher to cover an extensive amount of information across a large number of people in a limited amount of time. 
b. Observation: Direct or indirect. Indirect observation (videotape, time-lapse photography, etc.)
is useful in order to catch all the details). 
c. Mapping: The ways in which people map or draw particular data allows researchers to derive
personalized patterns. 
d. Sorting: This is a great method for generating a creative foundation to a project. Very useful
for establishing patterns between a client and a designer in a design project (it is a very effective
alternative to simply asking people to state their preferences). 
e. Archives: For inanimate objects or ‘removed’ individuals, researchers establish patterns from  archived information. 
f. Factor Analysis: Also for interval-ratio data. Instead of  using key variables to predict the outcomes of other variables, factor analysis aims to articulate an overall pattern. Variables that share similar patterns are grouped into clusters known as ‘factors’. 
g. Multidimensional Scaling: Useful for nominal or interval-ratio data. This process creates a graphic plot that locates relationships in a spatial manner (variables are plotted as points; points plotted in close proximity represent similar patterns, while distant points represent dissimilar patterns).

In short, Correlational research strategy seeks to clarify patterns of relationships between 2 or more variables.