During a study, there are often questions that strike the researcher that must be answered. These questions include questions like ‘are the groups different?’, ‘on what variables, are the groups most different?’, ‘can one predict which group a person belongs to using such variables?’ etc. In answering such questions, discriminant analysis is quite helpful.
Statistics Solutions is the country’s leader in discriminant analysis and dissertation statistics. Use the calendar below to schedule a free 30-minute consultation.
Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is interval in nature. The term categorical variable means that the dependent variable is divided into a number of categories. For example, three brands of computers, Computer A, Computer B and Computer C can be the categorical dependent variable.
The objective of discriminant analysis is to develop discriminant functions that are nothing but the linear combination of independent variables that will discriminate between the categories of the dependent variable in a perfect manner. It enables the researcher to examine whether significant differences exist among the groups, in terms of the predictor variables. It also evaluates the accuracy of the classification.
Discriminant analysis is described by the number of categories that is possessed by the dependent variable.
As in statistics, everything is assumed up until infinity, so in this case, when the dependent variable has two categories, then the type used is two-group discriminant analysis. If the dependent variable has three or more than three categories, then the type used is multiple discriminant analysis. The major distinction to the types of discriminant analysis is that for a two group, it is possible to derive only one discriminant function. On the other hand, in the case of multiple discriminant analysis, more than one discriminant function can be computed.
There are many examples that can explain when discriminant analysis fits. It can be used to know whether heavy, medium and light users of soft drinks are different in terms of their consumption of frozen foods. In the field of psychology, it can be used to differentiate between the price sensitive and non price sensitive buyers of groceries in terms of their psychological attributes or characteristics. In the field of business, it can be used to understand the characteristics or the attributes of a customer possessing store loyalty and a customer who does not have store loyalty.
Intellectus allows you to conduct and interpret your analysis in minutes. Click the link below to create a free account, and get started analyzing your data now!
For a researcher, it is important to understand the relationship of discriminant analysis with Regression and Analysis of Variance (ANOVA) which has many similarities and differences. Often we can find similarities and differences with the people we come across. Similarly, there are some similarities and differences with discriminant analysis along with two other procedures. The similarity is that the number of dependent variables is one in discriminant analysis and in the other two procedures, the number of independent variables are multiple in discriminant analysis. The difference is categorical or binary in discriminant analysis, but metric in the other two procedures. The nature of the independent variables is categorical in Analysis of Variance (ANOVA), but metric in regression and discriminant analysis.
The steps involved in conducting discriminant analysis are as follows:
• The problem is formulated before conducting.
• The discriminant function coefficients are estimated.
• The next step is the determination of the significance of these discriminant functions.
• One must interpret the results obtained.
• The last and the most important step is to assess the validity.
Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:
Edit your research questions and null/alternative hypotheses
Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references
Justify your sample size/power analysis, provide references
Explain your data analysis plan to you so you are comfortable and confident
Two hours of additional support with your statistician
Clean and code dataset
Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate)
Conduct analyses to examine each of your research questions
Provide APA 6th edition tables and figures
Explain chapter 4 findings
Ongoing support for entire results chapter statistics
Please call 727-442-4290 to request a quote based on the specifics of your research, schedule using the calendar on t his page, or email [email protected]