Eleven Multivariate Analysis Techniques:
Key Tools In Your Marketing Research Survival Kit
Situation 1: A harried executive walks into your office with a stack of printouts. She says, “You’re the marketing research whiz—tell me how many of this new red widget we are going to sell next year. Oh, yeah, we don’t know what price we can get for it either.” Situation 2: Another harried executive (they all seem to be that way) calls you into his office and shows you three proposed advertising campaigns for next year. He asks, “Which one should I use? They all look pretty good to me.” Situation 3: During the annual budget meeting, the sales manager wants to know why two of his main competitors are gaining share. Do they have better widgets? Do their products appeal to different types of customers? What is going on in the market? All of these situations are real, and they happen every day across corporate America. Fortunately, all of these questions are ones to which solid, quantifiable answers can be provided. An astute marketing researcher quickly develops a plan of action to address the situation. The researcher realizes that each question requires a specific type of analysis, and reaches into the analysis tool bag for. . . Over the past 20 years, the dramatic increase in desktop computing power has resulted in a corresponding increase in the availability of computation intensive statistical software. Programs like SAS and SPSS, once restricted to mainframe utilization, are now readily available in Windows-based, menu-driven packages. The marketing research analyst now has access to a much broader array of sophisticated techniques with which to explore the data. The challenge becomes knowing which technique to select, and clearly understanding their strengths and weaknesses. As my father once said to me, “If you only have a hammer, then every problem starts to look like a nail.” Overview
The purpose of this white paper is to provide an executive understanding of 11 multivariate analysis techniques, resulting in an understanding of the appropriate uses for each of the techniques. This is not a discussion of the underlying statistics of each technique; it is a field guide to understanding the types of research questions that can be formulated and the capabilities and limitations of each technique in answering those questions. In order to understand multivariate analysis, it is important to understand some of the terminology. A variate is a weighted combination of variables. The purpose of the analysis is to find the best combination of weights. Nonmetric data refers to data that are either qualitative or categorical in nature. Metric data refers to data that are quantitative, and interval or ratio in nature. Initial Step—Data Quality
Before launching into an analysis technique, it is important to have a clear understanding of the form and quality of the data. The form of the data refers to whether the data are nonmetric or metric. The quality of the data refers to how normally distributed the data are. The first few techniques discussed are sensitive to the linearity, normality, and equal variance assumptions of the data. Examinations of distribution, skewness, and kurtosis are helpful in examining distribution. Also, it is important to understand the magnitude of missing values in observations and to determine whether to ignore them or impute values to the missing observations. Another data quality measure is outliers, and it is important to determine whether the outliers should be removed. If they are kept, they may cause a distortion to the data; if they are eliminated, they may help with the assumptions of normality. The key is to attempt to understand what the outliers represent. Multiple Regression Analysis
Multiple regression is the most commonly utilized multivariate technique. It examines the relationship between a single metric dependent variable and two or more metric independent variables. The...
Please join StudyMode to read the full document