DOC-20240930-WA0002..pdf

MBA- III Marketing Research Session No ( 10 ) Designs of Experimentation...

MBA- III Marketing Research Session No ( 10 ) Designs of Experimentation By Dr. M. Rajkumar Disclaimer: This PPT is the property of the ICFAI University, Jharkhand and can be used only for the educational purpose of the students of the University Table of Contents Experimentation Definition Types of Experimentation Field Experimentation Laboratory Experimentation Factors affecting Validity in Experimentation Learning Objectives After this session the learner will be able to : Understand the basic concepts of experimentation. Get familiar with the different bases for inferring causal relationships. Recognize the principal impediments to valid experimental results Definition and Concepts Independent variables are variables or alternatives that are manipulated and whose effects are measured and compared, e.g., price levels. Test units are individuals, organizations, or other entities whose response to the independent variables or treatments is being examined, e.g., consumers or stores. Definition and Concepts Dependent variables are the variables which measure the effect of the independent variables on the test units, e.g., sales, profits, and market shares. Extraneous variables are all variables other than the independent variables that affect the response of the test units, e.g., store size, store location, and competitive effort. Experimental Design Symbols Designs of Experimentation Pre- Experimental Design True Experimental Design Extension of True Experimental Design Field Experiments Pre-Experimental Designs Do not adequately control for the problems associated with loss of external or internal validity Cannot be classified as true experiments Often used in exploratory research Three Examples of Pre-Experimental Designs One-Shot Design One-Group Pretest-Posttest Design Static Group Design One-Shot Design A single measure is recorded after the treatment is administered Study lacks any comparison or control of extraneous influences No measure of test units not exposed to the experimental treatment May be the only viable choice in taste tests Diagrammed as: X O1 One-Group Pretest-Posttest Design Subjects in the experimental group are measured before and after the treatment is administered. No control group Offers comparison of the same individuals before and after the treatment (e.g., training) If time between 1st & 2nd measurements is extended, may suffer maturation Diagrammed as O1 X O2 Static Group Design Experimental group is measured after being exposed to the experimental treatment Control group is measured without having been exposed to the experimental treatment No pre-measure is taken Major weakness is lack of assurance that the groups were equal on variables of interest prior to the treatment Diagrammed as: Experimental Group X O1 Control Group O2 Pretest-Posttest Control Group Design Experimental group tested before and after treatment exposure Control group tested at same two times without exposure to experimental treatment Includes random assignment to groups Effect of all extraneous variables assumed to be the same on both groups Do run the risk of a testing effect Pretest-Posttest Control Group Design Diagrammed as Effect of the experimental treatment equals (O2 – O1) -- (O4 – O3) Example 20% brand awareness among subjects before an advertising treatment 35% in experimental group & 22% in control group after the treatment Treatment effect equals (0.35 – 0.20) – (0.22 – 0.20) = 13% Posttest-Only Control Group Design Experimental group tested after treatment exposure Control group tested at same time without exposure to experimental treatment Includes random assignment to groups Effect of all extraneous variables assumed to be the same on both groups Do not run the risk of a testing effect Use in situations when cannot pretest Posttest-Only Control Group Design Diagrammed as Effect of the experimental treatment equals (O2 – O1) Example Assume you manufacture an athlete’s foot remedy Want to demonstrate your product is better than the competition Can’t really pretest the effectiveness of the remedy Completely Randomized Design Involves randomly assigning treatments to group members Allows control over all extraneous treatments while manipulating the treatment variable Simple to administer, but should NOT be used unless test members are similar, and they are also alike regarding a particular extraneous variable Different forms of the independent variable are called “levels.” Completely Randomized Design Example Grocery store chain trying to motivate consumers to shop in their stores 3 possible sales promotional efforts X1 = offer discount of 5% off total shopping bill X2 = offer taste sample of selected foods X3 = control group, no sales promotional effort applied Completely Randomized Design Example Randomized Block Design Randomly assigns treatments to experimental & control groups Test units broken into similar blocks (or groups) according to an extraneous variable I.e., location, age, gender, income, education, etc. Particularly useful when small sample sizes are necessary Randomized Block Design Grocery store chain trying to motivate consumers to shop in their stores 3 possible sales promotional efforts X1 =offer discount of 5% off total shopping bill X2 =offer taste sample of selected foods X3 =control group, no sales promotional effort applied Blocks = time stores have been in operation Latin Square Design Allows control or elimination of the effect of two extraneous variables Systematically blocks in 2 directions by grouping test units according to 2 extraneous variables Includes random assignment of treatments to each cell in the design Used for comparing t treatment levels in t rows and t columns I.e., if we have 3 treatment levels, we must have 3 rows and 3 columns Latin Square Design Latin Square Design Factorial Design Used to examine the effects that the manipulation of at least 2 independent variables (simultaneously at different levels) has upon the dependent variable The impact that each independent variable has on the dependent variable is referred to as the main effect Dependent variable may also be impacted by the interaction of the independent variables. This is called the interaction effect Factorial Design Examples Grocery store chain wants to use 12 of its stores to examine whether sales would change at 3 different hours of operation and 2 different types of sales promotions Dependent variable is change in sales Independent variables Store open 6 am to 6 pm Store open 6 am to midnight Store open 24 hours/day Sales promotion: samples for a free gift Sales promotion: food samples Called a 3 x 2 factorial design Need 6 experimental groups (3 x 2 = 6) Factorial Design Examples Summary Experimentation is the research process where one or more 1 variables are manipulated. 2 Experiments can be conducted either in the field or in a laboratory setting Factors affecting the validity are History effect, Maturation, Testing, 3 Instrumentation, Sampling bias, and Mortality. Thank You