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- 1. Unit 3 Experimental Designs and Sampling TOPICS 3.1. Experimentation in Marketing Research 3.2.Classical Designs 3.3. Statistical Designs 3.4. Test Marketing 3.5. Advertising & Sales Promotion Campaigns 3.6. Sampling 3.7. Case Studies
- 2. • Experimentation is widely used in marketing research. • Marketing experiments have been conducted in such diverse activities as evaluating new products, selecting advertising copy themes, determining the frequency of salespeople's calls, and evaluating all aspects of a movie (including ending, pacing, music, and even the story line). • Two general types of experimental designs existnatural and controlled. • A natural experiment is one in which the investigator intervenes only to the extent required for measurement, and there is no deliberate manipulation of an assumed causal variable. • “Nature” produces the changes.
- 3. Prior to 1960 experimentation in marketing research was very seldom. Thereafter there has been a steady growth in this area. A major field where experimentation in marketing is performed happens to be test marketing or market test. A controlled experiment is planned carefully in a selected part of the market place. In most of the cases the objective of such an experiment is to ascertain the probable sale of say a new product. A) Meaning: Experiments are performed to study the effects of the factor levels on the dependent variable. An experimental design is a plan for running an experiment. The factors of an experimental design are variables that have two or more fixed values, or levels. In a conjoint or discrete-choice study, the factors are the attributes of the hypothetical products or services, and the response is preference or choice
- 4. B) Experimental Designs: A number of experimental designs have been developed to overcome and reduce the various sources of invalidity. Experimental designs can be categorized into two broad groups classical and statistical. Classical designs consider the impact of only one dependent variable at a time, whereas statistical designs allow for examining the impact of two or more independent variables. The symbols used in experimental designs are as follows: O = The measurement of a dependent variable. X =The manipulation, or change, of an independent variable. R = Random assignment of subjects (consumers, stores. and so on to experimental and control groups. E = Experimental effect, that is, the change in the dependent variable due to the independent variable.
- 5. The following designs will be used in the discussion of classical experimental designs: 1) After-Only Design: This design consists of measuring the dependent variable after exposing the test unit to the experiment variable. The after-only design is achieved by changing the independent variable and, after some period of time, measuring the dependent variable. It is diagrammed as follows: X O1 where X represents the change in the independent variable and the distance between X and O represents the passage of some time period, O1 represents the measurement, a posttest, of the dependent variable. 2)After-Only With Control Group: This is the simplest of all the controlled experimental designs. In this design, only one treatment is given and then both the experimental and the control groups are measured. Symbolically it can be shown as follows: It has been criticized on the ground that it does not concern itself with the pre-test.
- 6. The following designs will be used in the discussion of classical experimental designs: 3) Before-After Design: The designs considered thus far have been "after-only“ designs, because there were "no- before" measures. Another approach to improving the control is to add a before measure. It is represented symbolically as follows: O1 X O2 The before measure can be added to any design already patented. 4) Before-After With Control Group: This design provides for pre-testing or before measurements. It can be shown symbolically as follows: This design provides for the selection of the experimental and control groups through the random method. The design is able to control most of the sources of systematic error.
- 7. The following designs will be used in the discussion of classical experimental designs: 5 )Four-Group Six Study Design: When the investigator has to obtain data from respondents in an undisguised manner the before-after with control group design such as the preceding one is not suitable, Symbolically, the design can be shown as follows: This is a combination of designs above. The effect of the treatment can be measured in
- 8. Introduction: Statistical designs consist of a series of bask experiments that allow for statistical control and analysis of external variables. In other words several basic experiments are conducted simultaneously. Thus, statistical designs are influenced by the same sources of invalidity that affect the basic designs being used. Statistical designs offer the following advantages: 1) The effects of more than one independent variable can be measured. 2) Specific extraneous variables can be statistically controlled 3) Economical designs can be formulated when each test unit is measured more than once. The most common statistical designs are the randomized block design.
- 9. The Latin square design and the factorial design discussed as below: 1) Randomized Block Design: A randomized block design is useful when there is only one major external variable such as sales, store size or income of the respondent that might influence the dependent variable. The test units are blocked or grouped on the basis of the external variable. The researcher must be able to identify and measure the blocking variable. By blocking, the researcher ensures that the various experimental and control groups are matched closely on the external variable. 2) Latin Square Design: A Latin square design allows the researcher to statistically control two non interacting external variables as well as to manipulate the independent variable. Each external or blocking variable is divided into an equal number of blocks or levels. The independent variable is also divided into the same number of levels. 3) Factorial Design: A factorial design is used to measure the effects of two or more independent variables at various levels. Unlike the randomized block design and the Latin square, factorial design sallow for interactions between variables. An interaction is said to take place when the simultaneous effect of two or more variables is different from the sum of their separate effects.
- 10. Test marketing is a technique used during product development to determine how people respond to a product. It can be used at many different phases of development to see whether or not the public will buy the product, how the product may need to be adjusted to make it appealing to the public, and how members of the public interact with the product. Using information from test marketing, product developers can refine products to make them more commercially viable before embarking on a widespread project launch. A) Applications Related to Test Marketing: Test marketing, also called market testing is an application of a controlled experiment done in limited but carefully selected parts of the marketplace called test markets. The two major objectives of test marketing are: 1) To determine market acceptance of the product, 2) To test alternative levels of marketing mix variables.
- 11. A) Applications Related to Test Marketing: Test-marketing procedures may be classified as Standard test markets, Controlled and Minimarket tests and Simulated test marketing. 1) Standard Test Market: In a standard test market, test markets are selected and the product is sold through regular distribution channels Typically, the company's own sales form is responsible for distributing the product. a) Design: b) Duration: 2) Controlled Test Market: In a controlled test market, the entire test-marketing program is conducted by an outside research company. The research company guarantees distribution of the product in retail outlets that represent predetermined percentage of the market. 3) Simulated Test Market: It is also called a laboratory test or test market simulation simulated test market yields mathematical estimates of market share based on initial reaction of consumers to anew product.
- 12. A) Study of Effectiveness of Advertising: In order to study the effectiveness of advertising with any degree of accuracy it is essential to divide the whole process into various stages. The effectiveness of advertising is assessed at each stage of the advertising campaign. 1) Analysis of Advertising Experience: A continuous analysis of past advertising experience is a very useful first step except incase of new advertisers or products such an analysis can provide a valuable basis for reviewing and developing advertising strategy. 2) Surveys of Buying Behaviour of Consumers: Surveys of buyer behaviour and consumer preferences are helpful in developing advertising objectives and strategy. Such research will also be useful in monitoring changes in the target audience. 3) Pre-Test Research: The third area involves a pre-testing of the advertisements before they are released. Pre- testing is very important as it provides an indication as lo the likely acceptance of the advertisement or campaign by the target market. 4) Post-Test Research The last stage of measuring the effectiveness of an advertisement or advertising campaign is post-test research. Post-test research is concerned with finding out information about how well the advertisement has succeeded in achieving its objective.
- 13. B) Effect on Sales Campaigns: Advertising by itself has no direct impact on the sales. This is because advertising together with many other factors affects the sales of the advertised product. Sales are affected by factors such as expansion of the sales force, improved distribution lessened efforts on the part of the competitors, general improvements in the business conditions and so on. 1) Direct Response by Mall or Telephone Orders: The measurement of the effectiveness of advertising through an increase in Sales is possible when the advertisement is so designed as to complete the entire sales transaction. This is what happens in mail order selling. 2) Controlled Field Experiments: A means for measuring the effectiveness of advertising involves the setting up of a controlled experiment within a limited area. An advertising programme is undertaken in one or more test cities. A tabulation of the sales volume is made for the advertised product in the retail stores of these cities before, during and after the test ads are run. At the same time a check is also kept on the sales in one of the controlled citied where no advertisement campaign is conducted.
- 14. A) Meaning: Sampling is based on the law of statistical regularity and the law of inertia of large numbers. It is a process of selecting a small representative group from the whole for intensive study purpose. A sample is a part of the whole and is representative in character. B) Definition: (a) Crisp R. D: The fundamental idea of sampling is that "of a small number of items or parts (called a sample) are chosen at random from a large number of items or a whole (called a universe or population) the sample will tend to have the same characteristics and to have them in approximately the same proportion as the universe.”
- 15. C) Features of a Good Reliable/Sample:
- 16. C) Features of a Good Reliable/Sample: 1) Goal Oriented: An ideal sample design should be goal oriented. It is a means and should be oriented to the research objectives and fitted to the survey conditions. 2) Accurate Representative of the Universe: A good sample should be an accurate representative of the universe from which it is taken. There are different methods for selecting a sample. A representative sample can be chosen by using any of these methods. The sample will truly be a representative only when it represents all types of units of groups in the total population in fair proportions. 3) Random Selection: An ideal sample should be selected at random. This means that any item in the group has a full and equal chance of being selected and included in the sample. This makes the selected sample truly representative in character.
- 17. C) Features of a Good Reliable/Sample: 4) Proportional: An ideal sample should be proportional. It should be large enough to represent the universe properly. If the total population is larger, the units selected should also be larger. The sample size should be simple i.e. it should be capable of being understood and followed in the field work. 5) Actual Information Provider: An ideal sample should be designed so as to provide actual information required for the study and also provide an adequate basis for the measurement of its own reliability. 6) Economical: A good sample should be economical. The objectives of the survey should be achieved with minimum cost and effort.
- 18. D) Determining Sample Size for Probability and Non-Probability Samples: There are two broad categories of sample designs probability sample and non-probability sample designs. Within these broad categories there are a number of sample designs. The first discussion lays emphasis on probability sample designs. Probability Sampling Non- Probability Sampling
- 19. D) Determining Sample Size for Probability and Non-Probability Samples: a) Probability Samples: In probability sampling methods, the sample units are selected at random. This means the selection is arbitrary. Every member in the universe has equal chance of being selected as the representative. I) Types of Probability Sampling to Determine Sample Size: Types of Probability sampling Random Sampling Systematic Sampling Stratified Random Sampling Disproportionate Random Sampling Cluster Sampling Area Sampling Systematic Random Sampling
- 20. D) Determining Sample Size for Probability and Non-Probability Samples: a) Probability Samples: I) Types of Probability Sampling to Determine Sample Size: 1) Random Sampling: A random sample gives every unit of the population a known and non-zero probability of being selected. Since random sampling implies equal probability to every unit in the population, it is necessary that the selection of the sample must be free from human judgment. 2) Systematic Sampling: In practice, the method followed in systematic sampling is simpler than that explained earlier First, a sampling fraction is calculated. For instance, in the foregoing example, a sample of 50 out of 500 units was chosen. 3) Stratified Random Sampling: A stratified random sample is one where the population is divided into mutually exclusive and mutually exhaustive strata or sub-groups and then a simple random sample is selected within each of the strata or sub-groups.
- 21. D) Determining Sample Size for Probability and Non-Probability Samples: a) Probability Samples: I) Types of Probability Sampling to Determine Sample Size: 4) Disproportionate Random Sampling: The preceding section describes stratified sampling which involves the use of the uniform sampling fraction over different strata of the population. At times, it may be preferable to use variable sampling fractions, resulting in disproportionate stratified sampling. 5) Cluster Sampling: In cluster sampling, the individual units are not selected as sample but are grouped together and are selected group-wise for inclusion in the sample. Thus, groups are selected on random basis as sample. 6) Area Sampling: Area sampling is one aspect of cluster sampling method in which specific area is selected as a sample in place of individual units out of the population. Area sampling is one probability method of selection of sample. 7) Systematic Random Sampling: In systematic random sampling method, the units of a population are first listed and the sample is selected as per well defined system.
- 22. D) Determining Sample Size for Probability and Non-Probability Samples: b) Non-Probability Samples: Non-probability samples are those in which the participants are chosen or choose themselves so that the chance of being selected is not known. The selection may be purposive. It may be based on the convenience or the judgment of the researcher. The selection is deliberate and not random. I) Types of Non-Probability Sampling Methods to Determine Sample Size: Types of non probability sampling Convenience Sampling Judgmental Sampling Quota Sampling
- 23. D) Determining Sample Size for Probability and Non-Probability Samples: b) Non-Probability Samples: I) Types of Non-Probability Sampling Methods to Determine Sample Size: 1) Convenience Sampling: In convenience sampling, the convenience of the researcher is given importance while selecting the sample. Inclusion of units in the sample is decided by the researcher as per his convenience. The items which are easily accessible or easily measurable are included in the sample. 2) Judgmental Sampling: In judgmental sampling method, inclusion of items in the sample is as per the expert judgment given. An expert in the subject of research will be appointed to suggest the units which are best representative of the total population for inclusion in the sample. 3) Quota Sampling: Quota Sampling is similar to stratified random sampling. This non-probability sampling method is commonly used in consumer research surveys. In this method, the universe is sub- divided into identical groups on the basis of certain well defined norms like age, sex, income, education and so on.

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