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A special type of cross-sectional analysis is a cohort analysis, which tracks an aggregate of individuals who experience the same event within the same time interval over time. Cohort analyses are useful for long-term forecasting of product demand. Causal research seeks to find cause and effect relationships between variables. It accomplishes this goal through laboratory and field experiments. Secondary Data Before going through the time and expense of collecting primary data, one should check for secondary data that previously may have been collected for other purposes but that can be used in the immediate study.

Secondary data may be internal to the firm, such as sales invoices and warranty cards, or may be external to the firm such as published data or commercially available data. The government census is a valuable source of secondary data. Secondary data has the advantage of saving time and reducing data gathering costs.

The disadvantages are that the data may not fit the problem perfectly and that the accuracy may be more difficult to verify for secondary data than for primary data. Some secondary data is republished by organizations other than the original source. Because errors can occur and important explanations may be missing in republished data, one should obtain secondary data directly from its source. One also should consider who the source is and whether the results may be biased.

There are several criteria that one should use to evaluate secondary data. Whether the data is useful in the research study. Specifications and methodologies used, including data collection method, response rate, quality and analysis of the data, sample size and sampling technique, and questionnaire design. Nature of the data, including definition of variables, units of measure, categories used, and relationships examined.

While useful, intentions are not a reliable indication of actual future behavior. Communication involves questioning respondents either verbally or in writing. This method is versatile, since one needs only to ask for the information; however, the response may not be accurate.

Communication usually is quicker and cheaper than observation. Observation involves the recording of actions and is performed by either a person or some mechanical or electronic device. Observation is less versatile than communication since some attributes of a person may not be readily observable, such as attitudes, awareness, knowledge, intentions, and motivation. Observation also might take longer since observers may have to wait for appropriate events to occur, though observation using scanner data might be quicker and more cost effective.

Observation typically is more accurate than communication. Personal interviews have an interviewer bias that mail-in questionnaires do not have. For example, in a personal interview the respondent's perception of the interviewer may affect the responses. The questionnaire is an important tool for gathering primary data. Poorly constructed questions can result in large errors and invalidate the research data, so significant effort should be put into the questionnaire design.

The questionnaire should be tested thoroughly prior to conducting the survey. Nominal numbers are simply identifiers, with the only permissible mathematical use being for counting. Ordinal scales are used for ranking.

The interval between the numbers conveys no meaning. Median and mode calculations can be performed on ordinal numbers. Interval scales maintain an equal interval between numbers. These scales can be used for ranking and for measuring the interval between two numbers.

Since the zero point is arbitrary, ratios cannot be taken between numbers on an interval scale; however, mean, median, and mode are all valid. Ratio scales are referenced to an absolute zero values, so ratios between numbers on the scale are meaningful. In addition to mean, median, and mode, geometric averages also are valid.

The validity of a test is the extent to which differences in scores reflect differences in the measured characteristic. Predictive validity is a measure of the usefulness of a measuring instrument as a predictor.

Proof of predictive validity is determined by the correlation between results and actual behavior. Construct validity is the extent to which a measuring instrument measures what it intends to measure.

Reliability is the extent to which a measurement is repeatable with the same results. A measurement may be reliable and not valid.

However, if a measurement is valid, then it also is reliable and if it is not reliable, then it cannot be valid. One way to show reliability is to show stability by repeating the test with the same results. Many of the questions in a marketing research survey are designed to measure attitudes.

Attitudes are a person's general evaluation of something. Customer attitude is an important factor for the following reasons:. Self-reporting - subjects are asked directly about their attitudes. Self-reporting is the most common technique used to measure attitude. Observation of behavior - assuming that one's behavior is a result of one's attitudes, attitudes can be inferred by observing behavior.

Performance of objective tasks - assumes that one's performance depends on attitude. For example, the subject can be asked to memorize the arguments of both sides of an issue. Physiological reactions - subject's response to a stimuli is measured using electronic or mechanical means. While the intensity can be measured, it is difficult to know if the attitude is positive or negative.

Multiple measures - a mixture of techniques can be used to validate the findings, especially worthwhile when self-reporting is used. Equal-appearing interval scaling - a set of statements are assembled. These statements are selected according to their position on an interval scale of favorableness. Statements are chosen that has a small degree of dispersion. Respondents then are asked to indicate with which statements they agree. Likert method of summated ratings - a statement is made and the respondents indicate their degree of agreement or disagreement on a five point scale Strongly Disagree, Disagree, Neither Agree Nor Disagree, Agree, Strongly Agree.

Semantic differential scale - a scale is constructed using phrases describing attributes of the product to anchor each end. For example, the left end may state, "Hours are inconvenient" and the right end may state, "Hours are convenient".

Stapel Scale - similar to the semantic differential scale except that 1 points on the scale are identified by numbers, 2 only one statement is used and if the respondent disagrees a negative number should marked, and 3 there are 10 positions instead of seven. This scale does not require that bipolar adjectives be developed and it can be administered by telephone. The sampling frame is the pool from which the interviewees are chosen.

The telephone book often is used as a sampling frame, but have some shortcomings. Telephone books exclude those households that do not have telephones and those households with unlisted numbers. Since a certain percentage of the numbers listed in a phone book are out of service, there are many people who have just moved who are not sampled. Such sampling biases can be overcome by using random digit dialing. Mall intercepts represent another sampling frame, though there are many people who do not shop at malls and those who shop more often will be over-represented unless their answers are weighted in inverse proportion to their frequency of mall shopping.

In designing the research study, one should consider the potential errors. Two sources of errors are random sampling error and non-sampling error. Sampling errors are those due to the fact that there is a non-zero confidence interval of the results because of the sample size being less than the population being studied. Non-sampling errors are those caused by faulty coding, untruthful responses, respondent fatigue, etc.

There is a tradeoff between sample size and cost. The larger the sample size, the smaller the sampling error but the higher the cost. After a certain point the smaller sampling error cannot be justified by the additional cost. While a larger sample size may reduce sampling error, it actually may increase the total error. There are two reasons for this effect. First, a larger sample size may reduce the ability to follow up on non-responses.

Second, even if there is a sufficient number of interviewers for follow-ups, a larger number of interviewers may result in a less uniform interview process. In addition to the intrinsic sampling error, the actual data collection process will introduce additional errors. These errors are called non-sampling errors. Some non-sampling errors may be intentional on the part of the interviewer, who may introduce a bias by leading the respondent to provide a certain response.

The interviewer also may introduce unintentional errors, for example, due to not having a clear understanding of the interview process or due to fatigue. Respondents also may introduce errors. A respondent may introduce intentional errors by lying or simply by not responding to a question.

A respondent may introduce unintentional errors by not understanding the question, guessing, not paying close attention, and being fatigued or distracted. Such non-sampling errors can be reduced through quality control techniques.

Data Analysis - Preliminary Steps Before analysis can be performed, raw data must be transformed into the right format. First, it must be edited so that errors can be corrected or omitted. The data must then be coded; this procedure converts the edited raw data into numbers or symbols. A codebook is created to document how the data was coded. Finally, the data is tabulated to count the number of samples falling into various categories.

Simple tabulations count the occurrences of each variable independently of the other variables. Cross tabulations , also known as contingency tables or cross tabs, treats two or more variables simultaneously.

However, since the variables are in a two-dimensional table, cross tabbing more than two variables is difficult to visualize since more than two dimensions would be required.

Cross tabulation can be performed for nominal and ordinal variables. Cross tabulation is the most commonly utilized data analysis method in marketing research. Many studies take the analysis no further than cross tabulation. This technique divides the sample into sub-groups to show how the dependent variable varies from one subgroup to another.

A third variable can be introduced to uncover a relationship that initially was not evident. Conjoint Analysis The conjoint analysis is a powerful technique for determining consumer preferences for product attributes. Hypothesis Testing A basic fact about testing hypotheses is that a hypothesis may be rejected but that the hypothesis never can be unconditionally accepted until all possible evidence is evaluated.

In the case of sampled data, the information set cannot be complete. So if a test using such data does not reject a hypothesis, the conclusion is not necessarily that the hypothesis should be accepted.

The null hypothesis in an experiment is the hypothesis that the independent variable has no effect on the dependent variable.

The null hypothesis is expressed as H0. This hypothesis is assumed to be true unless proven otherwise. The alternative to the null hypothesis is the hypothesis that the independent variable does have an effect on the dependent variable. This hypothesis is known as the alternative, research, or experimental hypothesis and is expressed as H1. This alternative hypothesis states that the relationship observed between the variables cannot be explained by chance alone.

There are two types of errors in evaluating a hypotheses: Because their names are not very descriptive, these types of errors sometimes are confused. To illustrate the difference, it is useful to consider a trial by jury in which the null hypothesis is that the defendant is innocent.

If the jury convicts a truly innocent defendant, a Type I error has occurred. If, on the other hand, the jury declares a truly guilty defendant to be innocent, a Type II error has occurred.

Hypothesis testing involves the following steps: Formulate the null and alternative hypotheses. Choose the appropriate test. Choose a level of significance alpha - determine the rejection region.

Gather the data and calculate the test statistic. Determine the probability of the observed value of the test statistic under the null hypothesis given the sampling distribution that applies to the chosen test. Compare the value of the test statistic to the rejection threshold. Based on the comparison, reject or do not reject the null hypothesis. Make the marketing research conclusion. In order to analyze whether research results are statistically significant or simply by chance, a test of statistical significance can be run.

Tests of Statistical Significance The chi-square c 2 goodness-of-fit test is used to determine whether a set of proportions have specified numerical values. It often is used to analyze bivariate cross-tabulated data. Some examples of situations that are well-suited for this test are: A manufacturer of packaged products test markets a new product and wants to know if sales of the new product will be in the same relative proportion of package sizes as sales of existing products.

The firm wants to know whether recent fluctuations in these proportions are random or whether they represent a real shift in sales.

The chi-square test is performed by defining k categories and observing the number of cases falling into each category. Knowing the expected number of cases falling in each category, one can define chi-squared as: Before calculating the chi-square value, one needs to determine the expected frequency for each cell. This is done by dividing the number of samples by the number of cells in the table.

These aspects made it imperative for the marketers to conduct marketing research. It helps in identifying new market opportunities for existing and new products. It provides information on market share, nature of competition, customer satisfaction levels, sales performances and channel of distribution. This helps the firms is solving problems. Today, markets are no more local. They have become global.

Manufactures find it difficult to contact customers and control distribution channels. Competition is equally severe. The consumer needs are difficult to predict.

The marketing intelligence provided through marketing research not only helps in framing but also in implementing the market strategies. Marketing has become customer-centric. However, large-scale production needs intermediaries for mass distribution.

Due to prevalence of multi channels of distribution, there is an information gap. Marketing research helps in collecting information on consumers from structured distribution research and helps in making marketing customer oriented. Marketing research is used to analyse and evaluate performances of a company within a market.

It also studies effectiveness of a sales force. Such information helps the companies in identifying areas of shortcoming in sales. It also examines alternative methods for distribution of goods. The most challenging task for any production manager is to keep optimum levels of inventory.

However, production is undertaken in anticipation of demand. Therefore, scientific forecast of sales is required. Marketing research helps in sales forecasting by using market share method, sales force estimate method and jury method.

Marketing research involves gathering and analyzing facts about a given business environment. The research is conducted by both new and existing businesses. A new business may conduct research to better understand the .

Value Market Research combines your need and our expertise to provide you with the best Market Research Reports on worldwide industries. We track the industries and market players diligently to provide you with the latest updated data in our market .

Need and Importance of Marketing Research! The most important task of a marketer is to get the right product at the right place with the right price to the right person. Besides, it was also necessary to go back and find whether consumer is getting optimum satisfaction, so that consumer remains. Marketing research and analytics departments continue to look for efficiencies and ways to stop lower value activity, but they are also reinvesting in building new capabilities. We expect expenditures on primary marketing research to be flat for

Importance of Marketing Research Posted on by admin Research, as a general concept, is the process of gathering information to learn about something that is not fully known. Market research is the process of assessing the viability of a new good or service through research conducted directly with the consumer. This practice allows a company to discover the target.