Latest issues Volume 35, Issue 2 pp. Volume 35, Issue 1 pp. Volume 34, Issue 4 pp. Volume 34, Issue 3 pp. Find out more About the journal. Articles in press Latest published articles Research article Abstract only A random coefficients mixture hidden Markov model for marketing research. Research article Abstract only Evaluating marketplace synergies of ingredient brand alliances. Research article Abstract only Temporal myopia in sustainable behavior under uncertainty. Research article Open access On the monetary impact of fashion design piracy.
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. To use the output of the chi-square function, one uses a chi-square table. To do so, one needs to know the number of degrees of freedom df. For chi-square applied to cross-tabulated data, the number of degrees of freedom is equal to number of columns - 1 number of rows - 1 This is equal to the number of categories minus one.
The conventional critical level of 0. If the calculated output value from the function is greater than the chi-square look-up table value, the null hypothesis is rejected. If multiple t-tests were applied, the probability of a TYPE I error rejecting a true null hypothesis increases as the number of comparisons increases. The test is called an F-test. ANOVA calculates the ratio of the variation between groups to the variation within groups the F ratio.
Identify the independent and dependent variables. The variation between group means SS between is the total variation minus the in-group variation SS total - SS within.
Measure the difference between each group's mean and the grand mean. Perform a significance test on the differences. This F-test assumes that the group variances are approximately equal and that the observations are independent. It also assumes normally distributed data; however, since this is a test on means the Central Limit Theorem holds as long as the sample size is not too small. ANOVA is efficient for analyzing data using relatively few observations and can be used with categorical variables.
Discriminant Analysis Analysis of the difference in means between groups provides information about individual variables, it is not useful for determine their individual impacts when the variables are used in combination.
Since some variables will not be independent from one another, one needs a test that can consider them simultaneously in order to take into account their interrelationship. One such test is to construct a linear combination, essentially a weighted sum of the variables. To determine which variables discriminate between two or more naturally occurring groups, discriminant analysis is used.
Discriminant analysis can determine which variables are the best predictors of group membership. It determines which groups differ with respect to the mean of a variable, and then uses that variable to predict new cases of group membership.
Essentially, the discriminant function problem is a one-way ANOVA problem in that one can determine whether multiple groups are significantly different from one another with respect to the mean of a particular variable. A discriminant analysis consists of the following steps: Determine the discriminant function coefficients that result in the highest ratio of between-group variation to within-group variation. Test the significance of the discriminant function.
Determine the validity of the analysis. Discriminant analysis analyzes the dependency relationship, whereas factor analysis and cluster analysis address the interdependency among variables.
Factor Analysis Factor analysis is a very popular technique to analyze interdependence. Factor analysis studies the entire set of interrelationships without defining variables to be dependent or independent.
Factor analysis combines variables to create a smaller set of factors. Mathematically, a factor is a linear combination of variables. A factor is not directly observable; it is inferred from the variables. The technique identifies underlying structure among the variables, reducing the number of variables to a more manageable set. Factor analysis groups variables according to their correlation. The Journal will provide food for thought to the marketing managers in public as well as the private sector, consultants, academicians and the students of marketing and other related disciplines.
It will include not only empirical but also conceptual and application oriented papers. JORM introduces peer-review from its first Edition onwards. The researchers submitting their papers for publication should review atleast one technical paper from their domain.
The manuscript also undergoes mandatory procedural review with JORM review and scholar panel. Join as reviewer Journal of Research in Marketing JORM invites interest from competent professionals and academicians to join our review team. Call for Paper - October Edition.
Marketing research helps the marketing manager link the marketing variables with the environment and the consumers. It helps remove some of the uncertainty by providing relevant information about the marketing variables, environment, and consumers.
The International Journal of Research in Marketing is an international, double-blind peer-reviewed journal for marketing academics and practitioners. The International Journal of Research in Marketing is an international, double-blind peer-reviewed journal for marketing academics and practitioners.
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 market and record opinions and other input from consumers regarding interest in the product. Market research provides relevant data to help solve marketing challenges that a business will most likely face--an integral part of the business planning process.
Read the latest articles of International Journal of Research in Marketing at railblogau5.gq, Elsevier’s leading platform of peer-reviewed scholarly literature. It is important for the companies to translate marketing resource allocations and marketing actions’ performance consequences into financial and firm value effects. Research in this area attempts to expand the knowledge on the financial impact of marketing actions and the effect of these strategic actions on shareholder value.