In a pilot survey, thirty (30) respondents were asked to indicate their preference for universities (measured on a 7-point scale where 1=not at all preferred and 7=greatly preferred) and their emphasis on the attributes of job prospects, quality of teaching, location benefits, and reputation (measured on a 7-point scale where 1=not at all important and 7=very important). Results of the pilot survey are presented below:

Hypothesis testing process Problem Definition Clearly state the null and alternative hypotheses Determine the Choose the relevant test degrees of freedom Determine the Decide if one-or two- significance level Choose the critical value tailed test Compute relevant Compare test statistic & critical value test statistic Does the test statistic fall in the critical Do not reject null region? Reject null Draw marketing research conclusionStep 1: Problem definition . Stating the problem confronting the manager and the marketing research problem that the researcher will address Symptoms vs. underlying causes . Research focus and purpose . Research questions . Research objectives . Constructs or variables of interest . Ultimately, your hypotheses!Step 2: Formulate the hypothesis A null hypothesis is a statement of the status quo, one of no difference or effect. If the null hypothesis is not rejected, no changes will be made. - An alternative hypothesis is one in which some difference or effect is expected. Accepting the alternative hypothesis will leac to changes in opinions or actions. - In marketing research, the null hypothesis is formulated in such a way that its rejection leads to the acceptance of the desired conclusion. The alternative hypothesis represents the conclusion for which evidence is sought. E.g., drug A reduces/increases symptom X (H1) vs. no effect (HO) Step 2: Formulate the hypothesis (cont) - The test of the null hypothesis is a one-tailed test, because the alternative hypothesis is expressed directionally (i.e., there is some preferred direction, whether larger or smaller than some predefined value) HO: p s 0.40 (null) H]: p > 0.40 (alternative, one-tailed) Ifthat is not the case, then a two-tailed test would be required, and the hypotheses would be expressed as: HO: (.1 = 0.40 (status-quo, no difference) H]: p i 0.40 (alternative, two-tailed) Step 3: Select an appropriate test Parametric statistical techniques Using parametric data (e.g., interval, ratio) . E.g., t-tests, correlation, regression analysis Non-parametric statistical techniques . Using non-parametric data (e.g., nominal, ordinal) . E.g., cross-tabulations, chi-square testTypes of data and statistical techniques Nominal and ordinal scales Interval and ratio scales Non-metric data (categorical) Metric data (numerical) Non-parametric tests Parametric tests - Frequencies - t-tests - Cross-tabulations - Correlation - Chi-square (hypothesis testing) - RegressionStep 4: Choose a level of signicance Whenever an inference is made about a population, there is a risk that an incorrect conclusion will be reached. Two types of error can occun Type I error occurs when the sample results lead to the rejection 01 the null hypothesis when it is in fact true The probability of Type I error (a) is also called the level of significance which is specified by the researcher Type I error is controlled by establishing the tolerable level of risk (i.e., threshold) of rejecting a null hypothesis (typically at 5%) - If p