In a pilot survey focusing on assessing???den???preferences for universities, thirty (30) respondents were asked to indicate the importance of the criteria of job prospects, quality of teaching, location benefits, and reputation, using a 7-point scale (where 1=not at all important and 7=very important). Results of the pilot survey are presented below:

Group Statistics Type of student N Mean Std. Deviation Std. Error Mean Job prospects Domestic 15 5.40 632 163 International 15 4.40 1.682 434 Quality of teaching Domestic 15 4.80 676 175 International 15 3.93 1.163 300 Location benefits Domestic 15 5.00 .756 .195 International 15 4.93 704 182 Reputation Domestic 15 4.00 845 218 International 15 4.93 704 .182 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Significance F Mean Std. Error Difference Sig. df One-Sided p Two-Sided p Difference Difference Lower Upper Job prospects Equal variances assumed 7.865 009 2.155 28 020 040 1.000 464 050 Equal variances not 1.950 2.155 17.882 023 assumed 045 1.000 464 025 1.975 Quality of teaching Equal variances assumed 2.670 113 2.495 28 .009 019 .867 347 .155 Equal variances not 1.578 2.495 22.494 010 020 assumed 867 347 147 1.586 Location benefits Equal variances assumed 038 846 250 28 402 .804 067 .267 Equal variances not .480 613 250 27.858 402 804 assumed .067 267 -.480 613 Reputation Equal variances assumed 030 864 -3.287 28 001 003 -.933 284 Equal variances not -1.515 .352 -3.287 27.111 001 assumed 003 -.933 .284 -1.516 .351Hypothesis 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