Chapter 11- Testing for differences. After reading this chapter, you should be able to: Explain the difference between descriptive and inferential statistics, use the four analytical steps to design and evaluate research designs and statistical findings, develop a hypothesis or research question and select the appropriate statistical test of difference (chi-square, t-test, ANOVA),. | Chapter 11 Testing for Differences Differences betweens groups or categories of the independent variable Statistical tests of difference reveal whether the differences observed are greater than differences that might occur by chance Chi-square t-test ANOVA Inferential Statistics Statistical test used to evaluate hypotheses and research questions Results of the sample assumed to hold true for the population if participants are Normally distributed on the dependent variable Randomly assigned to categories of the IV Caveats of application Alternative and Null Hypotheses Inferential statistics test the likelihood that the alternative hypothesis is true and the null hypothesis is not Significance level of .05 is generally the criterion for this decision If p .05, then alternative hypothesis accepted If p > .05, then null hypothesis is retained Degrees of Freedom Represented by df Specifies how many values vary within a statistical test Collecting data always carries error df | Chapter 11 Testing for Differences Differences betweens groups or categories of the independent variable Statistical tests of difference reveal whether the differences observed are greater than differences that might occur by chance Chi-square t-test ANOVA Inferential Statistics Statistical test used to evaluate hypotheses and research questions Results of the sample assumed to hold true for the population if participants are Normally distributed on the dependent variable Randomly assigned to categories of the IV Caveats of application Alternative and Null Hypotheses Inferential statistics test the likelihood that the alternative hypothesis is true and the null hypothesis is not Significance level of .05 is generally the criterion for this decision If p .05, then alternative hypothesis accepted If p > .05, then null hypothesis is retained Degrees of Freedom Represented by df Specifies how many values vary within a statistical test Collecting data always carries error df help account for this error Rules for calculating df for each statistical test Four Analytical Steps Statistical test determines if a difference exists Examine results to determine if the difference found is the one predicted Is the difference significant? Evaluate the process and procedures of collecting data Chi-Square Represented as χ2 Determines if differences among categories are statistically significant Compares the observed frequency with the expected frequency The greater the difference between observed and expected, the larger the χ2 Data must be nominal or categorical One-Dimensional Chi-Square Determines if differences in how cases are distributed across categories of one nominal variable are significant Significant χ2 indicates that variation of frequency across categories did not occur by chance Does not indicate where the significant variation occurs – only that one exists Example of One-Dimensional Chi-Square Contingency Analysis Also known as .
