Chapter 10 - 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). | Testing for differences Chapter 10 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 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 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 . | Testing for differences Chapter 10 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 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 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 Are there significant differences in how cases are distributed across categories of one nominal variable? Significant χ2 indicates that across categories did not occur by chance Does not indicate where the significant variation occurs One dimensional chi-square example Contingency analysis Also known as two-way chi-square or two-dimensional chi-square Examines association between two nominal variables relative to one another Columns represent 1st variable Rows represent 2nd variable Frequency of cases that satisfy conditions of both variables inserted into each cell Contingency analysis example Limitations of chi-square Can only use nominal data variables Test may .
