Significance Testing (Statistical Associates Blue Book Series 18)

Significance Testing (Statistical Associates Blue Book Series 18)

Significance Testing: Parametric and Nonparametric<br /><br />A statistical significance coefficient is the chance that a relationship as strong or stronger than the one observed was due to the chance of random sampling. Thus if a correlation coefficient is significant at exactly the .05 level, this means there is 5% chance that a correlation as strong or stronger than the observed one would result from an unusual random sampling of data when in fact the correlation was zero. There are many, many specific significance tests. Common tests are listed below, but in addition each statistical procedure has associated significance tests which are discussed in the respective Statistical Associates "Blue Book" volumes dealing with each procedure.<br /><br />The full content is now available from Statistical Associates Publishers: http://www.statisticalassociates.com. <br /><br />Below is the unformatted table of contents.<br /><br />Table of Contents<br /><br />Significance Testing15<br />Overview15<br />Types of significance tests15<br />Parametric tests15<br />Key Concepts and Terms16<br />When significance testing applies16<br />Significance and Type I Errors19<br />Confidence limits19<br />Power and Type II Errors20<br />One-tailed vs. two-tailed tests20<br />Assumptions of significance testing in general22<br />Random sampling22<br />Adequate sample size22<br />Significance is not importance22<br />À priori testing23<br />Appropriate alpha significance level23<br />Absence of intervening and common anteceding causes23<br />Frequently asked questions about significance testing in general23<br />How is significance related to effect size?24<br />Should I "fail to accept" or should I "reject" the null hypothesis?24<br />If cases in my sample are weighted, am I getting accurate significance values?24<br />Is significance the same for multistage random samples as for simple random samples?24<br />Binomial Test of Significance26<br />Overview26<br />Key Concepts and Terms26<br />Implementing the binomial test in SPSS27<br />Selections27<br />Normal approximation of the binomial test30<br />Assumptions of the binomial test31<br />Dichotomous distribution31<br />Data distribution31<br />Random sampling31<br />Student's t-Test of Difference of Means32<br />Overview of the t-test32<br />SPSS t-test types32<br />Key Concepts and Terms33<br />Formula33<br />Critical value33<br />Confidence limits34<br />One-sample t-test34<br />Example34<br />Interpretation35<br />Independent sample t-test35<br />Example36<br />Interpretation (with Levene's test)36<br />The independent samples assumption37<br />Paired sample t-test for non-independent samples37<br />Overview37<br />Example38<br />Interpretation38<br />Assumptions for t-tests39<br />Normal distribution39<br />Random sampling39<br />Similar variances39<br />Dependent/independent samples40<br />Effect size measures40<br />Frequently Asked Questions40<br />What are common alternatives to the t-test?40<br />What non-parametric test do I use instead of the t-test if my data cannot meet the assumption of normality?41<br />Normal Curve Tests of Means and Proportions42<br />Overview42<br />Key Concepts and Terms42<br />Deviation scores42<br />Standard deviation42<br />Variance43<br />Standard error43<br />Confidence limits43<br />Binomial distribution44<br />Normal distribution44<br />Normal curve means tests ("hypothesis tests")46<br />Confidence interval47<br />Manual computation of z values for normal curve tests49<br />Assumptions for normal curve tests50<br />Interval data51<br />Sample size should not be small51<br />Homogeneity of variances51<br />Random sampling51<br />Chi-Square Significance Tests52<br />Overview52<br />Pearson's chi-square52<br />Overview52<br />SPSS output53<br />Yates correction for continuity56<br />Crosstabulation control variables and chi-square57<br />Chi-square goodness-of-fit test58<br />Likelihood ratio chi-square61<br />Mantel-Haenszel (linear by linear) chi-square62<br />Assumptions for chi-square tests63<br />Random sampling63<br />Independence64<br />Known distribution64<br />Non-directional hypotheses64<br />Finite values64<br />Normal distribution of deviations64<br />Data level64<br />Frequently asked questions about chi-square65<br />and 165 more pages of topics on significance testing.