Understanding Descriptive & Inferential Stats: Measures, Spreads, Hypothesis Testing, Slides of Public Health

Download Understanding Descriptive & Inferential Stats: Measures, Spreads, Hypothesis Testing and more Slides Public Health in PDF only on Docsity! Research Methods Docsity.com Descriptive Statistics  Allows a description of the information  Statistics: indices are calculated from a sample drawn from a population.  Parameters: drawn from an entire population.  Frequency polygons: A graphical display using a frequency distribution Docsity.com Descriptive Statistics  T-scores: In order to eliminate negative z scores we express them in a different form.  We simply multiply the z score by 10 and add 50. Docsity.com Descriptive Statistics o Scatterplots o Bar graphs o histograms Docsity.com Testing the Hypothesis  Hypothesis testing is a decision-making process. Docsity.com Type I and Type II errors  The probability of making a Type I error – of rejecting the Ho when it is true is called the alpha level. Docsity.com Type I and Type II errors  Apart from making a Type I error, the researchers may mistakenly fail to reject the Ho when, in fact, it is false; this is a Type II error.  In this case, the researcher concludes that the IV does not have an effect when, in fact, it does.  Just as the probability of making a Type I error is called alpha, the probability of making a Type II error is called beta. Docsity.com Type I and Type II errors  To reduce the likelihood of making a Type II error, researchers try to design experiments that have high power.  Power is the probability that a study will correctly reject the Ho when the Ho is false, or the probability that the study will obtain a significant result if the researcher’s experimental hypothesis is in fact true. Docsity.com Inferential Statistics  Inferential statistics are used to draw conclusions about the reliability and generalizability of one’s findings. Docsity.com Inferential Statistics  There are two types of inference techniques that researchers use:  Parametric techniques  Nonparametric techniques Docsity.com Parametric Techniques  T-test: a parametric statistical test used to see whether a difference between the means of two samples is significant. Docsity.com One-Way ANOVA  The simplest form of ANOVA is one- way ANOVA.  It is a statistical test applied to data collected on the basis of a simple randomized Ss design. Docsity.com Two-way ANOVA  The two-way ANOVA is a statistical test that is applied to data collected from a factorial design.  A factorial design is one in which 2 or more IVs are studied simultaneously to determine their independent and interactive effects on the DV. Docsity.com Analysis of Covariance or ANCOVA  Used when groups are not initially equivalent (quasi-experimental).  A pretest (covariate) is used to adjust for pre-existing differences.  In a sense, we subtract out the differences up front. Docsity.com Nonparametric Techniques  Nonparametric techniques involve procedures that dk reference a specific parameter. Docsity.com Commonly used statistical tests Normal theory test Nonparametric test Purpose T test for indep samples Mann-Whitney U test; Compares 2 Wilcoxon rank sum test indep samples Paired t test Wilcoxon matched pairs examines a signed-rank test set of diffs. Pearson Correlation Spearman rank correlation Assess the Coefficient coefficient linear assoc. btwn 2 variables One way ANOVA Kruskal-Wallis ANOVA compares 3 or more groups. Two way ANOVA Friedman 2 way ANOVA compares groups classif by 2 diff. factors Docsity.com The Mann-Whitney U Test Docsity.com The Friedman Two-Way Analysis of Variance Docsity.com Parametric Techniques for analyzing categorical data Docsity.com t-test for proportions Docsity.com