1 / 17

chapter 6

chapter 6. Becoming Acquainted With Statistical Concepts. Chapter Outline. Why we need statistics Description and inference are not statistical techniques Ways to select a sample Justifying post hoc explanations Measures of central tendency and variability

creighton
Download Presentation

chapter 6

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. chapter6 Becoming Acquainted With Statistical Concepts

  2. Chapter Outline • Why we need statistics • Description and inference are not statistical techniques • Ways to select a sample • Justifying post hoc explanations • Measures of central tendency and variability • Basic concepts of statistical techniques

  3. Why We Need Statistics • Statistics is an objective way of interpreting a collection of observations. • Types of statistics • Descriptive techniques • Correlational techniques (relationship) • Differences among groups (comparison)

  4. Descriptive Statistical Techniques • Measures of Central Tendency • Mean, median, mode • Variability Scores • Standard deviation, range of scores • Frequency Distributions • Distribution of scores including the frequency with which they occur • Frequency Intervals • Small ranges of scores within a frequency distribution into which scores are groups

  5. Correlational Statistical Techniques • Pearson Product Moment Coefficient of Coorelation • Also known as Pearson’s r, interclass correlation, or simple correlation • Measure of the degree of association between two variables • Ranges from -1 to 1 • Indicates the “relationship” between two variables • To be discussed in Chapter 8

  6. Example of Correlation

  7. Comparison Statistical Techniques • t Test • Measure of the difference(s) between two groups • Compare t obtained from your data with a value from a t table • To be discussed in Chapter 9

  8. Description and Inference • Not statistical techniques • Any statistic describes the sample of participants for which it was calculated • If the sample represents some larger group (population), then the findings can be inferred to the larger group ***The statistic has nothing to do with inference***

  9. Ways to Select a Sample • Random sampling (selection) • Tables of random numbers • Stratified random sampling • Categorize population prior to random selection • Systematic sampling • Appropriate for large samples • Random assignment • Groups are formed within a sample

  10. Justifying Post Hoc Explanations • A post hoc explanation may only compare the characteristics measured within the sample • To be able to generalize to the population, random selection is necessary • “Plausible”

  11. Measures of Central Tendencyand Variability • Central tendency scores • Mean: average • Median: midpoint • Mode: most frequent • Variability scores • Standard deviation • Range of scores

  12. Categories of Statistical Tests • Parametric • Normal distribution • Normal curve • Skewness • Kurtosis • Equal variances • Independent observations • Nonparametric (distribution free) • Distribution is not normal

  13. Normal Curve

  14. Skewness

  15. Kurtosis

  16. Statistics • What statistical techniques tell us • Reliability (significance) of effect • Strength of the relationship (meaningfulness) • Types of statistical techniques • Relationships between or among variables • Regression • Correlation • Differences between or among groups • t test • ANOVA

  17. Summary • The type of statistic used does not determine whether the findings can be generalized; rather, it is sampling that permits (or limits) inference • Statistics can do two things: • Establish significance • A relationship or difference is reliable (may expect it to happen again if the study were repeated) • Assess meaningfulness • Importance of the results

More Related