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Copyright © 2014 Pearson Education, Inc.12-1 SPSS Core Exam Guide for Spring 2014 The goal of this guide is to: Be a side companion to your study, exercise and exam completion – it is not a substitute for reading through your book chapters Chronologically shortlist different analyses by chapter and Display must have information needed for the running, reading and understanding of the generated outputs.
Copyright © 2014 Pearson Education, Inc.12-2 Good to know about the exam: Goals: This exam is designed to test your ability to work with SPSS, As well as to interpret the results of the analyses If you write out the steps for your analyses you can get partial credit even if you do not get the right answer What can you use to show your understanding: This guide and The textbook (if you do not have notes in it) What you cannot use: Smartphones, mail, internet or help from your peers
Using Descriptive Analysis Performing Population Estimates and Testing Hypotheses Copyright © 2014 Pearson Education, Inc. 3
12-4 E1: Measures of Variability: Visualizing the Diversity of Respondents
Copyright © 2014 Pearson Education, Inc.12-5 E2: Describing responses to a question
Copyright © 2014 Pearson Education, Inc.12-6 Statistical Inference: Sample Statistics and Population Parameters Statistical inference is a set of procedures in which the sample size and sample statistic are used to make an estimate of the corresponding population parameter. Two types of statistical inferences: Parameter estimate is used to approximate the population value (parameter) through the use of confidence intervals. Hypothesis testing is used to compare the sample statistic with what is believed (hypothesized) to be the population value prior to undertaking the study.
Copyright © 2014 Pearson Education, Inc.12-7 Statistical Inference: Sample Statistics and Population Parameters A sample statistic is usually a mean or percentage. Standard error is the measure of variability in the sampling distribution. A confidence interval is the degree of accuracy desired by the researcher stated in the form of a range with an upper and lower boundary. 90% - lie within +/- 1.64 standard deviations 95% - lie within +/- 1.96 standard deviations 99% - lie within +/- 2.58 standard deviations
Copyright © 2014 Pearson Education, Inc.12-8 E3: Inferring the measure of the true population answer towards a certain question given a CI.
Copyright © 2014 Pearson Education, Inc.12-9 Hypothesis Tests Tests of an hypothesized population parameter value: Test of an hypothesis about a percent or a mean The crux of statistical hypothesis testing is the sampling distribution concept. In statistics, the t-statistic is a ratio of the departure of an estimated parameter from its notional value and its standard error. Does not hold if 2-tailed significance < 0.05 and outside +/- 1.96 St.D Holds if 2-tailed significance >/= 0.05 and within +/- 1.96 St.D
Copyright © 2014 Pearson Education, Inc.12-10 E4: Testing a hypothesis that the population will give a specific answer or display a parameter
Implementing Basic Differences Tests
Copyright © 2014 Pearson Education, Inc.12-12 Differences Between Percentages with Two Groups (Independent Samples) Independent samples are treated as representing two potentially different populations. Null hypothesis: the hypothesis that the difference in the population parameters is equal to zero With a differences test, the null hypothesis states that there is no difference between the percentages (or means) being compared. Significance of differences between two percentages - alternative to the null hypothesis is that there is a true difference between the population parameters.
Copyright © 2014 Pearson Education, Inc.12-13 E5:Testing differences b/n the means of two groups (Independent Samples)
Copyright © 2014 Pearson Education, Inc.12-14 ANOVA & Post Hoc Tests: Detect Statistically Significant Differences Among Group Means Analysis of variance (ANOVA): used when comparing the means of three or more groups Post hoc tests: options that are available to determine where the pair(s) of statistically significant differences between the means exist(s) Duncan’s multiple range test: provides output that is mostly a “picture” of what means are significantly different The Duncan multiple range test’s output is much less statistical than most other post hoc tests and is easy to interpret.
Copyright © 2014 Pearson Education, Inc.12-15 E6: Analysis of Variance
Copyright © 2014 Pearson Education, Inc.12-16 E7: Differences Between Two Means Within the Same Sample (Paired Sample)
Making Use of Associations Tests Copyright © 2014 Pearson Education, Inc. 17
Copyright © 2014 Pearson Education, Inc.12-18 The SPSS Chi-Square Analysis! Chi-square analysis: the examination of frequencies for two nominal-scaled variables in a cross-tabulation table to determine whether the variables have a significant relationship Assesses non-monotonic association in a cross-tabulation table based upon differences b/n observed and expected frequencies FOR THIS ANALYSIS! The null hypothesis is that the 2 variables are NOT related/associated! Observed frequencies are the actual cell counts in the cross- tabulation table. FOR THIS ANALYSIS! In order for the Hypothesis to hold we are looking for a P value that is > to 0.05 (it means no association), if it is ≤ to 0.05 it means that there is association
Copyright © 2014 Pearson Education, Inc.12-19 E8: Chi-Square Analysis
Copyright © 2014 Pearson Education, Inc.12-20 The Correlation Coefficient (r) A correlation coefficient’s size indicates the strength of association between two variables. The sign (+ or −) indicates the direction of the association. Regardless of its absolute value, the correlation coefficient must be tested for statistical significance. For this test! the Sig. (2-tailed must be lower than 0.05)
Copyright © 2014 Pearson Education, Inc.12-21 E9: Identifying Correlation
SPSS Session 5: Association between Nominal Variables Using Chi-Square Statistic.
Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 12 l Multiple Regression: Predicting One Factor from Several Others.
Statistics Versus Parameters
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
Chapter 11 Contingency Table Analysis. Nonparametric Systems Another method of examining the relationship between independent (X) and dependant (Y) variables.
Analysis and Interpretation Inferential Statistics ANOVA
Chapter18 Determining and Interpreting Associations Among Variables.
The Simple Regression Model
Chi-square Test of Independence
Chap 9-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 9 Estimation: Additional Topics Statistics for Business and Economics.
Aaker, Kumar, Day Seventh Edition Instructor’s Presentation Slides
Data Analysis Statistics. Levels of Measurement Nominal – Categorical; no implied rankings among the categories. Also includes written observations and.
Summary of Quantitative Analysis Neuman and Robson Ch. 11
Review for Exam 2 Some important themes from Chapters 6-9 Chap. 6. Significance Tests Chap. 7: Comparing Two Groups Chap. 8: Contingency Tables (Categorical.
Simple Linear Regression Analysis
AM Recitation 2/10/11.
Estimation and Hypothesis Testing Faculty of Information Technology King Mongkut’s University of Technology North Bangkok 1.
Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides
Analysis & Interpretation: Individual Variables Independently Chapter 12.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 14 Comparing Groups: Analysis of Variance Methods Section 14.2 Estimating Differences.
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