SPSS Core Exam Guide for Fall 2016 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.

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Good to know about the exam: Goals:  The 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 Copyright © 2014 Pearson Education, Inc.

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Using Descriptive Analysis Performing Population Estimates and Testing Hypotheses

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E1: Measures of Variability: Visualizing the Diversity of Respondents

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E2: Describing responses to a question

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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.

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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.

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E3: Inferring the measure of the true population answer towards a certain question given a CI.

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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.

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E4: Testing a hypothesis that the population will give a specific answer or display a parameter

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Implementing Basic Differences Tests

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.

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E5:Testing differences b/n the means of two groups (Independent Samples)

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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.

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E6: Analysis of Variance

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E7: Differences Between Two Means Within the Same Sample (Paired Sample)

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Making Use of Associations Tests

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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 crosstabulation 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.

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E8: Chi-Square Analysis

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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)

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E9: Identifying Correlation

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