## Description

**Test Bank For Discovering Statistics Using IBM SPSS Statistics North American Edition 5th Edition By Field**

**Test Bank For Discovering Statistics Using IBM SPSS Statistics North American Edition 5th Edition By Andy Field, ISBN: 9781526436566, ISBN: 9781544328225, ISBN: 9781544355368**

**Table Of Content**

What the hell am I doing here? I don’t belong here

The Research Process

Initial observation: finding something that needs explaining

Generating and testing theories and hypotheses

Collecting data: measurement

Collecting data: research design

Analysing data

Reporting data

What will this chapter tell me?

What is the SPINE of statistics?

Statistical models

Populations and samples

P is for parameters

E is for estimating parameters

S is for standard error

I is for (confidence) interval

N is for null hypothesis significance testing

Reporting significance tests

Problems with NHST

NHST as part of wider problems with science

A phoenix from the EMBERS

Sense, and how to use it

Pre-registering research and open science

Effect size

Bayesian approaches

Reporting effect sizes and Bayes factors

Versions of IBM SPSS Statistics

Windows, Mac OS, and Linux

Getting started

The data editor

Entering data into IBM SPSS Statistics

Importing data

The SPSS viewer

Exporting SPSS output

The syntax editor

Saving files

Opening files

Extending IBM SPSS Statistics

The art of presenting data

The SPSS Chart Builder

Histograms

Boxplots (box-whisker diagrams)

Graphing means: bar charts and error bars

Line charts

Graphing relationships: the scatterplot

Editing graphs

What is bias?

Outliers

Overview of assumptions

Additivity and linearity

Normally distributed something or other

Homoscedasticity/homogeneity of variance

Independence

Spotting outliers

Spotting normality

Spotting linearity and heteroscedasticity/heterogeneity of variance

Reducing bias

When to use non-parametric tests

General procedure of non-parametric tests in SPSS

Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test

Comparing two related conditions: the Wilcoxon signed-rank test

Differences between several independent groups: the Kruskal-Wallis test

Differences between several related groups: Friedman’s ANOVA

Modeling relationships

Data entry for correlation analysis

Bivariate correlation

Partial and semi-partial correlation

Comparaing correlations

Calculating the effect size

How to report correlation coefficents

An introduction to the linear model (regression)

Bias linear models?

Generalizing the model

Sample size and the linear model

Fitting linear models: the general procedure

Using IBM SPPS Statistics to fit a linear model with one predictor

Interpreting a linear model with one predictor

Interpreting a linear model with two or more predictors (multiple regression)

Using IBM SPSS Statistics to fit a linear model with several predictors

Interpreting a linear model with several predictors

Robust regression

Bayesian regression

Reporting linear models

Looking for differences

An example: are invisible people mischievous?

Categorical predictors in the linear model

The t-test

Assumptions of the t-test

Comparaing two means: general procedure

Comparing two independent means using IBM SPSS Statistics

Comparing two related means using IBM SPSS Statistics

Reporting comparisons between two means

Between groups or repeated measures?

The PROCESS tool

Moderation: interactions in the linear model

Mediation

Categorical predictors in regression

Using a linear model to compare several means

Assumptions when comparing means

Planned contrasts (contrast coding)

Post hoc procedures

Comparing several means using IBM SPSS Statistics

Output from one-way independent ANOVA

Robust comparisons of several means

Bayesian comparisons of several means

Calculating the effect size

Reporting results from one-way independent ANOVA

12.15 Smart Alex’s tasks

What is ANCOVA?

ANCOVA and the general linear model

Assumptions and issues in ANCOVA

Conducting ANCOVA using IBM SPSS Statistics

Interpreting ANCOVA

Testing the assumption of homogeneity of regression slopes

Robust ANCOVA

Bayesian analysis with covariates

Calculating the effect size

Reporting results

Factorial designs

Independent factorial designs and the linear model

Model assumptions in factorial designs

Factorial designs using IBM SPSS Statistics

Output from factorial designs

Interpreting interaction graphs

Robust models of factorial designs

Bayesian models of factorial designs

Calculating effect sizes

Reporting results of two-way ANOVA

Introduction to repeated-measures designs

A grubby example

Repeated-measures and the linear model

The ANOVA approach to repeated-measures designs

The F-statistics for repeated-measures designs

Assumptions in repeated-measures designs

One-way repeated-measures designs

Mixed designs

Assumptions in mixed designs

A speed-dating example

Mixed designs using IBM SPSS Statistics

Output for mixed factorial designs

Calculating effect sizes

Reporting the results of mixed designes

Introducing MANOVA

Introducing matrices

The theory behind MANOVA

Practical issues when conducting MANOVA

MANOVA using IBM SPSS Statistics

Interpreting MANOVA

Reporting results from MANOVA

Following up MANOVA with discriminant analysis

Interpreting discriminant analysis

Reporting results from discriminant analysis

The final interpretation

When to use factor analysis

Factors and components

Discovering factors

An anxious example

Factor analysis uisng IBM SPSS Statistics

Interpreting factor analysis

How to report factor analysis

Reliability analysis

Reliability analysis using IBM SPSS Statistics

Interpreting reliability analysis

How to report reliability analysis

Analysing categorical data

Associations between two categorical variables

Associations between several categorical variables: loglinear analysis

Assumptions when analysisng categorical data

General procedure for analysing categorical outcomes

Doing chi-square uisng IBM SPSS Statistics

Interpreting the chi-square test

Loglinear analysis using IBM SPSS Statistics

Interpreting loglinear analysis

Reporting the results of loglinear analysis

What is logitsic regression?

Theory of logistic regression

Sources of bias and common problems

Binary logistic regression

Interpreting logistic regression

Reporting logistic regression

Testing assumptions: another example

Predicting several categories: multinominal logistic regression

Reporting multinominal logistic regression

Hierarchical data

Theory of multilevel linear models

The multilevel model

Some practical issues

Multilevel modeling using IBM SPSS Statistics

Growth models

How to report a multilevel model