Intermediate Statistics

Muito boas aulas e slides sobre ANOVA, ANCOVA e regressão

Muito boas aulas e slides sobre ANOVA, ANCOVA e regressão

Welcome to the wood of suicides (is anyone getting all these Dante references or am I just wasting my time here?), where self flagellation is the name of the game. You will experience the bowel-evacuating effect of multiple regression, the bone-splintering power of ANOVA and the nose-hair pulling torment of factor analysis. Can you cope: I think not, mortal filth. Be warned, your brain will be placed in a jar of cerebral fluid and I will toy with it at my leisure.

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PANDA – Practical Analysis of Nutritional Data

Bom livro sobre análise de dados e testes estatísticos

Bom livro sobre análise de dados e testes estatísticos


Chapter 1 –
USING NUTRITIONAL DATA
Chapter 2 –
DATA CLEANING

Chapter 3 –
ONE-WAY ANALYSIS

Chapter 4 –
TWO-WAY ANALYSIS

Chapter 5 –
MULTI-WAY ANALYSIS

Chapter 6 –
SUBMODULES

Chapter 7 – ASSESS YOUR UNDERSTANDING
Chapter 8
KENYA REAL WORLD ANALYSIS

Building and presenting a situation analysis
Child Feeding Practices

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Determining the Number of Components and Factors Using Parallel Analysis and Velicer’s MAP Test

Softwere para análise do nº de componentes em ACP e AF

Softwere para análise do nº de componentes em ACP e AF

Popular statistical software packages do not have the proper procedures for determining the number of components or factors in correlation matrices. Parallel analysis and Velicer’s minimum average partial (MAP) test are validated procedures that are widely recommended by statisticians. This paper described brief and efficient programs for conducting parallel analyses and the MAP test using SPSS, SAS, and MATLAB.

Métodos para facilitar a interpretação da AFE

Métodos para facilitar a interpretação da AFE

Scale development using popular statistical software packages often produces results that are baffling or misunderstood by many users, which can lead to inappropriate substantive interpretations and item selection decisions. High internal consistencies do not indicate unidimensionality; item-total correlations are inflated because each item is correlated with its own error as well as the common variance among items; and the default number-of-eigenvalues-greater-than-one rule, followed by principal components analysis and varimax rotation, produces inflated loadings and the possible appearance of numerous uncorrelated factors for items that measure the same construct (Gorsuch, 1997a, 1997b). Concerned investigators may then neglect the higher order general factor in their data as they use misleading statistical output to trim items and fashion unidimensional scales.

These problems can be circumvented in exploratory factor analysis by using more appropriate factor analytic procedures and by using extension analysis as the basis for adding items to scales. Extension analysis provides correlations between nonfactored items and the factors that exist in a set of core items. The extension item correlations are then used to decide which factor, if any, a prospective item belongs to. The decisions are unbiased because factors are defined without being influenced by the extension items. One can also examine correlations between extension items and any higher order factor(s) in the core items. The end result is a comprehensive, undisturbed, and informative picture of the correlational structure that exists in a set of core items and of the potential contribution and location of additional items to the structure.

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SPSS Programs for Analyzing Lag-Sequential Categorical Data

Programas em SPSS e SAS para análise de séries categóricas

Programas em SPSS e SAS para análise de séries categóricas

This paper describes simple and flexible programs for conducting lag sequential event analyses using SAS and SPSS.  The programs read a stream of codes and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule’s Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests.

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Logistic regression and discriminant analysis

Bom texto sobre regressão logística e análise descriminante com SPSS

Bom texto sobre regressão logística e análise discriminante com SPSS

Contents of this handout: The problem of dichotomous dependent variables; Discriminant analysis; Logistic regression – theory; Logistic regression (and discriminant analysis) in practice; Interpreting and reporting logistic regression results; References and further reading; Examples.

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Analysis Of Categorical Dependent Variables

Nétodos para VD categóricas (modelos logísticos)

Nétodos para VD categóricas (modelos logísticos)

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Statistics on Likert Scale Surveys

Discute as análises de dados legítimas em escalas ordinais

Discute as análises de dados legítimas em escalas ordinais

Disclaimer

What is a Likert Scale?

Reading in the survey from optical mark scanning sheet data

Producing a Table of the frequency results

Producing Means and Standard Deviations

Using T-Tests to compare groups

About the assumptions of the T-Test

An Alternative to T-Tests: the Chi Square Statistic

T-Tests on pairs of questions

Computing Subscales and what to do about reverse wording

Statistics on subscales

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SPSS Course Content

Elementos de um curso de SPSS básico de 2000

Elementos de um curso de SPSS básico de 2000

Descriptive Statistics
Frequencies Descriptives Explore: Statistics and Charts

Statistical Notes
Nominal, Ordinal, Interval, Ratio 95% Confidence Intervals

Data Transformations

compute recode

File Transformations

Merge File - Add Cases

Crosstabulation and Measures of Association

Measures for Nominal Data Measures for Ordinal Data Interrater Agreement

Differences Between Two Groups

t test nonparametric tests

ANOVA: Between Subjects

—————————–General Linear Model (GLM)—————————

Design: A Design: A x B Design: A x B x C Simple Main Effects Studies with unequal ns

ANOVA: Repeated Measures

———General Linear Model (GLM)———

1 within-subjects factor 1 within-, 1 between-subjects factors 2 within-, 1 between-subjects factors

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SPSS Topics Multilevel Modeling

Vários materiais sobre Modelação Multinível

Vários materiais sobre Modelação Multinível

SPSS Topics
Multilevel Modeling

These pages contain links from all parts of our web site and others web sites on multilevel modeling.  The topics will vary from introductory to advanced.
SPSS Frequently Asked Questions
SPSS Textbook Examples
SPSS Paper Examples
SPSS Library
Code Fragments

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SPSS Topics Logistic Regression

Vários materiais sobre Regressão Logística

Vários materiais sobre Regressão Logística

SPSS Topics
Logistic Regression

These pages contain links from all parts of our web site and others web sites on logistic and related types of regression.  The topics will vary from introductory to advanced.
Data Analysis Examples
Annotated Output
SPSS Frequently Asked Questions
Textbook Examples
SPSS Library
SPSS Code Fragments

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