Wiley SPSS Statistics for Data Analysis and Visualization. Foreword xxiii. Introduction xxvii. Part I Advanced Statistics 1. Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques 3. T Test 7. ANCOVA 8. MANOVA 1. 3Factor Analysis and Unobserved Variables in SPSS 2. AMOS 2. 6Revisiting Factor Analysis and a General Orientation to AMOS 2. The General Model 2. Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping 4. Monte Carlo Simulation 4. Monte Carlo Simulation in IBM SPSS Statistics 4. Creating an SPSS Model File 4. IBM SPSS Bootstrapping 5. Proportions 6. 3Bootstrap Mean 6. Bootstrap and Linear Regression 6. Chapter 3 Regression with Categorical Outcome Variables 7. Regression Approaches in SPSS 7. Background Cetuximab, a chimeric mousehuman IgG1 monoclonal antibody against the epidermal growth factor receptor, is approved for use in colorectal cancer and. Spss 12 Full Version' title='Spss 12 Full Version' />Logistic Regression 7. Ordinal Regression Theory 7. Assumptions of Ordinal Regression Models 7. Ordinal Regression Dialogs 7. Ordinal Regression Output 8. IBM-SPSS-Statistics-24-free-download.jpg?resize=400%2C358&ssl=1' alt='Spss 12 Full Version' title='Spss 12 Full Version' />Comparison of the popularity or market share of data science, statistics, and advanced analytics software SAS, SPSS, Stata, Python, R, Mathworks, MATLAB, KNIME. WINRAR 32 BIT AND 64 BIT Crack Full Version With Key 2017. Winrar is the best software for every files and document in worldwide Computers much important. Spss 12 Full Version' title='Spss 12 Full Version' />Categorical Regression Theory 8. Assumptions of Categorical Regression Models 8. Categorical Regression Dialogs 8. Categorical Regression Output 9. Chapter 4 Building Hierarchical Linear Models 1. Overview of Hierarchical Linear Mixed Models 1. A Two Level Hierarchical Linear Model Example 1. Mixed ModelsLinear 1. Mixed ModelsLinear Output 1. Mixed ModelsGeneralized Linear 1. Mixed ModelsGeneralized Linear Output 1. Adjusting Model Structure 1. SPSS_13_screenshot.png' alt='Spss 12 Full Version' title='Spss 12 Full Version' />Part II Data Visualization 1. Chapter 5 Take Your Data Visualizations to the Next Level 1. Graphics Options in SPSS Statistics 1. Understanding the Revolutionary Approach in The Grammar of Graphics 1. Bar Chart Case Study 1. Bubble Chart Case Study 1. Chapter 6 The Code Behind SPSS Graphics Graphics Production Language 1. Introducing GPL Bubble Chart Case Study 1. GPL Help 1. 55. Bubble Chart Case Study Part Two 1. Double Regression Line Case Study 1. Arrows Case Study 1. MBTI Bubble Chart Case Study 1. Chapter 7 Mapping in IBM SPSS Statistics 1. Creating Maps with the Graphboard Template Chooser 1. Creating a Choropleth of Counts Map 1. Creating Other Map Types 1. Creating Maps Using Geographical Coordinates 1. Chapter 8 Geospatial Analytics 1. Geospatial Association Rules 1. Case Study Crime and 3. Calls 1. 94. Spatio Temporal Prediction 2. Case Study Predicting Weekly Shootings 2. Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL, and OMS 2. Starting with Crosstabs 2. Correspondence Analysis 2. Multiple Correspondence Analysis 2. Crosstabulations 2. Applying OMS and GPL to the MCA Perceptual Map 2. Chapter 1. 0 Display Complex Relationships with Multidimensional Scaling 2. Metric and Nonmetric Multidimensional Scaling 2. Nonmetric Scaling of Psychology Sub Disciplines 2. Multidimenional Scaling Dialog Options 2. Multidimensional Scaling Output Interpretation 2. Subjective Approach to Dimension Interpretation 2. Statistical Approach to Dimension Interpretation 2. Part III Predictive Analytics 2. Chapter 1. 1 SPSS Statistics versus SPSS Modeler Can I Be a Data Miner Using SPSS Statistics What Is Data Mining What Is IBM SPSS Modeler Can Data Mining Be Done in SPSS StatisticsHypothesis Testing, Type I Error, and Hold Out Validation 2. Significance of the Model and Importance of Each Independent Variable 2. The Importance of Finding and Modeling Interactions 2. Classic and Important Data Mining Tasks 2. Partitioning and Validating 2. Feature Selection 2. Balancing 2. 94. Comparing Results from Multiple Models 2. Creating Ensembles 2. Scoring New Records 3. Chapter 1. 2 IBM SPSS Data Preparation 3. Identify Unusual Cases 3. Identify Unusual Cases Dialogs 3. Identify Unusual Cases Output 3. Optimal Binning 3. Optimal Binning Dialogs 3. Optimal Binning Output 3. Chapter 1. 3 Model Complex Interactions with IBM SPSS Neural Networks 3. Why Neural Nets The Famous Case of Exclusive OR and the Perceptron 3. What Is a Hidden Layer and Why Is It Needed Neural Net Results with the XOR Variables 3. How the Weights Are Calculated Error Backpropagation 3. Creating a Consistent Partition in SPSS Statistics 3. Comparing Regression to Neural Net with the Bank Salary Case Study 3. Calculating Mean Absolute Percent Error for Both Models 3. Classification with Neural Nets Demonstrated with the Titanic Dataset 3. Chapter 1. 4 Powerful and Intuitive IBM SPSS Decision Trees 3. Building a Tree with the CHAID Algorithm 3. Review of the CHAID Algorithm 3. Adjusting the CHAID Settings 3. CRT for Classification 3. Understanding Why the CRT Algorithm Produces a Different Tree 3. Missing Data 3. 69. Changing the CRT Settings 3. Comparing the Results of All Four Models 3. Alternative Validation Options 3. The Scoring Wizard 3. Chapter 1. 5 Find Patterns and Make Predictions with K Nearest Neighbors 3. Using KNN to Find Neighbors 3. The Titanic Dataset and KNN Used as a Classifier 3. The Trade Offs between Bias and Variance 3. Comparing Our Models Decision Trees, Neural Nets, and KNN 3. Building an Ensemble 3. Part IV Syntax, Data Management, and Programmability 3. Chapter 1. 6 Write More Effi cient and Elegant Code with SPSS Syntax Techniques 3. A Syntax Primer for the Uninitiated 3. Making the Connection Menus and the Grammar of Syntax 4. What Is Inefficient Code The Case Study 4. Trap Receiver Software. Customer Dataset 4. Fixing the ZIP Codes 4. Addressing Case Sensitivity of City Names with UPPER and LOWER 4. Parsing Strings and the Index Function 4. Aggregate and Restructure 4. Pasting Variable Names, TO, Recode, and Count 4. DO REPEAT Spend Ratios 4. Merge 4. 15. Final Syntax File 4. Chapter 1. 7 Automate Your Analyses with SPSS Syntax and the Output Management System 4. Overview of the Output Management System 4. Running OMS from Menus 4. Contents xxi. Automatically Writing Selected Categories of Output to Different Formats 4. Suppressing Output 4. Working with OMS data 4. Running OMS from Syntax 4. Chapter 1. 8 Statistical Extension Commands 4. What Is an Extension Command TURF AnalysisDesigning Product Bundles 4. Large Problems 4. Quantile RegressionPredicting Airline Delays 4. Comparing Ordinary Least Squares with Quantile Regression Results 4. Operational Considerations 4. Support Vector MachinesPredicting Loan Default 4. Background 4. 61. An Example 4. 64. Operational Issues 4. Computing Cohens d Measure of Effect Size for a T Test 4. Intraclass Correlations ICC and Interrater Reliability in SPSSIf you think my writing about statistics is clear below, consider my student centered, practical and concise Step by Step Introduction to Statistics for Business for your undergraduate classes, available now from SAGE. Social scientists of all sorts will appreciate the ordinary, approachable language and practical value each chapter starts with and discusses a young small business owner facing a problem solvable with statistics, a problem solved by the end of the chapter with the statistical kung fu gained. This article has been published in the Winnower. You can cite it as Landers, R. N. 2. 01. 5. Computing intraclass correlations ICC as estimates of interrater reliability in SPSS. The Winnower 2 e. DOI 1. 0. 1. 52. You can also download the published version as a PDF by clicking here. Recently, a colleague of mine asked for some advice on how to compute interrater reliability for a coding task, and I discovered that there arent many resources online written in an easy to understand format most either 1 go in depth about formulas and computation or 2 go in depth about SPSS without giving many specific reasons for why youd make several important decisions. The primary resource available is a 1. Shrout and Fleiss1, which is quite dense. So I am taking a stab at providing a comprehensive but easier to understand resource. Reliability, generally, is the proportion of real information about a construct of interest captured by your measurement of it. For example, if someone reported the reliability of their measure was. The more uniform your measurement, the higher reliability will be. In the social sciences, we often have research participants complete surveys, in which case you dont need ICCs you would more typically use coefficient alpha. But when you have research participants provide something about themselves from which you need to extract data, your measurement becomes what you get from that extraction. For example, in one of my labs current studies, we are collecting copies of Facebook profiles from research participants, after which a team of lab assistants looks them over and makes ratings based upon their content. This process is called coding. Because the research assistants are creating the data, their ratings are my scale not the original data. Which means they 1 make mistakes and 2 vary in their ability to make those ratings. An estimate of interrater reliability will tell me what proportion of their ratings is real, i. An intraclass correlation ICC can be a useful estimate of inter rater reliability on quantitative data because it is highly flexible. A Pearson correlation can be a valid estimator of interrater reliability, but only when you have meaningful pairings between two and only two raters. What if you have more  What if your raters differ by ratee  This is where ICC comes in note that if you have qualitative data, e. ICC. Unfortunately, this flexibility makes ICC a little more complicated than many estimators of reliability. While you can often just throw items into SPSS to compute a coefficient alpha on a scale measure, there are several additional questions one must ask when computing an ICC, and one restriction. The restriction is straightforward you must have the same number of ratings for every case rated. The questions are more complicated, and their answers are based upon how you identified your raters, and what you ultimately want to do with your reliability estimate. Here are the first two questions Do you have consistent raters for all ratees  For example, do the exact same 8 raters make ratings on every rateeDo you have a sample or population of raters If your answer to Question 1 is no, you need ICC1. In SPSS, this is called One Way Random. In coding tasks, this is uncommon, since you can typically control the number of raters fairly carefully. It is most useful with massively large coding tasks. For example, if you had 2. Its called One Way Random because 1 it makes no effort to disentangle the effects of the rater and ratee i. ICC1 will always be the smallest of the ICCs. If your answer to Question 1 is yes and your answer to Question 2 is sample, you need ICC2. In SPSS, this is called Two Way Random. Unlike ICC1, this ICC assumes that the variance of the raters is only adding noise to the estimate of the ratees, and that mean rater error 0. Or in other words, while a particular rater might rate Ratee 1 high and Ratee 2 low, it should all even out across many raters. Like ICC1, it assumes a random effects model for raters, but it explicitly models this effect you can sort of think of it like controlling for rater effects when producing an estimate of reliability. If you have the same raters for each case, this is generally the model to go with. This will always be larger than ICC1 and is represented in SPSS as Two Way Random because 1 it models both an effect of rater and of ratee i. If your answer to Question 1 is yes and your answer to Question 2 is population, you need ICC3. In SPSS, this is called Two Way Mixed. This ICC makes the same assumptions as ICC2, but instead of treating rater effects as random, it treats them as fixed. This means that the raters in your task are the only raters anyone would be interested in. This is uncommon in coding, because theoretically your research assistants are only a few of an unlimited number of people that could make these ratings. This means ICC3 will also always be larger than ICC1 and typically larger than ICC2, and is represented in SPSS as Two Way Mixed because 1 it models both an effect of rater and of ratee i. After youve determined which kind of ICC you need, there is a second decision to be made are you interested in the reliability of a single rater, or of their mean  If youre coding for research, youre probably going to use the mean rating. If youre coding to determine how accurate a single person would be if they made the ratings on their own, youre interested in the reliability of a single rater. For example, in our Facebook study, we want to know both. First, we might ask what is the reliability of our ratings  Second, we might ask if one person were to make these judgments from a Facebook profile, how accurate would that person be  We add ,k to the ICC rating when looking at means, or ,1 when looking at the reliability of single raters. For example, if you computed an ICC2 with 8 raters, youd be computing ICC2,8. If you computed an ICC1 with the same 1. ICC2,1. For ICC,1, a large number of raters will produce a narrower confidence interval around your reliability estimate than a small number of raters, which is why youd still want a large number of raters, if possible, when estimating ICC,1.