Are you over 18 and want to see adult content?
More Annotations
A complete backup of onceinalifetimejourney.com
Are you over 18 and want to see adult content?
A complete backup of germantownmasjid.com
Are you over 18 and want to see adult content?
A complete backup of eglisecielouvert.com
Are you over 18 and want to see adult content?
A complete backup of arctic-council.org
Are you over 18 and want to see adult content?
Favourite Annotations
CRM | Customer Relationship Management System - Vtiger CRM
Are you over 18 and want to see adult content?
Welcome to Tower Federal Credit Union - Tower Federal Credit Union
Are you over 18 and want to see adult content?
Gyulai szállás: Wellness Hotel Gyula**** Superior - Hivatalos honlapja
Are you over 18 and want to see adult content?
Memorial Healthcare System | Memorial Healthcare System
Are you over 18 and want to see adult content?
Fitness, Bodybuilding, Powerlifting, Strongman and CrossFit News, Training | FitnessVolt.com
Are you over 18 and want to see adult content?
Blog des francais a Londres – s'installer et découvrir Londres
Are you over 18 and want to see adult content?
Box mensuelle: Découvrez toutes les meilleures Box du moment
Are you over 18 and want to see adult content?
Text
exceptions.
HOW BIG OF A SAMPLE SIZE DO YOU NEED FOR FACTOR ANALYSIS The specific focus in factor analysis is understanding which variables are associated with which latent constructs. The approach is slightly different if you’re running an exploratory or a confirmatory model, but this overall focus is the same.If power isn’t the main issue, how big of a sample do you need in factor analysis?The short answer is: a big one.The long answer is a little more THE ASSUMPTIONS OF LINEAR MODELS: EXPLICIT AND IMPLICIT If you’ve compared two textbooks on linear models, chances are, you’ve seen two different lists of assumptions. I’ve spent a lot of time trying to get to the bottom of this, and I think it comes down to a few things. 1. There are four assumptions that are explicitly stated along with the model, and some authors stop there. THE EFFECT SIZE: THE MOST DIFFICULT STEP IN CALCULATING One of the most difficult steps in calculating sample size estimates is determining the smallest scientifically meaningful effect size. Here's the logic: The power of every significance test is based on four things: the alpha level, the size of the effect, the amount of variation in the data, and the sample size. The effect size in question will be measured differently, depending on which THE FUNDAMENTAL DIFFERENCE BETWEEN PRINCIPAL COMPONENT One of the many confusing issues in statistics is the confusion between Principal Component Analysis (PCA) and Factor Analysis (FA). They are very similar in many ways, so it’s not hard to see why they’re so often confused. They appear to be different varieties of the same analysis rather than two different methods. Yet there is a fundamental difference between them that has huge effects WHEN TO LEAVE INSIGNIFICANT EFFECTS IN A MODEL You may have noticed conflicting advice about whether to leave insignificant effects in a model or take them out in order to simplify the model. One effect of leaving in insignificant predictors is on p-values–they use up precious df in small samples. But if your sample isn’t small, the effect is negligible. The bigger effect THE ADVANTAGES OF RSTUDIO There are multiple ways to interface with R. Some common interfaces are the basic R GUI, R Commander (the package “Rcmdr” that you use on top of the basic R GUI), and RStudio. When I first started to learn to use R, I was bound and determined to use the basic R GUI. As someone who was already used to programming in SAS, I wasn’t looking for a point-and-click interface like R Commander CHI-SQUARE TEST OF INDEPENDENCE RULE OF THUMB: N > 5 WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. PSPP-THE FREE, OPEN SOURCE VERSION OF SPSS I just heard recently about PSPP, which is a free, open source version of SPSS.. I have not tried it yet, but it does look promising. This is the description from its website: It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a fewexceptions.
THE ADVANTAGES OF RSTUDIO There are multiple ways to interface with R. Some common interfaces are the basic R GUI, R Commander (the package “Rcmdr” that you use on top of the basic R GUI), and RStudio. When I first started to learn to use R, I was bound and determined to use the basic R GUI. As someone who was already used to programming in SAS, I wasn’t looking for a point-and-click interface like R Commander THE ASSUMPTIONS OF LINEAR MODELS: EXPLICIT AND IMPLICIT If you’ve compared two textbooks on linear models, chances are, you’ve seen two different lists of assumptions. I’ve spent a lot of time trying to get to the bottom of this, and I think it comes down to a few things. 1. There are four assumptions that are explicitly stated along with the model, and some authors stop there. CONCEPTS YOU NEED TO UNDERSTAND TO RUN A MIXED OR Hi Karen, . I am currently using linear mixed effects models in SPSS to analysis data that are hierarchical in nature, specifically students nested in classrooms. My understanding is that linear mixed effects can be used to analyze multilevel data. While I understand the steps that are used to run linear mixed effects models in SPSS, I am having difficulty to understand how I can account for THE FUNDAMENTAL DIFFERENCE BETWEEN PRINCIPAL COMPONENT One of the many confusing issues in statistics is the confusion between Principal Component Analysis (PCA) and Factor Analysis (FA). They are very similar in many ways, so it’s not hard to see why they’re so often confused. They appear to be different varieties of the same analysis rather than two different methods. Yet there is a fundamental difference between them that has huge effects UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: OUTLIERS: TO DROP OR NOT TO DROP Outliers are one of those statistical issues that everyone knows about, but most people aren’t sure how to deal with. Most parametric statistics, like means, standard deviations, and correlations, and every statistic based on these, are highly sensitive to outliers. And since the assumptions of common statistical procedures, like linear regression and ANOVA, are also SAMPLE SIZE ESTIMATION WITHOUT PAST RELIABLE PILOT DATA OR Here's a common situation. Your grant application or committee requires sample size estimates. It's not the calculations that are hard (though they can be), it's getting the information to fill into the calculations. Every article you read on it says you need to either use pilot data or another similar study as a basis for the values to enter into the software. You have neither. No similar THE DIFFERENCE BETWEEN RELATIVE RISK AND ODDS RATIOS The basic difference is that the odds ratio is a ratio of two odds (yep, it’s that obvious) whereas the relative risk is a ratio of two probabilities. (The relative risk is also called the risk ratio). Let’s look at an example. Relative Risk/Risk Ratio. Suppose you have a school that wants to test out a new tutoring program. PSPP-THE FREE, OPEN SOURCE VERSION OF SPSS I just heard recently about PSPP, which is a free, open source version of SPSS.. I have not tried it yet, but it does look promising. This is the description from its website: It is a Free replacement for the proprietary program SPSS, and appears very similar to it with a fewexceptions.
WWW.THEANALYSISFACTOR.COM Every week, members have access to a live Q&A session where you can get the benefit of expert insight into your specific statistical challenges, questions, and issues — in a private, supportive environment. We know it can be hard to ask statistical questions. It seems like you’re either greeted with a blank stare (“I have no idea what you’re talking about”) or a superior smirk WWW.THEANALYSISFACTOR.COM We understand that statistics courses give you the theoretical knowledge, but not the skills to apply it to real, not-textbook-perfect data. And when you are in the midst of dissertation madness or your adviser does not have a background on a specific topic, it can be hard to know where to turn. HOW BIG OF A SAMPLE SIZE DO YOU NEED FOR FACTOR ANALYSIS The specific focus in factor analysis is understanding which variables are associated with which latent constructs. The approach is slightly different if you’re running an exploratory or a confirmatory model, but this overall focus is the same.If power isn’t the main issue, how big of a sample do you need in factor analysis?The short answer is: a big one.The long answer is a little more EIGHT WAYS TO DETECT MULTICOLLINEARITY Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.When the model tries to estimate their uniqueeffects, it
R IS NOT SO HARD! A TUTORIAL, PART 13: BOX PLOTS by David Lillis, Ph.D. In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot () command, which is easy to use. First, we set up a vector of numbers and then we plot them. Box plots can be created for individual variables or forvariables by
WHAT ARE NESTED MODELS? Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation. This includes favorites like: All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression Linear Mixed Models Generalized Linear Mixed Models Parametric Survival Analysis models, like Weibull models Structural Equation UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: WHEN UNEQUAL SAMPLE SIZES ARE AND ARE NOT A PROBLEM IN Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this. For example, in a two-way ANOVA, let’s say that your two independent variables ( factors) are Age (young vs. old) and Marital Status (married vs. not). STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. WWW.THEANALYSISFACTOR.COM Every week, members have access to a live Q&A session where you can get the benefit of expert insight into your specific statistical challenges, questions, and issues — in a private, supportive environment. We know it can be hard to ask statistical questions. It seems like you’re either greeted with a blank stare (“I have no idea what you’re talking about”) or a superior smirk WWW.THEANALYSISFACTOR.COM We understand that statistics courses give you the theoretical knowledge, but not the skills to apply it to real, not-textbook-perfect data. And when you are in the midst of dissertation madness or your adviser does not have a background on a specific topic, it can be hard to know where to turn. HOW BIG OF A SAMPLE SIZE DO YOU NEED FOR FACTOR ANALYSIS The specific focus in factor analysis is understanding which variables are associated with which latent constructs. The approach is slightly different if you’re running an exploratory or a confirmatory model, but this overall focus is the same.If power isn’t the main issue, how big of a sample do you need in factor analysis?The short answer is: a big one.The long answer is a little more EIGHT WAYS TO DETECT MULTICOLLINEARITY Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.When the model tries to estimate their uniqueeffects, it
R IS NOT SO HARD! A TUTORIAL, PART 13: BOX PLOTS by David Lillis, Ph.D. In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot () command, which is easy to use. First, we set up a vector of numbers and then we plot them. Box plots can be created for individual variables or forvariables by
WHAT ARE NESTED MODELS? Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation. This includes favorites like: All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression Linear Mixed Models Generalized Linear Mixed Models Parametric Survival Analysis models, like Weibull models Structural Equation UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: WHEN UNEQUAL SAMPLE SIZES ARE AND ARE NOT A PROBLEM IN Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this. For example, in a two-way ANOVA, let’s say that your two independent variables ( factors) are Age (young vs. old) and Marital Status (married vs. not). STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. CONCEPTS YOU NEED TO UNDERSTAND TO RUN A MIXED OR Hi Karen, . I am currently using linear mixed effects models in SPSS to analysis data that are hierarchical in nature, specifically students nested in classrooms. My understanding is that linear mixed effects can be used to analyze multilevel data. While I understand the steps that are used to run linear mixed effects models in SPSS, I am having difficulty to understand how I can account for UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various FIVE WAYS TO ANALYZE ORDINAL VARIABLES (SOME BETTER THAN 1. Treat ordinal variables as nominal. Ordinal variables are fundamentally categorical. One simple option is to ignore the order in the variable’s categories and treat it as nominal. There are many options for analyzing categorical variables that have no order. This can make a lot of sense for some variables. HOW TO GET A CODE BOOK FROM SPSS There is a nice little way to get a few tables with a list of all the variable metadata. It’s in the File m enu. Simply choose Display Data File Information and Working File. Doing this gives you two tables. The first includes the following information on the variables. I find the information I use the most are the labels and the missingdata
STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. SHOULD YOU ALWAYS CENTER A PREDICTOR ON THE MEAN? One problem is that the mean age at which infants utter their first word may differ from one sample to another. This means you're not always evaluating that mean that the exact same age. It's not comparable across samples. So another option is to choose a meaningful value of age that is within the values in the data set. One example may be at 12 months. OUTLIERS: TO DROP OR NOT TO DROP Outliers are one of those statistical issues that everyone knows about, but most people aren’t sure how to deal with. Most parametric statistics, like means, standard deviations, and correlations, and every statistic based on these, are highly sensitive to outliers. And since the assumptions of common statistical procedures, like linear regression and ANOVA, are also THE DIFFERENCE BETWEEN CROSSED AND NESTED FACTORS One of those tricky, but necessary, concepts in statistics is the difference between crossed and nested factors. As a reminder, a factor is just any categorical independent variable. In experiments, or any randomized designs, these factors are often manipulated. Experimental manipulations (like Treatment vs. Control) are factors. Observational categorical predictors, such as gender, time point WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: OBSERVED VALUES LESS THAN 5 IN A CHI SQUARE TEST-NO BIGGIE 1. The assumption of the Chi-square test is not that the observed value in each cell is greater than 5. It is that the expected value in each cell is greater than 5. (The expected value for each cell is row total*column total/overall total). Often when the observed values are low, the totals are too, so they overlap a lot, but not always. WWW.THEANALYSISFACTOR.COM Every week, members have access to a live Q&A session where you can get the benefit of expert insight into your specific statistical challenges, questions, and issues — in a private, supportive environment. We know it can be hard to ask statistical questions. It seems like you’re either greeted with a blank stare (“I have no idea what you’re talking about”) or a superior smirk WWW.THEANALYSISFACTOR.COM We understand that statistics courses give you the theoretical knowledge, but not the skills to apply it to real, not-textbook-perfect data. And when you are in the midst of dissertation madness or your adviser does not have a background on a specific topic, it can be hard to know where to turn. HOW BIG OF A SAMPLE SIZE DO YOU NEED FOR FACTOR ANALYSIS The specific focus in factor analysis is understanding which variables are associated with which latent constructs. The approach is slightly different if you’re running an exploratory or a confirmatory model, but this overall focus is the same.If power isn’t the main issue, how big of a sample do you need in factor analysis?The short answer is: a big one.The long answer is a little more EIGHT WAYS TO DETECT MULTICOLLINEARITY Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.When the model tries to estimate their uniqueeffects, it
R IS NOT SO HARD! A TUTORIAL, PART 13: BOX PLOTS by David Lillis, Ph.D. In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot () command, which is easy to use. First, we set up a vector of numbers and then we plot them. Box plots can be created for individual variables or forvariables by
WHAT ARE NESTED MODELS? Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation. This includes favorites like: All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression Linear Mixed Models Generalized Linear Mixed Models Parametric Survival Analysis models, like Weibull models Structural Equation UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: WHEN UNEQUAL SAMPLE SIZES ARE AND ARE NOT A PROBLEM IN Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this. For example, in a two-way ANOVA, let’s say that your two independent variables ( factors) are Age (young vs. old) and Marital Status (married vs. not). STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. WWW.THEANALYSISFACTOR.COM Every week, members have access to a live Q&A session where you can get the benefit of expert insight into your specific statistical challenges, questions, and issues — in a private, supportive environment. We know it can be hard to ask statistical questions. It seems like you’re either greeted with a blank stare (“I have no idea what you’re talking about”) or a superior smirk WWW.THEANALYSISFACTOR.COM We understand that statistics courses give you the theoretical knowledge, but not the skills to apply it to real, not-textbook-perfect data. And when you are in the midst of dissertation madness or your adviser does not have a background on a specific topic, it can be hard to know where to turn. HOW BIG OF A SAMPLE SIZE DO YOU NEED FOR FACTOR ANALYSIS The specific focus in factor analysis is understanding which variables are associated with which latent constructs. The approach is slightly different if you’re running an exploratory or a confirmatory model, but this overall focus is the same.If power isn’t the main issue, how big of a sample do you need in factor analysis?The short answer is: a big one.The long answer is a little more EIGHT WAYS TO DETECT MULTICOLLINEARITY Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.When the model tries to estimate their uniqueeffects, it
R IS NOT SO HARD! A TUTORIAL, PART 13: BOX PLOTS by David Lillis, Ph.D. In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot () command, which is easy to use. First, we set up a vector of numbers and then we plot them. Box plots can be created for individual variables or forvariables by
WHAT ARE NESTED MODELS? Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation. This includes favorites like: All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression Linear Mixed Models Generalized Linear Mixed Models Parametric Survival Analysis models, like Weibull models Structural Equation UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: WHEN UNEQUAL SAMPLE SIZES ARE AND ARE NOT A PROBLEM IN Real issues with unequal sample sizes do occur in factorial ANOVA in one situation: when the sample sizes are confounded in the two (or more) factors. Let’s unpack this. For example, in a two-way ANOVA, let’s say that your two independent variables ( factors) are Age (young vs. old) and Marital Status (married vs. not). STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. CONCEPTS YOU NEED TO UNDERSTAND TO RUN A MIXED OR Hi Karen, . I am currently using linear mixed effects models in SPSS to analysis data that are hierarchical in nature, specifically students nested in classrooms. My understanding is that linear mixed effects can be used to analyze multilevel data. While I understand the steps that are used to run linear mixed effects models in SPSS, I am having difficulty to understand how I can account for UNDERSTANDING RANDOM EFFECTS IN MIXED MODELS Understanding Random Effects in Mixed Models. by Kim Love 1 Comment. In fixed-effects models (e.g., regression, ANOVA, generalized linear models ), there is only one source of random variability. This source of variance is the random sample we take to measure our variables. It may be patients in a health facility, for whom we take various FIVE WAYS TO ANALYZE ORDINAL VARIABLES (SOME BETTER THAN 1. Treat ordinal variables as nominal. Ordinal variables are fundamentally categorical. One simple option is to ignore the order in the variable’s categories and treat it as nominal. There are many options for analyzing categorical variables that have no order. This can make a lot of sense for some variables. HOW TO GET A CODE BOOK FROM SPSS There is a nice little way to get a few tables with a list of all the variable metadata. It’s in the File m enu. Simply choose Display Data File Information and Working File. Doing this gives you two tables. The first includes the following information on the variables. I find the information I use the most are the labels and the missingdata
STRATEGIES FOR CHOOSING THE REFERENCE CATEGORY IN DUMMY So it's best to choose a category that makes interpretation of results easier. Here are a few common options for choosing a category. Remember, the regression coefficients will give you the difference in means ( and/or slopes if you've included an interaction term) between each other category and the reference category. SHOULD YOU ALWAYS CENTER A PREDICTOR ON THE MEAN? One problem is that the mean age at which infants utter their first word may differ from one sample to another. This means you're not always evaluating that mean that the exact same age. It's not comparable across samples. So another option is to choose a meaningful value of age that is within the values in the data set. One example may be at 12 months. OUTLIERS: TO DROP OR NOT TO DROP Outliers are one of those statistical issues that everyone knows about, but most people aren’t sure how to deal with. Most parametric statistics, like means, standard deviations, and correlations, and every statistic based on these, are highly sensitive to outliers. And since the assumptions of common statistical procedures, like linear regression and ANOVA, are also THE DIFFERENCE BETWEEN CROSSED AND NESTED FACTORS One of those tricky, but necessary, concepts in statistics is the difference between crossed and nested factors. As a reminder, a factor is just any categorical independent variable. In experiments, or any randomized designs, these factors are often manipulated. Experimental manipulations (like Treatment vs. Control) are factors. Observational categorical predictors, such as gender, time point WHAT IS KAPPA AND HOW DOES IT MEASURE INTER-RATER The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it's almost synonymous with inter-rater reliability.Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include: OBSERVED VALUES LESS THAN 5 IN A CHI SQUARE TEST-NO BIGGIE 1. The assumption of the Chi-square test is not that the observed value in each cell is greater than 5. It is that the expected value in each cell is greater than 5. (The expected value for each cell is row total*column total/overall total). Often when the observed values are low, the totals are too, so they overlap a lot, but not always.THE ANALYSIS FACTOR
* Home
* About
* Our Programs
* Our Team
* Our Core Values
* Our Privacy Policy* Employment
* Membership
* Statistically Speaking Membership Program* Login
* Workshops
* Live Online Workshops * On Demand Tutorials* Login
* Consulting
* Login
* Free Webinars
* Contact
* Login
LATEST BLOG POSTS
OCTOBER 2019 MEMBER TRAINING: REPORTING STRUCTURAL EQUATION MODELINGRESULTS
October 1, 2019
The last, and sometimes hardest, step for running any statistical model is writing up results. As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression. Any good statistical report includes enough information that someone else could replicate yourresults with
Read More
MULTILEVEL, HIERARCHICAL, AND MIXED MODELS–QUESTIONS ABOUTTERMINOLOGY
September 23, 2019
Multilevel models and Mixed Models are generally the same thing. In our recent webinar on the basics of mixed models, Random Intercept and Random Slope Models, we had a number of questions about terminology that I’m going to answer here. If you want to see the full recording of the webinar, get it here. It’sRead More
HOW DOES THE DISTRIBUTION OF A POPULATION IMPACT THE CONFIDENCEINTERVAL?
September 20, 2019
Spoiler alert, real data are seldom normally distributed. How does the distribution influence the estimate of the population mean and the resulting confidence interval? To figure this out, we randomly draw 100 observations 100 times from three distinct populations and plot the mean and corresponding 95% confidence interval of each sample.Read More
THE DIFFERENCE BETWEEN ASSOCIATION AND CORRELATIONSeptember 10, 2019
What does it mean for two variables to be correlated? Is that the same or different than if they’re associated or related? This is the kind of question that can feel silly, but shouldn’t. It’s just a reflection of the confusing terminology used in statistics. In this case, the technical statistical term looks like, butRead More
SEPTEMBER 2019 MEMBER TRAINING: INTERPRETATION OF EFFECT SIZESTATISTICS
August 30, 2019
Effect size statistics are required by most journals and committees these days — for good reason. They communicate just how big the effects are in your statistical results — something p-values can’t do. But they’re only useful if you can choose the most appropriate one and if you can interpret it. This can be hardRead More
R-SQUARED FOR MIXED EFFECTS MODELSAugust 21, 2019
By Kim Love When learning about linear models —that is, regression, ANOVA, and similar techniques—we are taught to calculate an R2. The R2 has the following useful properties: The range is limited to , so we can easily judge how relatively large it is. It is standardized, meaning its value does not depend on theRead More
HOW CONFIDENT ARE YOU ABOUT CONFIDENCE INTERVALS?August 12, 2019
The results of any statistical analysis should include the confidence intervals for estimated parameters. How confident are you that you can explain what they mean? Even those of us who have a solid understand of confidence intervals can get tripped up by the wording. Let’slook at an example.
Read More
MEMBER TRAINING: ELEMENTS OF EXPERIMENTAL DESIGNAugust 1, 2019
Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies. The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures.Any data set
Read More
LINEAR REGRESSION FOR AN OUTCOME VARIABLE WITH BOUNDARIESJuly 22, 2019
The following statement might surprise you, but it’s true. To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is: Continuous Unbounded Measured on an interval or ratio scale The normality assumption is about the errors in the model, which have thesame
Read More
HOW TO REDUCE THE NUMBER OF VARIABLES TO ANALYZEJuly 10, 2019
by Christos Giannoulis Many data sets contain well over a thousand variables. Such complexity, the speed of contemporary desktop computers, and the ease of use of statistical analysis packages can encourage ill-directed analysis. It is easy to generate a vast array of poor ‘results’ by throwing everything into your software andwaiting to see what
Read More
<< Older Entries
*
*
THIS MONTH’S STATISTICALLY SPEAKING WEBINAR * October 2019 Member Training: Reporting Structural EquationModeling Results
*
UPCOMING WORKSHOPS
* Analyzing Repeated Measures Data: ANOVA and Mixed Model Approaches(Oct 2019)
* Linear Models: Increasing Your Statistical Confidence (Jan 2020) * Principal Component and Exploratory Factor Analysis (Mar 2020)*
Customer Login
*
SEARCH
*
READ OUR BOOK
Data Analysis with SPSS(4th Edition)
by Stephen Sweet andKaren Grace-Martin
*
STATISTICAL RESOURCES BY TOPIC * Fundamental Statistics * Effect Size Statistics, Power, and Sample Size Calculations * Analysis of Variance and Covariance* Linear Regression
* Complex Surveys & Sampling * Count Regression Models * Logistic Regression* Missing Data
* Mixed and Multilevel Models * Principal Component Analysis and Factor Analysis * Survival Analysis and Event History Analysis * Data Analysis Practice and Skills* R
* SPSS
* Stata
*
Copyright © 2008-2019 The Analysis Factor, LLC . All rights reserved.877-272-8096
Contact Us
WordPress Admin
The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.ContinuePrivacy Policy
Privacy & Cookies Policy Necessary Always Enabled×
Details
Copyright © 2024 ArchiveBay.com. All rights reserved. Terms of Use | Privacy Policy | DMCA | 2021 | Feedback | Advertising | RSS 2.0