Publications by Dr. B
MLM Module 8, Part 2 Demonstration - Advanced Growth Models
This week, we continue with the data from the US Sustaining Effects Study (Carter 1984), which attempted to assess the sustaining impacts of compensatory schooling for students over time. We will dig into nonlinear growth patterns and heterogeneous variance models, two advanced topics in growth modeling. Load in Our MVP Packages suppressPackageS...
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MLM Module 9 - Generalized Linear Mixed Models (GLMMs)
This week, we will look at the 2018 National Survey of Children’s Health. https://www.census.gov/data/datasets/2018/demo/nsch/nsch2018.html Load in Our MVP Packages suppressPackageStartupMessages(library(tidyverse)) library(lme4) Loading required package: Matrix Attaching package: ‘Matrix’ The following objects are masked from ‘package...
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MLM Module 10 Demonstration - Checking Model Assumptions
This week, we will us the productivity dataset from Stata, developed by Dr. Alicia Munnell and published by the Federal Reserve Bank of Boston (1990). Load in Our MVP Packages suppressPackageStartupMessages(library(tidyverse)) library(lme4) library(Hmisc) Part 1: Loading in the Data, and then Recoding Variables Load in the Data productivity ...
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Relative Risk Estimates for MC Quant Paper
Load some useful packages suppressPackageStartupMessages(library(tidyverse)) library(haven) Load in the summary datasets in Stata format: state_wide_all <- read_dta("2017_state_wide_all.dta") head(state_wide_all) state_wide_elem <- read_dta("state_wide_elem.dta") head(state_wide_elem) state_wide_sec <- read_dta("state_wide_sec.dta") head(state...
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MLM Module 11 Demonstration - Cross-Classified Linear Mixed Models
This week, we will be using George Leckie’s (2009) scotland dataset, which includes 2,310 students, nested within 17 schools and separately nested within 500+ neighborhoods. Load in Our MVP Packages suppressPackageStartupMessages(library(tidyverse)) library(lme4) library(Hmisc) Part 1: Loading in the Data, and then Recoding Variables Load in...
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MVS Module 02 - Review of Regression and ANOVA
Load in relevant packages library(tidyverse) Registered S3 methods overwritten by 'dbplyr': method from print.tbl_lazy print.tbl_sql ── Attaching packages ──────────────────────────────────────────────────────�...
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MVS Module 1 Demonstration - Intro to R and R Studio
Load in your packages These can also be found on the right side menu under “Packages”. Packages are made by other R users and are groups of functions that make using R easier. There are certain packages we tend to use more often than not so it’s usually good to start with loading these packages. If you don’t have these in your library, yo...
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MVS Module 1 CR
Load Some Helpful Packages library(tidyverse) library(psych) library(haven) Load in the Data for the Assignment gss <- read_dta("descriptive_gss.dta") View(gss) Using describe to explore satjob7 describe(gss$satjob7) Using table to explore satjob7 table(gss$satjob7) 1 2 3 4 5 6 7 127 289 264 53 47 29 11 Creating a plot fo...
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MVS Module 6 Demonstration - Intro to Logistic Regression
Load in a few packages library(tidyverse) library(psych) Load in the data library(haven) els <- read_dta("ELS_Logistic.dta") Quick Data Cleaning and Coding Lesson! Using case_when glimpse(els) Rows: 16,197 Columns: 55 $ stu_id <dbl> 101101, 101102, 101104, 101105, 101106, 101107, 101108, 101109, 101110, 1011… $ sch_id <dbl> 1011, 1011, 10...
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MVS Module 5 Demonstration - Assessing Regression Assumptions
This week, we focus on checking assumptions for regression models. Many of these techniques are also useful for models other than the linear ones we have learned up to this point. We will use a dataset that has a lot of issues as a model to show what violation of assumptions “looks” like. Load in your data Load in packages library(tidyverse...
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