Start Date: August 5, 2024

Report Date: 30 August 2024

Source: Psi Chi R

Happy August! This month we are using data from the Psi Chi NICE project: Understanding Family Dynamics in a Cross-Cultural Sample. The manuscript for this project, which was the first ever NICE project, was recently published.

Codebook: https://osf.io/ey7ph

Load packages and import data

#library(tidyverse)
#library(purr)

library(dplyr)
library(ggplot2)

dataset=read.csv('https://osf.io/download/j58c7')
#skimr::skim(dataset)

Data processing (level 1)

Filter out participants who have progress values less than 100

dataset1 = dataset %>% 
  filter(Progress < 100)

slice_sample(dataset1,n=5)
Progress Duration_in_seconds UserLanguage Age Gender Countryborn Stateborn Countrycurrent Statecurrent Yearscurrent Ethnicity Race OtherText SexOrient Childbiodad Childbiomom Childstepdad Childstepmom Childadoptdad Childadoptmom Childaunt Childuncle Childgrandfather Childgrandmother Childfosterdad Childfostermom Currentbiodad Currentbiomom Currentstepdad Currentstepmom Currentadoptdad Currentadoptmom Currentaunt Currentuncle Currentgrandfather Currentgrandmother Currentfosterdad Currentfostermom Primarychild Primarynow Samesex Parentdecease Parentdeceasetext Siblings Primmomnow Primdadnow Hoursmom Hoursdad SubSES FatherEd FatherEdText MotherEd MotherEdText Religionnow ReligChristother ReligOther OtherMaternal OtherMaternalText DyadM1 DyadM2 DyadM3 DyadM4 DyadM5 DyadM6 DyadM7 DyadM8 DyadM9 DyadM10 DyadM11 DyadM12 DyadM13 DyadM14 DyadM15 DyadM16 DyadM17 DyadM18 DyadM19 DyadM20 DyadM21 DyadM22 DyadM23 DyadM24 DyadM25 NRIM1 NRIM2 NRIM3 NRIM4 NRIM5 NRIM6 NRIM7 NRIM8 NRIM9 NRIM10 NRIM11 NRIM12 NRIM13 NRIM14 NRIM15 NRIM16 NRIM17 NRIM18 NRIM19 NRIM20 NRIM21 NRIM22 NRIM23 NRIM24 NRIM25 NRIM26 NRIM27 NRIM28 NRIM29 NRIM30 NRIM31 NRIM32 NRIM33 NRIM34 NRIM35 NRIM36 NRIM37 NRIM38 NRIM39 NRIM40 NRIM41 NRIM42 NRIM43 NRIM44 NRIM45 NRIM46 NRIM47 NRIM48 NRIM49 NRIM50 NRIM51 NRIM52 NRIM53 NRIM54 NRIM55 NRIM56 NRIM57 NRIM58 NRIM59 NRIM60 NRIM61 NRIM62 NRIM63 NRIM64 OtherPaternal OtherPaternalText DyadF1 DyadF2 DyadF3 DyadF4 DyadF5 DyadF6 DyadF7 DyadF8 DyadF9 DyadF10 DyadF11 DyadF12 DyadF13 DyadF14 DyadF15 DyadF16 DyadF17 DyadF18 DyadF19 DyadF20 DyadF21 DyadF22 DyadF23 DyadF24 DyadF25 NRIF1 NRIF2 NRIF3 NRIF4 NRIF5 NRIF6 NRIF7 NRIF8 NRIF9 NRIF10 NRIF11 NRIF12 NRIF13 NRIF14 NRIF15 NRIF16 NRIF17 NRIF18 NRIF19 NRIF20 NRIF21 NRIF22 NRIF23 NRIF24 NRIF25 NRIF26 NRIF27 NRIF28 NRIF29 NRIF30 NRIF31 NRIF32 NRIF33 NRIF34 NRIF35 NRIF36 NRIF37 NRIF38 NRIF39 NRIF40 NRIF41 NRIF42 NRIF43 NRIF44 NRIF45 NRIF46 NRIF47 NRIF48 NRIF49 NRIF50 NRIF51 NRIF52 NRIF53 NRIF54 NRIF55 NRIF56 NRIF57 NRIF58 NRIF59 NRIF60 NRIF61 NRIF62 NRIF63 NRIF64 COS1 COS2 COS3 COS4 COS5 COS6 COS7 COS8 COS9 COS10 COS11 COS12 COS13 COS14 COS15 COS16 FPS1 FPS2 FPS3 FPS4 FPS5 FPS6 FPS7 FPS8 FPS9 FPS10 SBQ1 SBQ2 SBQ3 SBQ4 SBQ5 SBQ6 SBQ7 SBQ8 SBQ9 SBQ10 SBQ11 SBQ12 SBQ13 SBQ14 SBQ15 SBQ16 SBQ17 SBQ18 SBQ19 SBQ20 SBQ21 SBQ22 SBQ23 SBQ24 SBQ25 YRBS1 YRBS2 YRBS3 YRBS4 YRBS5 YRBS6 YRBS7 YRBS8 YRBS9 YRBS10 YRBS11 YRBS12 YRBS13 YRBS14 YRBS15 YRBS16 YRBS17 SDQ1 SDQ2 SDQ3 SDQ4 SDQ5 SDQ6 SDQ7 SDQ8 SDQ9 SDQ10 SDQ11 SDQ12 SDQ13 SDQ14 SDQ15 SDQ16 SDQ17 SDQ18 SDQ19 SDQ20 SDQ21 SDQ22 SDQ23 SDQ23b SDQ24 SDQ25 FACES1 FACES2 FACES3 FACES4 FACES5 FACES6 FACES7 FACES8 FACES9 FACES10 FACES11 FACES12 FACES13 FACES14 FACES15 FACES16 FACES17 FACES18 FACES19 FACES20 FACES21 FACES22 FACES23 FACES24 FACES25 FACES26 FACES27 FACES28 FACES29 FACES30 FACES31 FACES32 FACES33 FACES34 FACES35 FACES36 FACES37 FACES38 FACES39 FACES40 FACES41 FACES42 FACES43 FACES44 FACES45 FACES46 FACES47 FACES48 FACES49 FACES50 FACES51 FACES52 FACES53 FACES54 FACES55 FACES56 FACES57 FACES58 FACES59 FACES60 FACES61 FACES62
84 20055 EN 18 1 187 25 187 19 10 1 straight 1 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 2 3 2 2 3 2 3 11 NA 1 1 1 1 1 1 3 2 2 1 1 1 1 1 1 1 1 4 4 4 4 4 4 2 4 2 2 2 2 4 4 2 2 3 3 3 3 4 4 3 3 4 4 5 5 3 3 2 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 3 3 3 4 4 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 3 3 3 4 4 2 4 3 3 2 3 4 4 2 2 2 2 3 3 4 4 4 4 4 4 5 5 3 3 2 3 4 3 2 2 2 2 3 2 5 5 4 4 5 5 4 4 3 2 4 3 3 3 2 2 3 3 4 4 3 3 5 5 4 3 4 3 4 3 1 1 7 6 4 8 7 8 6 8 7 9 6 8 6 5 7 7 4 4 4 4 4 4 4 4 4 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 2 1 2 3 1 2 2 2 1 1 2 1 3 2 1 2 2 1 1 2 1 1 2 1 1 1 4 3 3 4 2 2 4 4 2 2 2 2 4 4 2 2 2 1 4 3 2 2 4 3 4 4 4 3 4 1 4 4 4 2 4 3 4 3 3 3 3 NA 3 4 3 3 4 4 4 3 3 3 3 2 3 4 4 4 4 4 4 4
87 673 EN 19 1 187 25 187 25 19 white 1 1 1 NA NA NA NA NA NA NA NA NA NA 1 NA NA 1 NA NA NA NA NA NA NA NA 1 1 2 NA 2 1 1 5 3 3 13 NA 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 3 NA 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 3 1 2 3 4 5 6 7 8 9 8 7 6 5 4 3 2 1 2 3 4 5 4 3 2 1 2 1 2 3 4 NA 4 3 2 1 2 3 4 NA 4 3 2 1 2 3 4 NA 4 3 2 1 1 1 1 7 1 7 6 1 NA 1 1 1 1 1 1 1 1 1 2 3 2 1 2 3 2 1 2 3 2 1 2 3 2 1 2 3 2 1 2 3 2 1 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 5 4 3 2 1 2 3 4 1 2 3 4 5 4 3 2 1 2
84 2197 EN 18 1 187 25 187 25 18 American 1 Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 2 1 2 1 6 3 4 1 Southern Baptist NA 1 1 1 2 1 1 1 1 1 1 1 1 1 NA 1 1 1 4 5 5 5 5 4 2 5 2 4 1 5 2 4 3 2 4 4 4 4 5 5 5 5 3 4 5 5 4 4 2 5 3 3 1 4 3 4 3 4 5 5 5 5 5 5 3 5 2 4 4 4 4 2 3 3 4 4 5 5 5 5 5 5 5 5 1 2 5 5 2 2 NA 2 3 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 4 4 5 4 4 4 2 4 3 3 1 3 4 5 1 5 2 3 4 5 5 5 4 5 3 4 5 5 4 5 1 5 5 5 2 3 1 3 4 4 5 5 4 5 5 5 3 4 1 5 4 4 1 5 1 3 4 5 5 5 4 4 5 5 4 5 2 3 4 5 1 1 8 3 7 7 8 4 8 6 8 9 7 7 9 6 8 8 5 5 5 5 4 5 4 4 3 5 3 1 NA 2 NA NA 1 NA NA NA NA NA 3 NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 1 1 3 2 3 3 2 3 1 3 1 2 3 1 3 3 1 2 3 2 1 2 1 1 2 4 2 2 2 4 2 4 4 1 1 4 2 4 4 2 2 4 2 5 4 2 3 3 4 5 1 2 1 4 2 4 4 3 1 4 2 5 3 4 3 2 2 4 3 4 4 4 4 4 3 2 3 4 3 3 4 3 4 4 3 3 3
84 1437 EN 18 1 187 44 187 25 18 White 1 1 1 1 NA NA NA NA NA 1 1 NA NA NA 1 1 NA NA NA NA NA NA NA NA NA 2 2 2 2 3 Step-father 1 2 7 4 2 1 Methodist NA 3 2 2 2 2 1 2 1 1 1 1 1 1 1 1 2 2 4 NA 5 4 5 3 4 4 2 3 2 5 3 5 2 4 2 5 3 3 5 5 5 5 2 5 4 3 3 2 3 3 4 5 3 3 2 5 5 5 5 5 5 5 4 3 2 3 2 5 4 5 2 5 2 5 2 5 5 5 5 5 4 4 5 5 5 4 5 5 3 3 NA 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 3 4 4 4 4 4 4 4 3 4 2 5 5 5 2 5 3 5 4 5 5 5 5 5 2 5 5 5 5 5 2 4 5 5 2 4 3 5 4 5 5 5 5 5 5 5 4 5 2 4 5 5 3 5 3 5 2 5 5 5 5 5 5 5 5 5 4 5 5 5 3 3 9 9 7 9 7 8 9 9 7 6 7 6 9 1 9 7 5 5 5 4 4 5 5 5 3 5 2 1 2 4 3 3 1 2 4 4 4 3 3 4 1 4 1 3 3 2 4 2 2 1 2 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 1 1 3 1 2 2 2 3 1 3 1 1 3 2 2 3 1 1 3 3 1 2 2 1 2 5 4 2 2 2 2 5 3 1 2 2 2 5 5 2 3 2 1 4 4 1 2 4 2 5 4 2 2 2 2 4 2 2 1 4 1 5 3 4 1 2 1 4 4 4 4 4 4 4 4 3 4 5 4 5 5 5 5 4 4 5 5
84 16722 EN 18 1 187 25 187 25 18 Bengali 4 straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA NA NA 2 1 2 1 Mother 1 1 1 6 1 1 8 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 NA 5 4 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 1 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2 2 1 2 2 1 2 2 2 1 1 2 1 2 2 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 3 3 3 3 2 3 3 3 3 3 2 3 2 3 3 3 2 3 3 3 3 3 3 3 3 5 5 4 5 5 3 3 5 5 3 3 3 5 5 3 4 5 5 4 5 4 3 4 5 5 4 5 4 5 4 5 4 4 5 3 4 5 4 5 2 4 4 4 3 NA 5 5 5 5 3 5 4 5 5 4 4 3 5 5 5 4 4

Filter out participants who are missing values for age and for gender.

dataset2 = dataset1 %>% 
  filter(!is.na(Age),
         !is.na(Gender))

slice_sample(dataset2,n=5)
Progress Duration_in_seconds UserLanguage Age Gender Countryborn Stateborn Countrycurrent Statecurrent Yearscurrent Ethnicity Race OtherText SexOrient Childbiodad Childbiomom Childstepdad Childstepmom Childadoptdad Childadoptmom Childaunt Childuncle Childgrandfather Childgrandmother Childfosterdad Childfostermom Currentbiodad Currentbiomom Currentstepdad Currentstepmom Currentadoptdad Currentadoptmom Currentaunt Currentuncle Currentgrandfather Currentgrandmother Currentfosterdad Currentfostermom Primarychild Primarynow Samesex Parentdecease Parentdeceasetext Siblings Primmomnow Primdadnow Hoursmom Hoursdad SubSES FatherEd FatherEdText MotherEd MotherEdText Religionnow ReligChristother ReligOther OtherMaternal OtherMaternalText DyadM1 DyadM2 DyadM3 DyadM4 DyadM5 DyadM6 DyadM7 DyadM8 DyadM9 DyadM10 DyadM11 DyadM12 DyadM13 DyadM14 DyadM15 DyadM16 DyadM17 DyadM18 DyadM19 DyadM20 DyadM21 DyadM22 DyadM23 DyadM24 DyadM25 NRIM1 NRIM2 NRIM3 NRIM4 NRIM5 NRIM6 NRIM7 NRIM8 NRIM9 NRIM10 NRIM11 NRIM12 NRIM13 NRIM14 NRIM15 NRIM16 NRIM17 NRIM18 NRIM19 NRIM20 NRIM21 NRIM22 NRIM23 NRIM24 NRIM25 NRIM26 NRIM27 NRIM28 NRIM29 NRIM30 NRIM31 NRIM32 NRIM33 NRIM34 NRIM35 NRIM36 NRIM37 NRIM38 NRIM39 NRIM40 NRIM41 NRIM42 NRIM43 NRIM44 NRIM45 NRIM46 NRIM47 NRIM48 NRIM49 NRIM50 NRIM51 NRIM52 NRIM53 NRIM54 NRIM55 NRIM56 NRIM57 NRIM58 NRIM59 NRIM60 NRIM61 NRIM62 NRIM63 NRIM64 OtherPaternal OtherPaternalText DyadF1 DyadF2 DyadF3 DyadF4 DyadF5 DyadF6 DyadF7 DyadF8 DyadF9 DyadF10 DyadF11 DyadF12 DyadF13 DyadF14 DyadF15 DyadF16 DyadF17 DyadF18 DyadF19 DyadF20 DyadF21 DyadF22 DyadF23 DyadF24 DyadF25 NRIF1 NRIF2 NRIF3 NRIF4 NRIF5 NRIF6 NRIF7 NRIF8 NRIF9 NRIF10 NRIF11 NRIF12 NRIF13 NRIF14 NRIF15 NRIF16 NRIF17 NRIF18 NRIF19 NRIF20 NRIF21 NRIF22 NRIF23 NRIF24 NRIF25 NRIF26 NRIF27 NRIF28 NRIF29 NRIF30 NRIF31 NRIF32 NRIF33 NRIF34 NRIF35 NRIF36 NRIF37 NRIF38 NRIF39 NRIF40 NRIF41 NRIF42 NRIF43 NRIF44 NRIF45 NRIF46 NRIF47 NRIF48 NRIF49 NRIF50 NRIF51 NRIF52 NRIF53 NRIF54 NRIF55 NRIF56 NRIF57 NRIF58 NRIF59 NRIF60 NRIF61 NRIF62 NRIF63 NRIF64 COS1 COS2 COS3 COS4 COS5 COS6 COS7 COS8 COS9 COS10 COS11 COS12 COS13 COS14 COS15 COS16 FPS1 FPS2 FPS3 FPS4 FPS5 FPS6 FPS7 FPS8 FPS9 FPS10 SBQ1 SBQ2 SBQ3 SBQ4 SBQ5 SBQ6 SBQ7 SBQ8 SBQ9 SBQ10 SBQ11 SBQ12 SBQ13 SBQ14 SBQ15 SBQ16 SBQ17 SBQ18 SBQ19 SBQ20 SBQ21 SBQ22 SBQ23 SBQ24 SBQ25 YRBS1 YRBS2 YRBS3 YRBS4 YRBS5 YRBS6 YRBS7 YRBS8 YRBS9 YRBS10 YRBS11 YRBS12 YRBS13 YRBS14 YRBS15 YRBS16 YRBS17 SDQ1 SDQ2 SDQ3 SDQ4 SDQ5 SDQ6 SDQ7 SDQ8 SDQ9 SDQ10 SDQ11 SDQ12 SDQ13 SDQ14 SDQ15 SDQ16 SDQ17 SDQ18 SDQ19 SDQ20 SDQ21 SDQ22 SDQ23 SDQ23b SDQ24 SDQ25 FACES1 FACES2 FACES3 FACES4 FACES5 FACES6 FACES7 FACES8 FACES9 FACES10 FACES11 FACES12 FACES13 FACES14 FACES15 FACES16 FACES17 FACES18 FACES19 FACES20 FACES21 FACES22 FACES23 FACES24 FACES25 FACES26 FACES27 FACES28 FACES29 FACES30 FACES31 FACES32 FACES33 FACES34 FACES35 FACES36 FACES37 FACES38 FACES39 FACES40 FACES41 FACES42 FACES43 FACES44 FACES45 FACES46 FACES47 FACES48 FACES49 FACES50 FACES51 FACES52 FACES53 FACES54 FACES55 FACES56 FACES57 FACES58 FACES59 FACES60 FACES61 FACES62
84 1786 EN 18 1 111 NA 187 1 12 Hispanic NA Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 2 2 2 1 8 4 4 1 Baptist/catholic NA 3 3 3 4 1 2 3 3 1 1 1 1 1 1 2 2 2 4 3 5 5 5 5 5 5 4 4 2 4 4 5 1 5 3 5 5 5 5 5 5 5 2 5 5 5 3 5 2 5 5 5 2 5 3 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 2 5 2 5 5 5 5 5 5 5 5 5 5 5 5 5 2 4 NA 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 2 3 1 5 4 5 2 3 2 3 4 5 5 5 5 5 1 5 5 5 3 5 1 5 4 5 2 3 2 3 4 5 5 5 5 5 5 5 4 5 1 5 4 4 1 4 2 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 2 3 8 8 8 9 8 6 8 9 6 6 9 8 5 6 8 8 5 5 5 5 5 5 5 5 4 5 4 1 4 1 4 1 1 1 1 4 4 4 4 4 NA 1 2 1 3 4 4 3 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 2 1 3 2 1 3 2 3 3 3 2 2 3 1 2 3 1 1 3 3 1 3 1 2 3 5 4 2 2 3 4 5 2 1 2 3 2 5 4 5 5 3 1 5 NA 1 4 1 2 5 4 1 3 4 2 5 4 1 3 4 2 2 4 4 4 2 2 4 4 5 5 4 5 4 5 1 5 5 4 4 5 5 4 5 3 3 5
87 3386 EN 19 2 187 25 187 25 19 white 1 straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 NA 3 1 1 7 5 3 11 NA 2 2 2 1 1 1 3 1 2 1 1 1 1 1 1 1 1 4 5 5 5 5 5 5 5 3 3 2 1 4 3 2 1 4 4 4 4 5 5 5 5 2 3 5 5 4 4 2 2 5 5 2 1 4 3 4 4 5 5 5 5 4 4 4 4 2 1 4 4 2 1 3 3 4 4 5 5 3 3 5 5 5 5 5 5 5 5 3 3 NA 3 3 3 2 2 1 2 1 3 1 1 1 1 1 1 1 1 1 3 2 2 2 2 1 2 1 2 2 1 1 2 2 1 1 2 1 2 2 1 2 1 3 1 1 1 1 1 3 1 2 1 2 1 1 1 1 1 2 1 2 1 1 1 1 1 3 1 2 1 2 1 1 1 1 2 2 1 2 1 1 1 2 1 1 1 2 1 2 2 6 9 6 5 6 6 7 7 7 6 5 8 7 6 9 7 3 4 5 5 5 5 5 5 5 5 4 1 4 4 1 4 1 1 4 4 4 4 4 4 1 1 1 4 4 4 4 1 4 1 3 1 1 1 1 1 3 2 1 NA 1 1 1 1 1 1 1 1 3 2 3 1 2 2 2 3 3 2 3 2 1 2 2 3 2 1 1 3 2 2 2 2 3 2 5 2 2 3 2 1 4 1 1 2 3 1 5 4 1 2 1 1 5 4 1 3 4 1 5 2 1 1 4 1 4 2 2 1 4 2 4 4 3 3 2 1 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4
83 2609 EN 19 1 163 NA 187 25 19 2 NA 1 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA 2 2 2 2 1 Step Father 2 1 7 3 2 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 4 4 2 4 5 5 1 5 5 5 2 4 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 4 4 4 4 4 4 1 1 1 1 1 1 1 4 1 3 1 4 5 5 5 5 1 4 3 3 1 3 1 3 1 3 1 5 1 3 1 3 5 5 4 4 3 3 2 1 1 4 1 1 1 5 1 4 1 4 1 3 1 3 1 4 1 3 1 2 3 2 5 5 7 7 7 8 8 8 8 8 9 9 6 6 8 7 8 8 3 3 4 3 4 4 4 4 4 4 4 1 4 4 2 1 1 1 4 4 4 3 3 1 3 1 1 1 1 4 4 1 4 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 2 1 3 1 3 2 3 3 2 3 1 2 3 2 2 3 1 2 3 3 1 1 2 2 2 4 3 2 2 2 2 4 4 3 3 2 3 4 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 1 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4
84 4942 EN 18 1 187 25 187 25 18 Black African American 2 Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 1 2 2 2 Biological mother Biological mother 1 1 7 4 5 11 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 5 5 5 5 5 5 2 5 5 5 5 5 3 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 4 5 1 5 5 5 2 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 4 4 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 5 5 5 5 5 5 4 5 5 5 3 5 1 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 2 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 9 1 9 9 9 8 9 9 9 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 NA 1 3 1 1 1 1 3 3 3 3 1 3 1 1 3 1 3 3 1 1 3 3 1 3 3 1 3 5 4 2 3 4 1 5 5 1 1 5 5 4 3 3 4 2 2 1 1 4 4 2 4 2 3 4 3 2 4 3 2 3 3 3 NA 2 NA 2 2 4 5 5 4 5 4 4 3 2 4 2 4 5 NA 5 5 5 5 4 4 3 2
84 3061 EN 18 1 187 44 187 44 18 white 1 straight 1 1 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA 2 2 2 1 dad 1 my mom step dad 2 1 7 3 3 1 NA 1 1 1 1 1 1 1 1 3 1 1 2 1 1 1 1 1 3 5 5 5 5 5 5 5 2 3 1 5 1 5 2 3 2 4 2 5 5 5 5 5 3 3 5 5 4 5 2 5 4 5 2 5 2 5 5 5 5 5 5 5 5 5 3 5 2 5 4 5 3 5 2 5 2 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 NA 1 1 1 1 1 1 1 2 4 4 1 2 1 1 1 1 1 1 2 3 2 2 2 2 2 1 3 2 1 2 3 3 2 1 4 2 3 3 4 3 5 4 3 2 3 1 3 3 3 2 3 4 3 1 3 1 3 2 3 2 3 2 4 1 4 2 3 2 3 2 3 1 3 1 3 1 4 2 3 2 3 2 3 2 3 3 3 1 1 8 8 5 4 6 7 7 9 6 9 6 7 7 5 7 7 5 5 5 5 4 5 5 4 5 5 4 1 4 3 1 1 1 1 4 2 4 1 3 4 2 2 2 1 4 3 2 2 4 3 3 1 1 3 1 1 5 5 1 NA 1 1 1 1 1 1 1 1 3 1 1 2 1 2 3 1 3 2 3 1 1 3 2 2 3 1 1 2 2 1 2 1 1 2 4 4 3 4 4 3 3 2 1 3 4 1 3 2 4 3 4 2 2 3 2 3 3 1 4 3 2 5 3 1 3 5 3 2 3 1 2 2 2 4 4 3 3 3 3 4 3 3 3 2 2 3 3 3 2 3 3 2 2 2 2 3

Descriptive Statistics (level 2)

Create a variable called FACEcomm (Family Communication) by adding together the following items FACES43 + FACES44 + FACES45 + FACES46 + FACES47 + FACES48 + FACES49 + FACES50 + FACES51 + FACES52

dataset3=dataset2 %>% 
  mutate(FACEcomm = FACES43 + FACES44 + FACES45 + FACES46 + FACES47 + FACES48 + FACES49 + FACES50 + FACES51 + FACES52) %>% 
  select(FACEcomm,everything())

#skimr::skim(dataset3$FACEcomm)

-Note the average, standard deviation, and median values for the FACEcomm variable. Find the range as well.

desc_stat = function(x){
  c(mean = mean(x,na.rm = T),
  standard_dev = sd(x,na.rm = T),
  median = median(x,na.rm = T),
  range = range(x,na.rm = T))
}

desc_stat(dataset3$FACEcomm)
##         mean standard_dev       median       range1       range2 
##    37.314286     5.666057    38.000000    26.000000    50.000000
#mean:37.31, SD: 5.67, median: 38, range: 26 to 50

Data visualization (level 3)

Create a box plot of the FACEcomm variable to identify any outliers

dataset4=dataset3 %>% 
  filter(!is.na(FACEcomm))

boxplot(dataset4$FACEcomm)

dataset4 %>% 
  ggplot(aes(y=FACEcomm))+
  geom_boxplot()

Create a histogram on the FACEcomm variable to help visualize the distribution

hist(dataset4$FACEcomm)

dataset4 %>% 
  ggplot(aes(x=FACEcomm))+
  geom_histogram(binwidth = 5)

dataset4 %>% 
  ggplot(aes(x=FACEcomm))+
  geom_density()

Inferential statistics (level 4)

If you identified outliers in the previous step, filter them out from the data.

dataset5=dataset4 %>% 
  filter(FACEcomm >= 33,
         FACEcomm <= 40)

slice_sample(dataset4,n=5)
FACEcomm Progress Duration_in_seconds UserLanguage Age Gender Countryborn Stateborn Countrycurrent Statecurrent Yearscurrent Ethnicity Race OtherText SexOrient Childbiodad Childbiomom Childstepdad Childstepmom Childadoptdad Childadoptmom Childaunt Childuncle Childgrandfather Childgrandmother Childfosterdad Childfostermom Currentbiodad Currentbiomom Currentstepdad Currentstepmom Currentadoptdad Currentadoptmom Currentaunt Currentuncle Currentgrandfather Currentgrandmother Currentfosterdad Currentfostermom Primarychild Primarynow Samesex Parentdecease Parentdeceasetext Siblings Primmomnow Primdadnow Hoursmom Hoursdad SubSES FatherEd FatherEdText MotherEd MotherEdText Religionnow ReligChristother ReligOther OtherMaternal OtherMaternalText DyadM1 DyadM2 DyadM3 DyadM4 DyadM5 DyadM6 DyadM7 DyadM8 DyadM9 DyadM10 DyadM11 DyadM12 DyadM13 DyadM14 DyadM15 DyadM16 DyadM17 DyadM18 DyadM19 DyadM20 DyadM21 DyadM22 DyadM23 DyadM24 DyadM25 NRIM1 NRIM2 NRIM3 NRIM4 NRIM5 NRIM6 NRIM7 NRIM8 NRIM9 NRIM10 NRIM11 NRIM12 NRIM13 NRIM14 NRIM15 NRIM16 NRIM17 NRIM18 NRIM19 NRIM20 NRIM21 NRIM22 NRIM23 NRIM24 NRIM25 NRIM26 NRIM27 NRIM28 NRIM29 NRIM30 NRIM31 NRIM32 NRIM33 NRIM34 NRIM35 NRIM36 NRIM37 NRIM38 NRIM39 NRIM40 NRIM41 NRIM42 NRIM43 NRIM44 NRIM45 NRIM46 NRIM47 NRIM48 NRIM49 NRIM50 NRIM51 NRIM52 NRIM53 NRIM54 NRIM55 NRIM56 NRIM57 NRIM58 NRIM59 NRIM60 NRIM61 NRIM62 NRIM63 NRIM64 OtherPaternal OtherPaternalText DyadF1 DyadF2 DyadF3 DyadF4 DyadF5 DyadF6 DyadF7 DyadF8 DyadF9 DyadF10 DyadF11 DyadF12 DyadF13 DyadF14 DyadF15 DyadF16 DyadF17 DyadF18 DyadF19 DyadF20 DyadF21 DyadF22 DyadF23 DyadF24 DyadF25 NRIF1 NRIF2 NRIF3 NRIF4 NRIF5 NRIF6 NRIF7 NRIF8 NRIF9 NRIF10 NRIF11 NRIF12 NRIF13 NRIF14 NRIF15 NRIF16 NRIF17 NRIF18 NRIF19 NRIF20 NRIF21 NRIF22 NRIF23 NRIF24 NRIF25 NRIF26 NRIF27 NRIF28 NRIF29 NRIF30 NRIF31 NRIF32 NRIF33 NRIF34 NRIF35 NRIF36 NRIF37 NRIF38 NRIF39 NRIF40 NRIF41 NRIF42 NRIF43 NRIF44 NRIF45 NRIF46 NRIF47 NRIF48 NRIF49 NRIF50 NRIF51 NRIF52 NRIF53 NRIF54 NRIF55 NRIF56 NRIF57 NRIF58 NRIF59 NRIF60 NRIF61 NRIF62 NRIF63 NRIF64 COS1 COS2 COS3 COS4 COS5 COS6 COS7 COS8 COS9 COS10 COS11 COS12 COS13 COS14 COS15 COS16 FPS1 FPS2 FPS3 FPS4 FPS5 FPS6 FPS7 FPS8 FPS9 FPS10 SBQ1 SBQ2 SBQ3 SBQ4 SBQ5 SBQ6 SBQ7 SBQ8 SBQ9 SBQ10 SBQ11 SBQ12 SBQ13 SBQ14 SBQ15 SBQ16 SBQ17 SBQ18 SBQ19 SBQ20 SBQ21 SBQ22 SBQ23 SBQ24 SBQ25 YRBS1 YRBS2 YRBS3 YRBS4 YRBS5 YRBS6 YRBS7 YRBS8 YRBS9 YRBS10 YRBS11 YRBS12 YRBS13 YRBS14 YRBS15 YRBS16 YRBS17 SDQ1 SDQ2 SDQ3 SDQ4 SDQ5 SDQ6 SDQ7 SDQ8 SDQ9 SDQ10 SDQ11 SDQ12 SDQ13 SDQ14 SDQ15 SDQ16 SDQ17 SDQ18 SDQ19 SDQ20 SDQ21 SDQ22 SDQ23 SDQ23b SDQ24 SDQ25 FACES1 FACES2 FACES3 FACES4 FACES5 FACES6 FACES7 FACES8 FACES9 FACES10 FACES11 FACES12 FACES13 FACES14 FACES15 FACES16 FACES17 FACES18 FACES19 FACES20 FACES21 FACES22 FACES23 FACES24 FACES25 FACES26 FACES27 FACES28 FACES29 FACES30 FACES31 FACES32 FACES33 FACES34 FACES35 FACES36 FACES37 FACES38 FACES39 FACES40 FACES41 FACES42 FACES43 FACES44 FACES45 FACES46 FACES47 FACES48 FACES49 FACES50 FACES51 FACES52 FACES53 FACES54 FACES55 FACES56 FACES57 FACES58 FACES59 FACES60 FACES61 FACES62
42 84 1786 EN 18 1 111 NA 187 1 12 Hispanic NA Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 2 2 2 1 8 4 4 1 Baptist/catholic NA 3 3 3 4 1 2 3 3 1 1 1 1 1 1 2 2 2 4 3 5 5 5 5 5 5 4 4 2 4 4 5 1 5 3 5 5 5 5 5 5 5 2 5 5 5 3 5 2 5 5 5 2 5 3 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 2 5 2 5 5 5 5 5 5 5 5 5 5 5 5 5 2 4 NA 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 2 3 1 5 4 5 2 3 2 3 4 5 5 5 5 5 1 5 5 5 3 5 1 5 4 5 2 3 2 3 4 5 5 5 5 5 5 5 4 5 1 5 4 4 1 4 2 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 2 3 8 8 8 9 8 6 8 9 6 6 9 8 5 6 8 8 5 5 5 5 5 5 5 5 4 5 4 1 4 1 4 1 1 1 1 4 4 4 4 4 NA 1 2 1 3 4 4 3 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 2 1 3 2 1 3 2 3 3 3 2 2 3 1 2 3 1 1 3 3 1 3 1 2 3 5 4 2 2 3 4 5 2 1 2 3 2 5 4 5 5 3 1 5 NA 1 4 1 2 5 4 1 3 4 2 5 4 1 3 4 2 2 4 4 4 2 2 4 4 5 5 4 5 4 5 1 5 5 4 4 5 5 4 5 3 3 5
50 84 471 EN 19 1 187 25 187 25 19 White 1 Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 2 2 1 1 1 6 3 3 11 NA 5 4 4 4 4 5 3 5 4 5 4 5 3 4 3 4 4 3 5 4 4 3 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 NA 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 1 3 1 NA 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
45 87 1311 EN 18 2 187 44 187 44 18 Czech, Native American 1 Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 NA 3 1 1 9 3 3 1 NA 2 3 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 4 5 5 5 5 5 5 5 2 3 1 5 5 5 2 5 3 5 3 3 5 5 5 5 2 3 5 5 3 3 2 5 5 5 1 5 1 5 5 5 5 5 5 5 5 5 3 3 1 5 4 4 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 2 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 2 2 1 5 5 5 1 5 2 5 2 5 5 5 5 5 2 5 5 5 5 5 1 5 2 5 1 5 2 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 3 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5 9 9 9 9 1 9 5 5 5 4 9 9 5 9 9 5 5 5 5 5 5 5 5 5 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 2 1 1 NA 1 1 1 1 1 1 1 1 3 2 1 3 1 1 2 2 3 1 3 1 1 3 2 1 3 1 1 3 3 1 2 1 2 2 5 4 2 1 1 1 4 3 1 1 1 1 5 5 3 1 1 1 5 5 1 1 5 1 5 3 5 1 2 1 5 3 5 1 3 1 5 3 3 1 1 1 4 5 5 5 5 5 5 5 1 5 3 3 3 5 3 3 3 3 5 5
40 87 8477 EN 18 1 187 1 187 25 18 White 1 Straight 1 1 NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA NA 2 2 2 NA 1 3 3 6 3 3 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 5 5 5 5 5 5 5 3 4 1 4 4 5 1 4 4 4 4 4 5 5 5 5 2 4 5 5 4 4 2 4 4 4 4 4 2 4 4 4 5 5 5 5 5 5 5 5 1 4 5 5 2 4 3 4 5 5 5 5 5 5 5 5 5 5 2 5 5 5 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 4 4 4 5 5 3 5 3 3 1 3 4 4 1 4 3 4 4 4 5 5 5 5 2 4 5 5 5 5 1 5 5 5 1 5 2 4 4 4 5 5 5 5 5 5 5 5 1 5 5 4 3 4 4 4 4 4 4 4 5 4 5 5 5 5 3 4 4 5 1 1 9 9 1 4 8 8 6 8 8 8 8 8 8 8 8 8 4 5 5 5 5 5 5 5 5 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 1 1 2 1 3 2 1 3 2 3 2 1 3 1 2 3 1 1 2 3 1 3 1 1 3 5 3 2 2 3 2 4 3 1 3 3 2 4 4 2 2 3 2 4 4 2 3 4 3 3 3 2 2 2 1 4 4 2 3 4 2 4 3 2 2 3 2 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4
31 83 2609 EN 19 1 163 NA 187 25 19 2 NA 1 NA NA NA NA NA NA NA NA NA NA NA 1 1 NA NA NA NA NA NA NA NA NA 2 2 2 2 1 Step Father 2 1 7 3 2 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 4 4 2 4 5 5 1 5 5 5 2 4 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 5 5 1 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 4 4 4 4 4 4 1 1 1 1 1 1 1 4 1 3 1 4 5 5 5 5 1 4 3 3 1 3 1 3 1 3 1 5 1 3 1 3 5 5 4 4 3 3 2 1 1 4 1 1 1 5 1 4 1 4 1 3 1 3 1 4 1 3 1 2 3 2 5 5 7 7 7 8 8 8 8 8 9 9 6 6 8 7 8 8 3 3 4 3 4 4 4 4 4 4 4 1 4 4 2 1 1 1 4 4 4 3 3 1 3 1 1 1 1 4 4 1 4 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 3 2 1 3 1 3 2 3 3 2 3 1 2 3 2 2 3 1 2 3 3 1 1 2 2 2 4 3 2 2 2 2 4 4 3 3 2 3 4 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 1 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 4 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4

Is there a significant difference in FACEcomm between people who identify with ‘Religionnow’ = 1 and ‘Religionnow’ = 11?

religion_1=dataset5 %>% 
  filter(Religionnow=='1') %>% 
  select(Religionnow,everything())

religion_11=dataset5 %>% 
  filter(Religionnow=='11') %>% 
  select(Religionnow,everything())

shapiro.test(religion_1$FACEcomm)
## 
##  Shapiro-Wilk normality test
## 
## data:  religion_1$FACEcomm
## W = 0.89318, p-value = 0.2917
shapiro.test(religion_11$FACEcomm)
## 
##  Shapiro-Wilk normality test
## 
## data:  religion_11$FACEcomm
## W = 0.98684, p-value = 0.7804
hist(religion_1$FACEcomm)

hist(religion_11$FACEcomm)

#no significant difference
t.test(religion_1$FACEcomm,religion_11$FACEcomm)
## 
##  Welch Two Sample t-test
## 
## data:  religion_1$FACEcomm and religion_11$FACEcomm
## t = 1.0092, df = 2.9257, p-value = 0.3889
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.560630  6.798725
## sample estimates:
## mean of x mean of y 
##  38.28571  36.66667
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