2. Homework - MVA
Januš Grm
2025-01-26
The homework aims to explore the factors that influence experiences in close relationships. A survey was conducted using 36 statements designed to measure different aspects of relationship experiences. Participants were asked to rate their level of agreement with each statement on a 5-point Likert scale, ranging from “Strongly Disagree” to “Strongly Agree.” The data was collected from 1,000 randomly selected individuals. The goal of the analysis is to identify the most important factors that shape experiences in close relationships using factor analysis.
mydata <- read.table("./data.csv", header=TRUE, sep=",", dec=".")
head(mydata)## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21
## 1 1 5 5 5 1 5 1 5 1 5 1 5 1 2 5 5 1 5 4 5 1
## 2 1 1 5 1 1 1 1 1 1 1 1 5 1 1 5 5 1 1 5 5 1
## 3 1 5 5 5 3 5 1 5 1 5 2 3 1 5 4 4 3 5 5 2 2
## 4 4 5 1 4 5 5 5 1 1 3 4 4 4 4 2 4 4 3 4 2 5
## 5 4 5 3 4 4 4 3 4 2 2 4 2 3 4 4 3 2 4 4 4 3
## 6 4 2 3 3 5 3 4 3 5 3 5 1 5 2 2 2 5 1 2 1 5
## Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36 age gender
## 1 1 1 5 5 5 5 4 5 5 5 5 5 5 5 5 32 2
## 2 5 1 1 5 1 5 1 5 5 5 5 5 1 5 5 35 1
## 3 1 1 3 2 2 4 2 3 4 4 4 4 4 3 2 19 1
## 4 2 4 4 5 3 4 4 2 3 2 4 4 5 4 3 32 1
## 5 2 2 5 4 3 4 5 4 5 4 4 5 5 5 4 27 1
## 6 4 5 3 2 1 4 3 1 5 5 5 5 3 4 2 18 1
## country
## 1 US
## 2 US
## 3 IL
## 4 US
## 5 IE
## 6 IT• Q1: I prefer not to show a partner how I feel deep down.
• Q2: I worry about being abandoned.
• Q3: I am very comfortable being close to romantic partners.
• Q4: I worry a lot about my relationships.
• Q5: Just when my partner starts to get close to me I find myself pulling away.
• Q6: I worry that romantic partners won’t care about me as much as I care about them.
• Q7: I get uncomfortable when a romantic partner wants to be very close.
• Q8: I worry a fair amount about losing my partner.
• Q9: I don’t feel comfortable opening up to romantic partners.
• Q10: I often wish that my partner’s feelings for me were as strong as my feelings for him/her.
• Q11: I want to get close to my partner, but I keep pulling back.
• Q12: I often want to merge completely with romantic partners, and this sometimes scares them away.
• Q13: I am nervous when partners get too close to me.
• Q14: I worry about being alone.
• Q15: I feel comfortable sharing my private thoughts and feelings with my partner.
• Q16: My desire to be very close sometimes scares people away.
• Q17: I try to avoid getting too close to my partner.
• Q18: I need a lot of reassurance that I am loved by my partner.
• Q19: I find it relatively easy to get close to my partner.
• Q20: Sometimes I feel that I force my partners to show more feeling, more commitment.
• Q21: I find it difficult to allow myself to depend on romantic partners.
• Q22: I do not often worry about being abandoned.
• Q23: I prefer not to be too close to romantic partners.
• Q24: If I can’t get my partner to show interest in me, I get upset or angry.
• Q25: I tell my partner just about everything.
• Q26: I find that my partner(s) don’t want to get as close as I would like.
• Q27: I usually discuss my problems and concerns with my partner.
• Q28: When I’m not involved in a relationship, I feel somewhat anxious and insecure.
• Q29: I feel comfortable depending on romantic partners.
• Q30: I get frustrated when my partner is not around as much as I would like.
• Q31: I don’t mind asking romantic partners for comfort, advice, or help.
• Q32: I get frustrated if romantic partners are not available when I need them.
• Q33: It helps to turn to my romantic partner in times of need.
• Q34: When romantic partners disapprove of me, I feel really bad about myself.
• Q35: I turn to my partner for many things, including comfort and reassurance.
• Q36: I resent it when my partner spends time away from me.
• age.
• gender: (1:M, 2:F)
• countrySource: https://www.kaggle.com/datasets/mathurinache/experiences-in-close-relationship-scale
mydata <- mydata[, -c(37, 38, 39)]Research Question:
What are the factors that influence experiences in close relationships?
library(pastecs)
round(stat.desc(mydata, basic = FALSE), 2)## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13
## median 2.00 4.00 4.00 4.00 2.00 4.00 2.00 3.00 2.00 3.00 3.00 2.00 2.00
## mean 2.72 3.60 3.61 3.40 2.75 3.54 2.58 3.25 2.66 3.30 2.79 2.47 2.62
## SE.mean 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## CI.mean.0.95 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
## var 1.67 1.67 1.63 1.59 1.79 1.68 1.76 1.64 1.63 1.65 1.74 1.60 1.64
## std.dev 1.29 1.29 1.28 1.26 1.34 1.30 1.33 1.28 1.28 1.29 1.32 1.27 1.28
## coef.var 0.48 0.36 0.35 0.37 0.49 0.37 0.51 0.39 0.48 0.39 0.47 0.51 0.49
## Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26
## median 4.00 4.00 2.00 2.00 4.00 4.00 3.00 3.00 2.00 2.00 3.00 4.00 2.00
## mean 3.26 3.42 2.44 2.60 3.35 3.25 2.75 3.24 2.69 2.38 3.10 3.27 2.63
## SE.mean 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04
## CI.mean.0.95 0.09 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.08 0.08 0.07
## var 2.04 1.74 1.55 1.50 1.77 1.47 1.73 1.79 1.77 1.38 1.71 1.69 1.37
## std.dev 1.43 1.32 1.24 1.22 1.33 1.21 1.32 1.34 1.33 1.18 1.31 1.30 1.17
## coef.var 0.44 0.39 0.51 0.47 0.40 0.37 0.48 0.41 0.50 0.49 0.42 0.40 0.45
## Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36
## median 4.00 2.00 3.00 3.00 4.00 4.00 4.00 4.00 4.00 2.00
## mean 3.61 2.66 2.94 3.16 3.64 3.26 3.76 3.60 3.65 2.53
## SE.mean 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04
## CI.mean.0.95 0.07 0.09 0.08 0.08 0.07 0.08 0.07 0.08 0.07 0.08
## var 1.28 1.93 1.54 1.62 1.41 1.53 1.21 1.62 1.29 1.57
## std.dev 1.13 1.39 1.24 1.27 1.19 1.24 1.10 1.27 1.14 1.25
## coef.var 0.31 0.52 0.42 0.40 0.33 0.38 0.29 0.35 0.31 0.50mydata_FA <- mydata
R <- cor(mydata_FA)
library(psych)
corPlot(R)
library(psych)
cortest.bartlett(R, n=nrow(mydata))## $chisq
## [1] 19738.39
##
## $p.value
## [1] 0
##
## $df
## [1] 630library(psych)
KMO(R)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = R)
## Overall MSA = 0.94
## MSA for each item =
## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16
## 0.95 0.92 0.97 0.96 0.93 0.94 0.95 0.94 0.96 0.92 0.93 0.91 0.95 0.94 0.96 0.90
## Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32
## 0.95 0.96 0.96 0.96 0.94 0.93 0.95 0.95 0.94 0.94 0.95 0.94 0.93 0.93 0.94 0.92
## Q33 Q34 Q35 Q36
## 0.94 0.96 0.94 0.94det(R)## [1] 2.029926e-09fa.parallel(mydata_FA,
sim = FALSE,
fa = "fa")
## Parallel analysis suggests that the number of factors = 6 and the number of components = NAlibrary(psych)
library(GPArotation)##
## Attaching package: 'GPArotation'## The following objects are masked from 'package:psych':
##
## equamax, variminfactors <- fa(mydata_FA,
covar = FALSE,
fm = "minres",
nfactors = 6,
rotate = "oblimin",
impute = "mean")
print.psych(factors,
cut = 0.3,
sort = TRUE)## Factor Analysis using method = minres
## Call: fa(r = mydata_FA, nfactors = 6, rotate = "oblimin", covar = FALSE,
## impute = "mean", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item MR1 MR3 MR2 MR4 MR5 MR6 h2 u2 com
## Q13 13 0.87 0.68 0.32 1.0
## Q5 5 0.79 0.66 0.34 1.0
## Q7 7 0.79 0.62 0.38 1.0
## Q11 11 0.75 0.60 0.40 1.1
## Q17 17 0.71 0.61 0.39 1.1
## Q23 23 0.68 0.61 0.39 1.4
## Q21 21 0.55 0.37 0.63 1.3
## Q9 9 0.50 -0.36 0.60 0.40 2.0
## Q3 3 -0.47 0.46 0.54 1.8
## Q1 1 0.40 -0.35 0.46 0.54 2.4
## Q27 27 0.79 0.57 0.43 1.0
## Q25 25 0.73 0.59 0.41 1.1
## Q33 33 0.72 0.57 0.43 1.1
## Q31 31 0.72 0.55 0.45 1.0
## Q35 35 0.65 0.57 0.43 1.2
## Q15 15 0.61 0.48 0.52 1.1
## Q19 19 -0.30 0.44 0.45 0.55 2.1
## Q29 29 0.34 0.35 0.65 2.7
## Q2 2 0.80 0.64 0.36 1.0
## Q8 8 0.79 0.66 0.34 1.1
## Q22 22 -0.73 0.51 0.49 1.2
## Q14 14 0.61 0.56 0.44 1.5
## Q4 4 0.59 0.54 0.46 1.3
## Q6 6 0.58 0.59 0.41 1.6
## Q34 34 0.42 0.31 0.69 1.7
## Q18 18 0.37 0.34 0.53 0.47 2.5
## Q32 32 0.80 0.61 0.39 1.0
## Q30 30 0.74 0.64 0.36 1.1
## Q36 36 0.63 0.45 0.55 1.1
## Q24 24 0.59 0.55 0.45 1.2
## Q16 16 0.85 0.68 0.32 1.0
## Q12 12 0.75 0.58 0.42 1.0
## Q26 26 0.54 0.54 0.46 1.5
## Q20 20 0.31 0.32 0.46 0.54 2.9
## Q10 10 0.40 0.40 0.62 0.38 2.8
## Q28 28 0.40 0.60 4.0
##
## MR1 MR3 MR2 MR4 MR5 MR6
## SS loadings 5.20 4.33 4.07 2.96 2.48 0.66
## Proportion Var 0.14 0.12 0.11 0.08 0.07 0.02
## Cumulative Var 0.14 0.26 0.38 0.46 0.53 0.55
## Proportion Explained 0.26 0.22 0.21 0.15 0.13 0.03
## Cumulative Proportion 0.26 0.48 0.69 0.84 0.97 1.00
##
## With factor correlations of
## MR1 MR3 MR2 MR4 MR5 MR6
## MR1 1.00 -0.57 0.14 -0.06 -0.12 0.11
## MR3 -0.57 1.00 0.03 0.27 0.25 -0.10
## MR2 0.14 0.03 1.00 0.50 0.50 0.13
## MR4 -0.06 0.27 0.50 1.00 0.50 0.08
## MR5 -0.12 0.25 0.50 0.50 1.00 0.19
## MR6 0.11 -0.10 0.13 0.08 0.19 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 6 factors are sufficient.
##
## df null model = 630 with the objective function = 20.02 with Chi Square = 19738.39
## df of the model are 429 and the objective function was 1.39
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic n.obs is 1000 with the empirical chi square 623.85 with prob < 2.2e-09
## The total n.obs was 1000 with Likelihood Chi Square = 1364.72 with prob < 5.1e-98
##
## Tucker Lewis Index of factoring reliability = 0.928
## RMSEA index = 0.047 and the 90 % confidence intervals are 0.044 0.05
## BIC = -1598.7
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR3 MR2 MR4 MR5 MR6
## Correlation of (regression) scores with factors 0.96 0.95 0.95 0.93 0.92 0.76
## Multiple R square of scores with factors 0.93 0.90 0.90 0.86 0.85 0.58
## Minimum correlation of possible factor scores 0.85 0.80 0.80 0.72 0.70 0.15mydata_FA <- mydata[ , !colnames(mydata) %in% c("Q28", "Q20", "Q10", "Q18", "Q1", "Q9", "Q19" , "Q29")]fa.parallel(mydata_FA,
sim = FALSE,
fa = "fa")
## Parallel analysis suggests that the number of factors = 5 and the number of components = NAlibrary(psych)
library(GPArotation)
factors <- fa(mydata_FA,
covar = FALSE,
fm = "minres",
nfactors = 5,
rotate = "oblimin",
impute = "mean")
print.psych(factors,
cut = 0.3,
sort = TRUE)## Factor Analysis using method = minres
## Call: fa(r = mydata_FA, nfactors = 5, rotate = "oblimin", covar = FALSE,
## impute = "mean", fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## item MR1 MR2 MR3 MR4 MR5 h2 u2 com
## Q13 10 0.86 0.69 0.31 1.0
## Q5 4 0.79 0.67 0.33 1.0
## Q7 6 0.77 0.61 0.39 1.0
## Q11 8 0.74 0.59 0.41 1.1
## Q17 14 0.71 0.61 0.39 1.1
## Q23 17 0.69 0.61 0.39 1.3
## Q21 15 0.56 0.34 0.66 1.0
## Q3 2 -0.47 0.44 0.56 1.7
## Q2 1 0.82 0.67 0.33 1.0
## Q8 7 0.78 0.64 0.36 1.0
## Q22 16 -0.74 0.52 0.48 1.2
## Q14 11 0.60 0.49 0.51 1.1
## Q4 3 0.59 0.54 0.46 1.3
## Q6 5 0.55 0.51 0.49 1.4
## Q34 26 0.43 0.30 0.70 1.5
## Q27 21 0.79 0.58 0.42 1.0
## Q33 25 0.73 0.57 0.43 1.0
## Q31 23 0.71 0.55 0.45 1.0
## Q25 19 0.70 0.57 0.43 1.1
## Q35 27 0.67 0.59 0.41 1.2
## Q15 12 0.58 0.46 0.54 1.1
## Q32 24 0.81 0.64 0.36 1.0
## Q30 22 0.76 0.66 0.34 1.0
## Q36 28 0.63 0.45 0.55 1.1
## Q24 18 0.56 0.52 0.48 1.3
## Q16 13 0.83 0.69 0.31 1.0
## Q12 9 0.75 0.60 0.40 1.0
## Q26 20 0.55 0.48 0.52 1.3
##
## MR1 MR2 MR3 MR4 MR5
## SS loadings 4.36 3.48 3.42 2.35 1.98
## Proportion Var 0.16 0.12 0.12 0.08 0.07
## Cumulative Var 0.16 0.28 0.40 0.49 0.56
## Proportion Explained 0.28 0.22 0.22 0.15 0.13
## Cumulative Proportion 0.28 0.50 0.72 0.87 1.00
##
## With factor correlations of
## MR1 MR2 MR3 MR4 MR5
## MR1 1.00 0.14 -0.55 -0.09 -0.11
## MR2 0.14 1.00 0.05 0.48 0.48
## MR3 -0.55 0.05 1.00 0.32 0.25
## MR4 -0.09 0.48 0.32 1.00 0.48
## MR5 -0.11 0.48 0.25 0.48 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 5 factors are sufficient.
##
## df null model = 378 with the objective function = 14.77 with Chi Square = 14603.18
## df of the model are 248 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic n.obs is 1000 with the empirical chi square 302.68 with prob < 0.01
## The total n.obs was 1000 with Likelihood Chi Square = 752.02 with prob < 3.4e-52
##
## Tucker Lewis Index of factoring reliability = 0.946
## RMSEA index = 0.045 and the 90 % confidence intervals are 0.041 0.049
## BIC = -961.1
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3 MR4 MR5
## Correlation of (regression) scores with factors 0.96 0.94 0.94 0.92 0.91
## Multiple R square of scores with factors 0.92 0.89 0.89 0.85 0.84
## Minimum correlation of possible factor scores 0.84 0.78 0.77 0.70 0.67Q13: I am nervous when partners get too close to me. Q5: Just when my partner starts to get close to me, I find myself pulling away. Q7: I get uncomfortable when a romantic partner wants to be very close. Q11: I want to get close to my partner, but I keep pulling back. Q17: My desire to be very close sometimes scares people away. Q23: I prefer not to be too close to romantic partners. Q21: I find it difficult to allow myself to depend on romantic partners. Q3: I am very comfortable being close to romantic partners. Q2: I worry about being abandoned. Q8: I worry a fair amount about losing my partner. Q22: I do not often worry about being abandoned. Q14: I worry about being alone. Q4: I worry a lot about my relationships. Q6: I worry that romantic partners won’t care about me as much as I care about them. Q34: When romantic partners disapprove of me, I feel really bad about myself. Q27: I usually discuss my problems and concerns with my partner. Q33: It helps to turn to my romantic partner in times of need. Q31: I don’t mind asking romantic partners for comfort, advice, or help. Q25: I tell my partner just about everything. Q35: I turn to my partner for many things, including comfort and reassurance. Q15: I feel comfortable sharing my private thoughts and feelings with my partner. Q32: I get frustrated if romantic partners are not available when I need them. Q30: I get frustrated when my partner is not around as much as I would like. Q36: I resent it when my partner spends time away from me. Q24: If I can’t get my partner to show interest in me, I get upset or angry. Q16: I try to avoid getting too close to my partner. Q12: I often want to merge completely with romantic partners, and this sometimes scares them away.
1. F1 -> Q13–Q3: Avoidance of Closeness
2. F2 -> Q2–Q34: Anxiety in Relationships
3. F3 -> Q27–Q15: Emotional Support
4. F4 -> Q32–Q24: Frustration
5. F5 -> Q16–Q26: Fear of Overdependencefa.diagram(factors)
ResidualMatrix <- factors$residual
Residuals <- as.matrix(ResidualMatrix[upper.tri(ResidualMatrix)])
head(Residuals)## [,1]
## [1,] -0.012859452
## [2,] -0.018541736
## [3,] 0.020347814
## [4,] 0.007553678
## [5,] 0.007739095
## [6,] 0.007872295HighResiduals <- abs(Residuals) > 0.05
head(HighResiduals)## [,1]
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSEsum(HighResiduals)/nrow(Residuals)## [1] 0.02645503print(factors$weights)## MR1 MR2 MR3 MR4 MR5
## Q2 0.010511213 0.271807126 -0.005011581 0.006492624 -0.042048791
## Q3 -0.064862475 0.016122334 0.054214187 -0.003843821 0.017410977
## Q4 0.026808692 0.140051574 0.002644869 0.017272210 0.056890878
## Q5 0.205039342 0.016292110 -0.009034183 0.016846951 -0.029246754
## Q6 0.017559296 0.116680365 -0.008585334 0.015963318 0.083536019
## Q7 0.157062341 0.002956260 0.005703222 -0.025868636 -0.024220608
## Q8 0.011384046 0.238182696 0.014751741 -0.014634297 0.034033686
## Q11 0.135617348 0.042031421 0.002709268 -0.013986665 0.021506076
## Q12 -0.007434004 0.002283164 -0.005184729 0.006507665 0.296980886
## Q13 0.226675243 0.018415916 0.030381888 -0.026387021 0.005008014
## Q14 -0.005264532 0.130839399 0.009587393 0.027040725 0.019034693
## Q15 -0.023351491 -0.004376624 0.121466668 -0.005891275 0.018124677
## Q16 -0.004963033 -0.011237605 0.013058461 -0.006830649 0.451930313
## Q17 0.152801282 -0.002779474 -0.042053093 0.032490895 0.029384435
## Q21 0.067630149 -0.005608144 -0.004279478 0.001799837 -0.008299079
## Q22 0.025970262 -0.163142686 0.043792207 -0.005023775 0.017848966
## Q23 0.150214445 -0.066923949 -0.041052370 0.036994448 -0.011330545
## Q24 0.015273851 0.045042730 -0.005518699 0.178259798 0.050119226
## Q25 -0.011437916 -0.030785930 0.192680263 -0.007356759 0.043839550
## Q26 0.008003323 0.025388487 -0.012530373 0.051904554 0.172155326
## Q27 0.007907600 -0.015493529 0.216002268 -0.011097465 0.001170816
## Q30 -0.013716548 0.006835173 0.030565198 0.336539843 0.003788176
## Q31 -0.014798400 -0.026689922 0.180803336 0.007722940 -0.010044227
## Q32 0.006226027 -0.023605399 0.018782411 0.329241198 -0.016883741
## Q33 0.010699859 0.009180826 0.194543758 0.026916888 -0.007503618
## Q34 0.003392178 0.065465056 0.018344031 0.034120218 -0.005336652
## Q35 -0.010894560 0.042693858 0.182575544 0.054490669 -0.021896725
## Q36 -0.005198202 0.007723861 -0.022180585 0.167189551 0.028493064head(factors$scores)## MR1 MR2 MR3 MR4 MR5
## [1,] -1.5533301 1.2195905 1.50651767 1.8785807 2.3800392
## [2,] -1.7727823 -2.3850747 1.62794431 0.8560867 1.1789166
## [3,] -0.7761031 1.3942420 0.01538614 0.4269966 0.8368599
## [4,] 1.7202695 0.2962311 -0.18835678 0.5556762 1.0609238
## [5,] 0.3518504 0.9992494 0.81860968 1.3715420 0.2584069
## [6,] 2.0173714 -0.9114805 0.23086347 0.9448649 -0.9741135mydata$F1_Avoidance <- factors$scores[ , 1]
mydata$F2_Anxiety <- factors$scores[ , 2]
mydata$F3_Emotional <- factors$scores[ , 3]
mydata$F4_Frustration <- factors$scores[ , 4]
mydata$F4_Fear <- factors$scores[ , 5]print(mydata[100, ])## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21
## 100 3 4 2 4 4 4 4 4 4 4 4 5 4 5 4 2 3 4 2 2 4
## Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36 F1_Avoidance
## 100 2 3 4 4 4 2 1 2 4 3 4 4 5 5 3 1.001538
## F2_Anxiety F3_Emotional F4_Frustration F4_Fear
## 100 0.843962 -0.05587213 0.8893779 0.7640959F4_Fear <- mydata[, c("Q16", "Q12", "Q26")]
head(F4_Fear)## Q16 Q12 Q26
## 1 5 5 5
## 2 5 5 1
## 3 4 3 2
## 4 4 4 3
## 5 3 2 3
## 6 2 1 1library(psych)
alpha(F4_Fear,
check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = F4_Fear, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.73 0.56 3.9 0.011 2.5 1 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.77 0.8 0.82
## Duhachek 0.77 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Q16 0.66 0.66 0.49 0.49 1.9 0.022 NA 0.49
## Q12 0.70 0.70 0.54 0.54 2.3 0.019 NA 0.54
## Q26 0.80 0.80 0.66 0.66 4.0 0.013 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Q16 1000 0.87 0.87 0.79 0.70 2.4 1.2
## Q12 1000 0.86 0.85 0.75 0.66 2.5 1.3
## Q26 1000 0.79 0.80 0.63 0.56 2.6 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## Q16 0.00 0.26 0.33 0.19 0.14 0.08 0
## Q12 0.00 0.26 0.31 0.19 0.14 0.09 0
## Q26 0.01 0.16 0.35 0.23 0.19 0.07 0Factor analysis was done on 36 survey questions (n = 1000). The KMO value was 0.94, showing the data was suitable for factor analysis, and all individual measures MSA were above 0.90. Bartlett’s test of sphericity was significant (p < 0.001), confirming the correlations between items.
Based on parallel analysis, 5 factors were chosen, and oblique rotation was used. Seven questions were removed due to low communalities and high Hoffmans index. The final 5 factors were:
• Factor 1 (λ̂1 = 4.36): Avoidance of Closeness
• Factor 2 (λ̂2 = 3.48): Anxiety in Relationships
• Factor 3 (λ̂3 = 3.42): Emotional Support
• Factor 4 (λ̂4 = 2.35): Frustration
• Factor 5 (λ̂5 = 1.98): Fear of OverdependenceThese 5 factors explain 56% of the variation in the data and provide insights into key aspects of close relationships.
Factor reliability (Cronbach’s alpha) was calculated for each F5, with acceptable reliability (α > 0.8).