Exploratory factor analysis
The number of principal components used for factor analysis was identified using a scree plot, the dashed line represents an ordinary least squares linear regression fit through points 4 to 20:

Exploratory factor analysis with OBLIMIN rotation:
## Principal Components Analysis
## Call: psych::principal(r = df.ordered.scaled, nfactors = 3, rotate = "oblimin",
## scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC3 TC2 h2 u2 com
## q09 0.45 -0.22 0.22 0.27 0.73 2.0
## q11 0.55 0.24 0.01 0.44 0.56 1.4
## q12 0.37 0.49 0.01 0.49 0.51 1.9
## q13 0.06 0.26 0.47 0.33 0.67 1.6
## q15 0.64 0.23 -0.14 0.52 0.48 1.4
## q16 0.76 -0.09 -0.04 0.53 0.47 1.0
## q21 0.68 -0.17 0.02 0.43 0.57 1.1
## q25 0.12 0.43 0.12 0.26 0.74 1.3
## q27 -0.07 0.59 0.01 0.33 0.67 1.0
## q28 -0.10 0.55 -0.15 0.29 0.71 1.2
## q29 0.04 0.47 0.13 0.26 0.74 1.2
## q30 -0.07 0.58 0.08 0.32 0.68 1.1
## q31 0.07 0.50 -0.03 0.28 0.72 1.0
## q33 -0.17 0.00 0.33 0.12 0.88 1.5
## q34 0.45 0.14 0.36 0.47 0.53 2.1
## q35 0.43 0.18 0.25 0.37 0.63 2.0
## q36_q43.p -0.08 -0.07 0.64 0.39 0.61 1.1
## q39_q43.p 0.02 -0.07 0.53 0.29 0.71 1.0
## q42 -0.02 0.04 0.71 0.50 0.50 1.0
## q44 0.05 -0.16 0.34 0.13 0.87 1.5
##
## TC1 TC3 TC2
## SS loadings 2.70 2.33 2.01
## Proportion Var 0.13 0.12 0.10
## Cumulative Var 0.13 0.25 0.35
## Proportion Explained 0.38 0.33 0.29
## Cumulative Proportion 0.38 0.71 1.00
##
## With component correlations of
## TC1 TC3 TC2
## TC1 1.00 0.29 0.21
## TC3 0.29 1.00 0.09
## TC2 0.21 0.09 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.08
## with the empirical chi square 1668.07 with prob < 2.1e-263
##
## Fit based upon off diagonal values = 0.8
Confirmatory factor analysis
Model specification:
## TC1 =~ q09 + q11 + q15 + q16 + q21 + q34 + q35
## TC2 =~ q13 + q33 + q36_q43.p + q39_q43.p + q42 + q44
## TC3 =~ q12 + q25 + q27 + q28 + q29 + q30 + q31
Commonly used fit indices:
## rmsea cfi srmr
## 0.062 0.945 0.076
Factors, corresponding items, and short descriptions:
## Factor Item Description
## 1 TC1 q09 Certification / Accreditation
## 2 TC1 q11 Laboratory improvement program
## 3 TC1 q15 Type of TAT monitored (if TAT used as KPI)
## 4 TC1 q16 Frequency of TAT measurement
## 5 TC1 q21 TAT monitored for very specific assay
## 6 TC1 q34 Use of basic IT solutions
## 7 TC1 q35 Use of advanced IT solutions
## 8 TC2 q13 Use of auto-validation
## 9 TC2 q33 Percent of digital ordering
## 10 TC2 q36_q43.p Primary tubes (clinical chemistry) per FTE
## 11 TC2 q39_q43.p Primary tubes (clinical chemistry) per square meter
## 12 TC2 q42 Use of pre-analytical automation
## 13 TC2 q44 Integration
## 14 TC3 q12 Key Performance Indicators
## 15 TC3 q25 Services provided to physicians
## 16 TC3 q27 Diagnostic committees
## 17 TC3 q28 Review of diagnostic pathways
## 18 TC3 q29 Utilization management
## 19 TC3 q30 Combining data digitally with other disciplines
## 20 TC3 q31 Measurement of patient outcomes
Rough estimate for internal consistency (overall and for subscales):
##
## Reliability analysis
## Call: psych::alpha(x = df.ordered.scaled)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.75 0.78 0.13 2.9 0.013 1.7e-17 0.41 0.12
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.75 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q09 0.74 0.74 0.77 0.13 2.9 0.014 0.013 0.12
## q11 0.72 0.72 0.75 0.12 2.6 0.014 0.012 0.11
## q12 0.72 0.72 0.75 0.12 2.6 0.015 0.011 0.11
## q13 0.73 0.73 0.76 0.13 2.7 0.014 0.013 0.11
## q15 0.73 0.73 0.75 0.12 2.6 0.014 0.011 0.11
## q16 0.73 0.73 0.75 0.12 2.7 0.014 0.012 0.12
## q21 0.73 0.73 0.76 0.13 2.8 0.014 0.013 0.12
## q25 0.74 0.74 0.77 0.13 2.8 0.014 0.013 0.12
## q27 0.74 0.74 0.77 0.13 2.8 0.014 0.013 0.12
## q28 0.75 0.75 0.77 0.14 3.0 0.013 0.013 0.13
## q29 0.74 0.74 0.77 0.13 2.8 0.014 0.013 0.11
## q30 0.74 0.74 0.77 0.13 2.8 0.014 0.013 0.12
## q31 0.74 0.74 0.77 0.13 2.8 0.014 0.013 0.12
## q33 0.76 0.76 0.78 0.14 3.1 0.013 0.012 0.13
## q34 0.72 0.72 0.75 0.12 2.6 0.015 0.011 0.11
## q35 0.73 0.73 0.75 0.12 2.7 0.014 0.012 0.11
## q36_q43.p 0.75 0.75 0.77 0.13 2.9 0.013 0.013 0.13
## q39_q43.p 0.75 0.75 0.77 0.13 2.9 0.013 0.013 0.13
## q42 0.73 0.73 0.76 0.13 2.8 0.014 0.013 0.11
## q44 0.75 0.75 0.78 0.14 3.0 0.013 0.012 0.13
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q09 759 0.36 0.36 0.30 0.252 2.1e-16 1
## q11 759 0.57 0.57 0.55 0.478 3.7e-17 1
## q12 759 0.58 0.58 0.57 0.486 -9.0e-18 1
## q13 759 0.46 0.46 0.41 0.356 -2.5e-17 1
## q15 759 0.54 0.54 0.53 0.443 9.7e-17 1
## q16 759 0.51 0.51 0.50 0.416 -6.4e-17 1
## q21 759 0.44 0.44 0.40 0.339 -9.0e-17 1
## q25 759 0.43 0.43 0.38 0.326 2.3e-17 1
## q27 759 0.38 0.38 0.31 0.268 -5.3e-17 1
## q28 759 0.27 0.27 0.19 0.158 3.1e-17 1
## q29 759 0.42 0.42 0.36 0.312 8.3e-17 1
## q30 759 0.39 0.39 0.32 0.277 -2.3e-16 1
## q31 759 0.40 0.40 0.34 0.294 1.8e-16 1
## q33 759 0.16 0.16 0.06 0.043 -1.7e-16 1
## q34 759 0.60 0.60 0.60 0.518 -1.6e-16 1
## q35 759 0.53 0.53 0.51 0.437 6.0e-17 1
## q36_q43.p 759 0.30 0.30 0.22 0.182 4.6e-17 1
## q39_q43.p 759 0.30 0.30 0.22 0.189 1.3e-17 1
## q42 759 0.43 0.43 0.39 0.328 9.8e-17 1
## q44 759 0.21 0.21 0.11 0.089 2.6e-16 1
##
## Reliability analysis
## Call: psych::alpha(x = df.ordered.scaled[, colnames(df.ordered.scaled) %in%
## interpretation$Item[interpretation$Factor == "TC1"]])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.73 0.73 0.28 2.7 0.015 1.3e-17 0.62 0.27
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.73 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q09 0.73 0.73 0.73 0.32 2.8 0.015 0.010 0.30
## q11 0.69 0.69 0.69 0.27 2.2 0.018 0.017 0.25
## q15 0.69 0.69 0.68 0.28 2.3 0.017 0.010 0.26
## q16 0.68 0.68 0.67 0.26 2.1 0.018 0.011 0.26
## q21 0.70 0.70 0.70 0.28 2.4 0.017 0.015 0.28
## q34 0.68 0.68 0.67 0.27 2.2 0.018 0.013 0.26
## q35 0.70 0.70 0.68 0.28 2.3 0.017 0.012 0.27
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q09 759 0.49 0.49 0.34 0.28 2.1e-16 1
## q11 759 0.65 0.65 0.56 0.49 3.7e-17 1
## q15 759 0.63 0.63 0.55 0.45 9.7e-17 1
## q16 759 0.67 0.67 0.61 0.51 -6.4e-17 1
## q21 759 0.60 0.60 0.50 0.42 -9.0e-17 1
## q34 759 0.66 0.66 0.59 0.50 -1.6e-16 1
## q35 759 0.62 0.62 0.54 0.45 6.0e-17 1
##
## Reliability analysis
## Call: psych::alpha(x = df.ordered.scaled[, colnames(df.ordered.scaled) %in%
## interpretation$Item[interpretation$Factor == "TC2"]])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.49 0.49 0.46 0.14 0.94 0.029 3.7e-17 0.53 0.15
##
## lower alpha upper 95% confidence boundaries
## 0.43 0.49 0.54
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q13 0.45 0.45 0.41 0.140 0.82 0.032 0.0082 0.146
## q33 0.51 0.51 0.47 0.171 1.03 0.028 0.0091 0.168
## q36_q43.p 0.39 0.39 0.37 0.114 0.64 0.035 0.0104 0.118
## q39_q43.p 0.44 0.44 0.41 0.135 0.78 0.032 0.0092 0.146
## q42 0.34 0.34 0.32 0.095 0.52 0.038 0.0080 0.079
## q44 0.49 0.49 0.45 0.159 0.94 0.029 0.0110 0.160
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q13 759 0.51 0.51 0.34 0.23 -2.5e-17 1
## q33 759 0.42 0.42 0.16 0.11 -1.7e-16 1
## q36_q43.p 759 0.60 0.60 0.48 0.33 4.6e-17 1
## q39_q43.p 759 0.53 0.53 0.37 0.25 1.3e-17 1
## q42 759 0.66 0.66 0.59 0.41 9.8e-17 1
## q44 759 0.46 0.46 0.23 0.16 2.6e-16 1
##
## Reliability analysis
## Call: psych::alpha(x = df.ordered.scaled[, colnames(df.ordered.scaled) %in%
## interpretation$Item[interpretation$Factor == "TC3"]])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.62 0.62 0.6 0.19 1.7 0.021 5.1e-18 0.55 0.21
##
## lower alpha upper 95% confidence boundaries
## 0.58 0.62 0.66
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## q12 0.56 0.56 0.53 0.18 1.3 0.025 0.0035 0.17
## q25 0.60 0.60 0.56 0.20 1.5 0.023 0.0032 0.21
## q27 0.59 0.59 0.55 0.19 1.4 0.023 0.0040 0.21
## q28 0.60 0.60 0.57 0.20 1.5 0.022 0.0025 0.21
## q29 0.59 0.59 0.55 0.19 1.4 0.023 0.0035 0.21
## q30 0.58 0.58 0.55 0.19 1.4 0.023 0.0043 0.18
## q31 0.58 0.58 0.55 0.19 1.4 0.023 0.0042 0.21
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## q12 759 0.62 0.62 0.52 0.41 -9.0e-18 1
## q25 759 0.53 0.53 0.40 0.31 2.3e-17 1
## q27 759 0.55 0.55 0.42 0.33 -5.3e-17 1
## q28 759 0.51 0.51 0.36 0.28 3.1e-17 1
## q29 759 0.55 0.55 0.43 0.33 8.3e-17 1
## q30 759 0.56 0.56 0.43 0.34 -2.3e-16 1
## q31 759 0.56 0.56 0.44 0.34 1.8e-16 1
Fitted values
To aid in interpretation, fitted values for each factor were scaled so that they had a median of 100 and interquartile range (IQR) of 40.
Minimum, 1st quartile, median, mean, 3rd quartile, and maximum of fitted values per factor:
## TC1 TC2 TC3
## Min. 25.05849 53.86188 38.01975
## 1st Qu. 79.64756 84.13787 79.32972
## Median 100.00000 100.00000 100.00000
## Mean 99.02431 105.42738 100.39185
## 3rd Qu. 119.64756 124.13787 119.32972
## Max. 166.04166 346.02059 177.36106
Standard deviation of fitted values per factor:
## TC1 TC2 TC3
## 26.90987 28.73592 28.12970
Interquartile range (IQR) of fitted values per factor:
## TC1 TC2 TC3
## 40 40 40