Faculty Survey Analysis

Section 1: Sense of belonging

This report contains two sets of sense of belong items including institution and department.(The overall item wording: How much do you agree or disagree with the following statements?; Seven-point-scale)

SOB Means and Alpha

Table 1: Descriptive Statistics of Institutional and Departmental Variables
Item Wording Mean SDs
sobinst_overall I feel a sense of belonging in this institution 4.989 1.571
sobinst_enthus I am enthusiastic about this institution 5.292 1.466
sobinst_comm I feel that I am member of the institutional community 4.847 1.63
sobinst_happy I am happy to work at this institution 5.588 1.331
sobinst_commself I see myself as part of the institutional community 5.042 1.602
sobinst_rep I am happy to work at this institution 4.589 1.746
sobdept_overall I feel a sense of belonging in this department 5.167 1.581
sobdept_enthus I am enthusiastic about this department 5.416 1.416
sobdept_comm I feel that I am member of the departmental community 5.179 1.56
sobdept_happy I am happy to work at this department 5.628 1.346
sobdept_commself I see myself as part of the departmental community 5.228 1.564
sobdept_rep I am happy to work at this department 4.931 1.702

Interpreting the SOB Means and SDs

The mean scores for the institutional variables ranged from 4.59 to 5.59, with the highest mean reported for the item “I am happy to work at this institution” (M = 5.59), and the lowest mean reported for “I feel that I am a member of the institutional community” (M = 4.85). Similarly, for the departmental variables, the mean scores ranged from 5.17 to 5.63, with the highest mean reported for “I am happy to work at this department” (M = 5.63), and the lowest mean reported for “I feel that I am a member of the departmental community” (M = 5.18).

The standard deviations for the institutional variables ranged from 1.33 to 1.75, indicating variability in responses around the mean. The highest variability was observed for the item “I am happy to work at this institution” (SD = 1.75), and the lowest variability was observed for “I feel a sense of belonging in this institution” (SD = 1.57). Similarly, for the departmental variables, the standard deviations ranged from 1.35 to 1.70, with the highest variability found for “I am happy to work at this department” (SD = 1.70), and the lowest variability found for “I am enthusiastic about this department” (SD = 1.42).

These descriptive statistics provide valuable insights into the participants’ responses, helping to understand the central tendency and spread of their perceptions related to institutional and departmental aspects. The results can aid in identifying areas where employees have relatively higher or lower agreement, facilitating targeted interventions to enhance institutional and departmental experiences.

Interpreting SOB Alphas

The overall internal consistency of the scale, as measured by Cronbach’s alpha, was found to be high, with a value of 0.942. This indicates a strong level of reliability in the measurement of the construct. The bootstrap 95% confidence interval (CI) based on 1000 samples ranged from 0.920 to 0.957, suggesting that we can be reasonably confident that the true alpha value lies within this interval.

Furthermore, when considering only the institutional items, the alpha coefficient was 0.93, indicating a high level of internal consistency for this subset of items. The bootstrap 95% CI for the institutional items ranged from 0.904 to 0.948, providing additional evidence of the scale’s reliability within this specific domain.

Likewise, the departmental items also demonstrated a strong internal consistency, with a Cronbach’s alpha of 0.936. The bootstrap 95% CI for the departmental items ranged from 0.918 to 0.949, further reinforcing the reliability of this subset of items.

In conclusion, both the overall scale and its distinct components (institutional and departmental items) exhibit robust internal consistency, as evidenced by their high alpha coefficients and narrow bootstrap confidence intervals. These findings enhance our confidence in using the scale to measure the constructs of interest and provide a solid foundation for subsequent data analysis and interpretation.

SoB Correlation Matrix

Correlation Matrix of the institutional variables
sobinst_overall sobinst_enthus sobinst_comm sobinst_happy sobinst_commself sobinst_rep
sobinst_overall 1.0000000 0.7191802 0.8178377 0.6992850 0.7906528 0.6814912
sobinst_enthus 0.7191802 1.0000000 0.6833801 0.8210182 0.6559415 0.7714521
sobinst_comm 0.8178377 0.6833801 1.0000000 0.6888627 0.8482654 0.6566069
sobinst_happy 0.6992850 0.8210182 0.6888627 1.0000000 0.6711249 0.7155433
sobinst_commself 0.7906528 0.6559415 0.8482654 0.6711249 1.0000000 0.6210646
sobinst_rep 0.6814912 0.7714521 0.6566069 0.7155433 0.6210646 1.0000000
Correlation Matrix of the departmental variables
sobdept_overall sobdept_enthus sobdept_comm sobdept_happy sobdept_commself sobdept_rep
sobdept_overall 1.0000000 0.7647380 0.8186177 0.6714109 0.7895869 0.6473412
sobdept_enthus 0.7647380 1.0000000 0.6922527 0.8610738 0.6615006 0.7790166
sobdept_comm 0.8186177 0.6922527 1.0000000 0.6304192 0.8480536 0.5963866
sobdept_happy 0.6714109 0.8610738 0.6304192 1.0000000 0.6330523 0.7455328
sobdept_commself 0.7895869 0.6615006 0.8480536 0.6330523 1.0000000 0.5883812
sobdept_rep 0.6473412 0.7790166 0.5963866 0.7455328 0.5883812 1.0000000

The correlation matrix reveals significant positive correlations between institutional sense of belonging and employee satisfaction measures. Specifically, there were positive correlations between institutional sense of belonging and enthusiasm (r = 0.719, p < 0.001), community involvement (r = 0.818, p < 0.001), happiness (r = 0.699, p < 0.001), and self-reported representation (r = 0.681, p < 0.001). Additionally, a strong positive correlation was found between community involvement and happiness (r = 0.689, p < 0.001).

These findings suggest that employees who feel a stronger sense of belonging to the institution tend to report higher levels of enthusiasm, community involvement, and happiness at work. Moreover, employees who perceive themselves as part of the institutional community also report higher levels of happiness. These results highlight the importance of fostering a supportive and inclusive work environment to enhance employee satisfaction and well-being.

Compared to the institutional level, the departmental level exhibits similar trends in correlations between variables. However, some minor differences are noted. The correlation between enthusiasm and happiness at work appears to be stronger at the departmental level (r=0.861) than at the institutional level (r=0.821), implying that department-specific factors might have a more direct impact on employees’ happiness.

On the other hand, the sense of belonging in the department (sobdept_overall) seems to have a slightly weaker correlation with happiness in the department (sobdept_happy; r=0.671) than the corresponding correlation at the institutional level (sobinst_overall and sobinst_happy; r=0.699). This might indicate that while the sense of belonging is important at both levels, other factors may have a slightly higher influence on happiness at the departmental level.

Section 2: PIP variables

PIP variables are instructional practices scales. The overarching item wording: Please indicate the degree to which the following statements are descriptive of your teaching in this course.; Five-point-scale)

PIP Means and Alpha

Table: Descriptive Statistics of Instructional Practices Scales
Item Wording Mean SDs
pip_questions I frequently ask students to respond to questions during class time 4.16 1.21
pip_integrate I require students to make connections between related ideas or concepts when completing assignments 4.15 0.95
pip_syllspecific My syllabus contains the specific topics that will be covered in every class session 4.13 1.11
pip_formassess I give students frequent assignments worth a small portion of their grade 4.01 1.1
pip_discuss I structure class so that students regularly talk with one another about course concepts 3.73 1.21
pip_connectlives I design activities that connect course content to my students’ lives and future work 3.72 1.24
pip_guidequest I use student questions and comments to determine the focus and direction of classroom discussion 3.72 1.15
pip_manysol I structure problems so that students consider multiple approaches to finding a solution 3.63 1.22
pip_solprocess I provide time for students to reflect about the processes they use to solve problems 3.62 1.2
pip_groups I require students to work together in small groups 3.47 1.43
pip_guideassess I use student assessment results to guide the direction of my instruction during the semester 3.46 1.18
pip_feedbackimm I provide students with immediate feedback on their work during class (e.g., student response systems, short quizzes) 3.42 1.35
pip_notes My class sessions are structured to give students a good set of notes 3.42 1.29
pip_testdefine My test questions focus on important facts and definitions from the course 3.42 1.46
pip_lecture I guide students through major topics as they listen and take notes 3.4 1.28
pip_assume I structure my course with the assumption that most of the students have little useful knowledge of the topics 3.32 1.26
pip_testoneright My test questions contain well-defined problems with one correct solution 3.22 1.42
pip_visuals I have students use a variety of means (models, drawings, graphs, symbols, simulations, etc.) to represent phenomena 3.18 1.37
pip_testapply My test questions require students to apply course concepts to unfamiliar situations 3.1 1.44
pip_discussprelect I structure class so that students explore or discuss their understanding of new concepts before formal instruction 3.09 1.26
pip_discussprobs I structure class so that students discuss the difficulties they have with this subject with other students 3.01 1.28
pip_feedbackdraft I provide feedback on student assignments without assigning a formal grade 2.77 1.39
pip_discusscritique I structure class so that students constructively criticize one another’s ideas 2.7 1.36
pip_curve I adjust student scores (e.g. curve) when necessary to reflect a proper distribution of grades 2.31 1.46

Interpreting the PIP Means and SDs

The data in Table X displays the descriptive statistics of various instructional practice scales, each represented by a different teaching method or practice, as measured by a Likert scale. This Likert scale ranges from 1 to 5, with 1 being “strongly disagree” and 5 being “strongly agree”.

In general, the means of the instructional practices ranged from 2.31 to 4.16. The lowest mean score was observed for the practice of adjusting student scores (M = 2.31, SD = 1.46), indicating that, on average, instructors disagreed with this practice. In contrast, the highest mean score was found for the practice of frequently asking students to respond to questions during class time (M = 4.16, SD = 1.21). This suggests that most instructors agreed or strongly agreed with the use of this instructional practice.

On the other hand, instructional practices related to students’ peer-to-peer interaction like students constructively criticizing one another’s ideas (M = 2.70, SD = 1.36) and discussing difficulties with the subject among themselves (M = 3.01, SD = 1.28) had comparatively lower mean scores, pointing to lesser agreement among the instructors about their use.

The standard deviations across all the instructional practices suggest a considerable variation in responses. This indicates a diversity in instructional practices among educators, with no single approach garnering universal agreement or disagreement.

Interpreting PIP Alphas

The 24-item scale demonstrated high reliability, with a Cronbach’s alpha of .853. A bootstrap 95% confidence interval (CI), based on 1000 samples, further confirmed the scale’s reliability, with a range of .815 to .879. This suggests that the items on the scale are consistently measuring the same construct.

PIP correlation matrix

pip_data <- Data[,c(224:247)]
kable(cor(pip_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Instructional Practices Scales")
Table: Correlation Matrix of the Instructional Practices Scales
pip_lecture pip_connectlives pip_syllspecific pip_feedbackimm pip_assume pip_guideassess pip_questions pip_guidequest pip_visuals pip_discussprelect pip_notes pip_discuss pip_discusscritique pip_discussprobs pip_groups pip_manysol pip_solprocess pip_formassess pip_integrate pip_feedbackdraft pip_testdefine pip_testapply pip_testoneright pip_curve
pip_lecture 1.0000000 0.3097794 0.2364272 0.2050272 0.3688008 0.2563442 0.3873783 0.1559230 0.1535956 0.0445064 0.6133921 0.0301328 -0.1302813 0.0015637 0.0955169 0.0239495 -0.0120541 0.1945444 0.1259397 0.0150709 0.4141664 0.3433006 0.4391668 0.1908633
pip_connectlives 0.3097794 1.0000000 0.2531311 0.1570003 0.1028380 0.2139706 0.3137087 0.3016150 0.2488668 0.3826746 0.1838000 0.2952533 0.2835546 0.2417406 0.2790794 0.3500231 0.2463969 0.1141560 0.3327762 0.0689608 0.1407872 0.2841526 0.0145223 0.1685372
pip_syllspecific 0.2364272 0.2531311 1.0000000 0.2307832 0.1162888 0.2385267 0.0966994 0.1655061 0.1993728 0.1805986 0.2407320 0.1022717 0.1185783 0.1444475 0.0711831 0.1421878 0.0913769 0.0280483 0.1418842 0.0405272 0.2275348 0.1474402 0.1656086 0.1421930
pip_feedbackimm 0.2050272 0.1570003 0.2307832 1.0000000 0.2377364 0.1828847 0.1934944 0.1345249 0.3098342 0.2544058 0.2489220 0.0881336 0.0643243 0.1367950 0.1078748 0.1088760 0.1681619 0.2037132 0.0815472 0.2975421 0.1820095 0.1728612 0.2200011 0.0762853
pip_assume 0.3688008 0.1028380 0.1162888 0.2377364 1.0000000 0.2572986 0.1500830 0.1386546 0.1840826 0.0399568 0.3546542 -0.0017304 -0.0053817 -0.0410207 0.0168830 0.0163119 0.0652212 0.1703487 0.0641663 0.0832633 0.2807167 0.2346216 0.2906664 0.1739290
pip_guideassess 0.2563442 0.2139706 0.2385267 0.1828847 0.2572986 1.0000000 0.2781095 0.4071944 0.2188318 0.2799991 0.2154289 0.2762482 0.1934450 0.2946533 0.3300541 0.1199520 0.2609194 0.1239639 0.2219470 0.2713801 0.2179436 0.1720095 0.2067162 0.1752723
pip_questions 0.3873783 0.3137087 0.0966994 0.1934944 0.1500830 0.2781095 1.0000000 0.4730798 0.1795610 0.2893135 0.3162877 0.4684525 0.2585651 0.3110885 0.4142517 0.2374910 0.2196692 0.1704353 0.2353032 0.2130693 0.1114358 0.2263514 0.0836558 0.0704205
pip_guidequest 0.1559230 0.3016150 0.1655061 0.1345249 0.1386546 0.4071944 0.4730798 1.0000000 0.2069073 0.3424083 0.1783405 0.3571262 0.3171292 0.3788685 0.1835003 0.3240943 0.3525602 0.1290392 0.2910828 0.2313968 0.0388352 0.2065462 0.0128102 0.0674812
pip_visuals 0.1535956 0.2488668 0.1993728 0.3098342 0.1840826 0.2188318 0.1795610 0.2069073 1.0000000 0.3434826 0.2210141 0.2526018 0.2152553 0.2291259 0.1967389 0.3694822 0.2268813 0.2396634 0.2414896 0.2171881 0.1026290 0.1709787 0.2145947 0.1825655
pip_discussprelect 0.0445064 0.3826746 0.1805986 0.2544058 0.0399568 0.2799991 0.2893135 0.3424083 0.3434826 1.0000000 0.1858523 0.3939488 0.3874146 0.4418866 0.3032078 0.4518238 0.4297186 0.1560203 0.2784892 0.3449357 0.0030147 0.1667502 -0.0772172 0.1400421
pip_notes 0.6133921 0.1838000 0.2407320 0.2489220 0.3546542 0.2154289 0.3162877 0.1783405 0.2210141 0.1858523 1.0000000 0.0981875 -0.0075513 0.1141254 0.0541020 0.1624519 0.1717896 0.1979740 0.2158293 0.1296049 0.3674745 0.3328005 0.4202402 0.1780630
pip_discuss 0.0301328 0.2952533 0.1022717 0.0881336 -0.0017304 0.2762482 0.4684525 0.3571262 0.2526018 0.3939488 0.0981875 1.0000000 0.5588710 0.5555694 0.5301893 0.3815014 0.2698352 0.1172899 0.2402014 0.2250745 -0.0086723 0.1701779 -0.0285104 0.0584504
pip_discusscritique -0.1302813 0.2835546 0.1185783 0.0643243 -0.0053817 0.1934450 0.2585651 0.3171292 0.2152553 0.3874146 -0.0075513 0.5588710 1.0000000 0.6198743 0.2633194 0.4533576 0.3847531 0.0015295 0.2281149 0.3167569 -0.0083721 0.0370478 -0.2230429 -0.0069173
pip_discussprobs 0.0015637 0.2417406 0.1444475 0.1367950 -0.0410207 0.2946533 0.3110885 0.3788685 0.2291259 0.4418866 0.1141254 0.5555694 0.6198743 1.0000000 0.3507021 0.4580786 0.4976946 0.1013245 0.2933314 0.3844869 -0.0217853 0.0803077 -0.1328119 0.0850742
pip_groups 0.0955169 0.2790794 0.0711831 0.1078748 0.0168830 0.3300541 0.4142517 0.1835003 0.1967389 0.3032078 0.0541020 0.5301893 0.2633194 0.3507021 1.0000000 0.2758339 0.1886113 0.1385119 0.2090974 0.2295942 -0.0024476 0.1415333 0.0405064 0.0275296
pip_manysol 0.0239495 0.3500231 0.1421878 0.1088760 0.0163119 0.1199520 0.2374910 0.3240943 0.3694822 0.4518238 0.1624519 0.3815014 0.4533576 0.4580786 0.2758339 1.0000000 0.5487391 0.1404092 0.3382114 0.3005612 -0.0202287 0.1909410 -0.0797194 0.0683235
pip_solprocess -0.0120541 0.2463969 0.0913769 0.1681619 0.0652212 0.2609194 0.2196692 0.3525602 0.2268813 0.4297186 0.1717896 0.2698352 0.3847531 0.4976946 0.1886113 0.5487391 1.0000000 0.2078469 0.3438729 0.4352269 0.0215763 0.0752733 -0.0475724 0.0751074
pip_formassess 0.1945444 0.1141560 0.0280483 0.2037132 0.1703487 0.1239639 0.1704353 0.1290392 0.2396634 0.1560203 0.1979740 0.1172899 0.0015295 0.1013245 0.1385119 0.1404092 0.2078469 1.0000000 0.3626434 0.0877985 0.2397505 0.1796576 0.2729502 0.0808582
pip_integrate 0.1259397 0.3327762 0.1418842 0.0815472 0.0641663 0.2219470 0.2353032 0.2910828 0.2414896 0.2784892 0.2158293 0.2402014 0.2281149 0.2933314 0.2090974 0.3382114 0.3438729 0.3626434 1.0000000 0.1907777 0.1235155 0.1912283 -0.0139620 0.0372350
pip_feedbackdraft 0.0150709 0.0689608 0.0405272 0.2975421 0.0832633 0.2713801 0.2130693 0.2313968 0.2171881 0.3449357 0.1296049 0.2250745 0.3167569 0.3844869 0.2295942 0.3005612 0.4352269 0.0877985 0.1907777 1.0000000 0.0479517 0.0974384 -0.0805587 0.0390970
pip_testdefine 0.4141664 0.1407872 0.2275348 0.1820095 0.2807167 0.2179436 0.1114358 0.0388352 0.1026290 0.0030147 0.3674745 -0.0086723 -0.0083721 -0.0217853 -0.0024476 -0.0202287 0.0215763 0.2397505 0.1235155 0.0479517 1.0000000 0.3251097 0.6084653 0.1949309
pip_testapply 0.3433006 0.2841526 0.1474402 0.1728612 0.2346216 0.1720095 0.2263514 0.2065462 0.1709787 0.1667502 0.3328005 0.1701779 0.0370478 0.0803077 0.1415333 0.1909410 0.0752733 0.1796576 0.1912283 0.0974384 0.3251097 1.0000000 0.3550872 0.3280419
pip_testoneright 0.4391668 0.0145223 0.1656086 0.2200011 0.2906664 0.2067162 0.0836558 0.0128102 0.2145947 -0.0772172 0.4202402 -0.0285104 -0.2230429 -0.1328119 0.0405064 -0.0797194 -0.0475724 0.2729502 -0.0139620 -0.0805587 0.6084653 0.3550872 1.0000000 0.2847250
pip_curve 0.1908633 0.1685372 0.1421930 0.0762853 0.1739290 0.1752723 0.0704205 0.0674812 0.1825655 0.1400421 0.1780630 0.0584504 -0.0069173 0.0850742 0.0275296 0.0683235 0.0751074 0.0808582 0.0372350 0.0390970 0.1949309 0.3280419 0.2847250 1.0000000

EFA analysis for PIP variables

# Perform EFA
efamodel_2 <- fa(r=pip_data, nfactors=2, rotate="varimax")
efamodel_3 <- fa(r=pip_data, nfactors=3, rotate="varimax")
efamodel_4 <- fa(r=pip_data, nfactors=4, rotate="varimax")
fa.diagram(efamodel_2)

fa.diagram(efamodel_3)

fa.diagram(efamodel_4)

print.psych(efamodel_2)
## Factor Analysis using method =  minres
## Call: fa(r = pip_data, nfactors = 2, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                       MR1   MR2   h2   u2 com
## pip_lecture          0.02  0.72 0.52 0.48 1.0
## pip_connectlives     0.42  0.29 0.26 0.74 1.8
## pip_syllspecific     0.21  0.31 0.14 0.86 1.8
## pip_feedbackimm      0.22  0.34 0.17 0.83 1.7
## pip_assume           0.02  0.48 0.23 0.77 1.0
## pip_guideassess      0.40  0.35 0.28 0.72 2.0
## pip_questions        0.48  0.31 0.33 0.67 1.7
## pip_guidequest       0.55  0.20 0.34 0.66 1.3
## pip_visuals          0.39  0.27 0.22 0.78 1.8
## pip_discussprelect   0.64  0.10 0.42 0.58 1.1
## pip_notes            0.15  0.66 0.46 0.54 1.1
## pip_discuss          0.68  0.05 0.47 0.53 1.0
## pip_discusscritique  0.70 -0.14 0.50 0.50 1.1
## pip_discussprobs     0.75 -0.04 0.56 0.44 1.0
## pip_groups           0.50  0.11 0.26 0.74 1.1
## pip_manysol          0.67  0.05 0.46 0.54 1.0
## pip_solprocess       0.63  0.07 0.40 0.60 1.0
## pip_formassess       0.19  0.36 0.17 0.83 1.5
## pip_integrate        0.43  0.22 0.23 0.77 1.5
## pip_feedbackdraft    0.48  0.07 0.24 0.76 1.0
## pip_testdefine      -0.05  0.62 0.39 0.61 1.0
## pip_testapply        0.16  0.50 0.28 0.72 1.2
## pip_testoneright    -0.17  0.73 0.57 0.43 1.1
## pip_curve            0.10  0.34 0.12 0.88 1.2
## 
##                        MR1  MR2
## SS loadings           4.67 3.35
## Proportion Var        0.19 0.14
## Cumulative Var        0.19 0.33
## Proportion Explained  0.58 0.42
## Cumulative Proportion 0.58 1.00
## 
## Mean item complexity =  1.3
## Test of the hypothesis that 2 factors are sufficient.
## 
## df null model =  276  with the objective function =  8.4 with Chi Square =  6295.97
## df of  the model are 229  and the objective function was  2.17 
## 
## The root mean square of the residuals (RMSR) is  0.06 
## The df corrected root mean square of the residuals is  0.06 
## 
## The harmonic n.obs is  271 with the empirical chi square  482.36  with prob <  3.5e-20 
## The total n.obs was  759  with Likelihood Chi Square =  1622.96  with prob <  3e-208 
## 
## Tucker Lewis Index of factoring reliability =  0.72
## RMSEA index =  0.09  and the 90 % confidence intervals are  0.086 0.094
## BIC =  104.23
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy             
##                                                    MR1  MR2
## Correlation of (regression) scores with factors   0.94 0.92
## Multiple R square of scores with factors          0.88 0.85
## Minimum correlation of possible factor scores     0.77 0.69
print.psych(efamodel_3)
## Factor Analysis using method =  minres
## Call: fa(r = pip_data, nfactors = 3, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                       MR1   MR2   MR3   h2   u2 com
## pip_lecture         -0.13  0.70  0.26 0.58 0.42 1.3
## pip_connectlives     0.33  0.27  0.29 0.26 0.74 2.9
## pip_syllspecific     0.18  0.30  0.11 0.14 0.86 2.0
## pip_feedbackimm      0.24  0.35  0.06 0.18 0.82 1.8
## pip_assume           0.01  0.48  0.05 0.23 0.77 1.0
## pip_guideassess      0.26  0.32  0.34 0.29 0.71 2.9
## pip_questions        0.16  0.25  0.69 0.56 0.44 1.4
## pip_guidequest       0.38  0.17  0.44 0.36 0.64 2.3
## pip_visuals          0.42  0.28  0.08 0.27 0.73 1.8
## pip_discussprelect   0.60  0.10  0.25 0.43 0.57 1.4
## pip_notes            0.12  0.65  0.14 0.46 0.54 1.2
## pip_discuss          0.42 -0.01  0.64 0.58 0.42 1.7
## pip_discusscritique  0.58 -0.16  0.36 0.50 0.50 1.9
## pip_discussprobs     0.61 -0.06  0.42 0.55 0.45 1.8
## pip_groups           0.28  0.06  0.49 0.33 0.67 1.6
## pip_manysol          0.71  0.05  0.17 0.53 0.47 1.1
## pip_solprocess       0.70  0.08  0.10 0.51 0.49 1.1
## pip_formassess       0.22  0.37  0.02 0.19 0.81 1.7
## pip_integrate        0.43  0.22  0.15 0.25 0.75 1.8
## pip_feedbackdraft    0.47  0.07  0.16 0.25 0.75 1.3
## pip_testdefine      -0.01  0.63 -0.02 0.40 0.60 1.0
## pip_testapply        0.12  0.49  0.14 0.28 0.72 1.3
## pip_testoneright    -0.14  0.74 -0.05 0.57 0.43 1.1
## pip_curve            0.11  0.34  0.02 0.13 0.87 1.2
## 
##                        MR1  MR2  MR3
## SS loadings           3.46 3.26 2.12
## Proportion Var        0.14 0.14 0.09
## Cumulative Var        0.14 0.28 0.37
## Proportion Explained  0.39 0.37 0.24
## Cumulative Proportion 0.39 0.76 1.00
## 
## Mean item complexity =  1.6
## Test of the hypothesis that 3 factors are sufficient.
## 
## df null model =  276  with the objective function =  8.4 with Chi Square =  6295.97
## df of  the model are 207  and the objective function was  1.77 
## 
## The root mean square of the residuals (RMSR) is  0.05 
## The df corrected root mean square of the residuals is  0.06 
## 
## The harmonic n.obs is  271 with the empirical chi square  357.09  with prob <  4.3e-10 
## The total n.obs was  759  with Likelihood Chi Square =  1322.36  with prob <  1e-161 
## 
## Tucker Lewis Index of factoring reliability =  0.752
## RMSEA index =  0.084  and the 90 % confidence intervals are  0.08 0.089
## BIC =  -50.47
## Fit based upon off diagonal values = 0.96
## Measures of factor score adequacy             
##                                                    MR1  MR2  MR3
## Correlation of (regression) scores with factors   0.89 0.92 0.84
## Multiple R square of scores with factors          0.80 0.84 0.70
## Minimum correlation of possible factor scores     0.59 0.69 0.40
print.psych(efamodel_4)
## Factor Analysis using method =  minres
## Call: fa(r = pip_data, nfactors = 4, rotate = "varimax")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                       MR2   MR3   MR1   MR4   h2   u2 com
## pip_lecture          0.72  0.21 -0.07 -0.10 0.57 0.43 1.2
## pip_connectlives     0.27  0.31  0.44 -0.18 0.40 0.60 3.0
## pip_syllspecific     0.30  0.13  0.17  0.07 0.14 0.86 2.2
## pip_feedbackimm      0.34  0.09  0.10  0.36 0.26 0.74 2.3
## pip_assume           0.48  0.04 -0.02  0.13 0.25 0.75 1.2
## pip_guideassess      0.32  0.39  0.12  0.25 0.33 0.67 2.9
## pip_questions        0.27  0.68  0.08  0.05 0.54 0.46 1.3
## pip_guidequest       0.16  0.48  0.27  0.17 0.36 0.64 2.1
## pip_visuals          0.25  0.13  0.38  0.19 0.26 0.74 2.6
## pip_discussprelect   0.07  0.33  0.51  0.23 0.43 0.57 2.2
## pip_notes            0.65  0.13  0.12  0.07 0.46 0.54 1.2
## pip_discuss         -0.01  0.69  0.31  0.08 0.59 0.41 1.4
## pip_discusscritique -0.18  0.45  0.46  0.20 0.49 0.51 2.7
## pip_discussprobs    -0.09  0.51  0.44  0.33 0.57 0.43 2.8
## pip_groups           0.07  0.53  0.22  0.05 0.33 0.67 1.4
## pip_manysol          0.02  0.25  0.72  0.13 0.59 0.41 1.3
## pip_solprocess       0.04  0.20  0.58  0.36 0.51 0.49 1.9
## pip_formassess       0.36  0.04  0.25  0.05 0.20 0.80 1.9
## pip_integrate        0.20  0.18  0.49 -0.01 0.31 0.69 1.6
## pip_feedbackdraft    0.04  0.23  0.25  0.58 0.45 0.55 1.7
## pip_testdefine       0.63 -0.04  0.01  0.04 0.40 0.60 1.0
## pip_testapply        0.49  0.13  0.18 -0.05 0.30 0.70 1.4
## pip_testoneright     0.74 -0.09 -0.11  0.02 0.57 0.43 1.1
## pip_curve            0.34  0.03  0.13  0.02 0.13 0.87 1.3
## 
##                        MR2  MR3  MR1  MR4
## SS loadings           3.23 2.61 2.58 1.03
## Proportion Var        0.13 0.11 0.11 0.04
## Cumulative Var        0.13 0.24 0.35 0.39
## Proportion Explained  0.34 0.28 0.27 0.11
## Cumulative Proportion 0.34 0.62 0.89 1.00
## 
## Mean item complexity =  1.8
## Test of the hypothesis that 4 factors are sufficient.
## 
## df null model =  276  with the objective function =  8.4 with Chi Square =  6295.97
## df of  the model are 186  and the objective function was  1.57 
## 
## The root mean square of the residuals (RMSR) is  0.04 
## The df corrected root mean square of the residuals is  0.05 
## 
## The harmonic n.obs is  271 with the empirical chi square  288.61  with prob <  2.1e-06 
## The total n.obs was  759  with Likelihood Chi Square =  1172.09  with prob <  1.3e-142 
## 
## Tucker Lewis Index of factoring reliability =  0.756
## RMSEA index =  0.084  and the 90 % confidence intervals are  0.079 0.088
## BIC =  -61.46
## Fit based upon off diagonal values = 0.97
## Measures of factor score adequacy             
##                                                    MR2  MR3  MR1  MR4
## Correlation of (regression) scores with factors   0.92 0.86 0.84 0.74
## Multiple R square of scores with factors          0.85 0.74 0.71 0.54
## Minimum correlation of possible factor scores     0.70 0.47 0.43 0.09

Section 3: Intensive Professional Development Items

Intensive Professional Development items are items concerning the benefits of the professional development. It includes: 1, Instructional practices; 2, Student success; 3, Network of colleagues; 4, Institutional integration; 5, Professionalization; 6, Leadership capacity. (To what degree, if any, has the IPD program helped you grow in the following areas?; Five-point-scale)

IPD variables means and SDs

Table: Descriptive Statistics of Intensive Professional Development Variables
Item Wording Mean SDs
pdb_instknow Understanding institutional resources available to me and/or to students 3.22 1.19
pdb_careergrowth Feeling confident about my ongoing growth and development as a faculty member 3.14 1.3
pdb_tech Understanding different instructional tools and resources 3.12 1.2
pdb_networkteach Developing a network of colleagues with peers involved in professional development 3.09 1.34
pdb_inclclass Creating an inclusive classroom community 3.01 1.26
pdb_sob Feeling a sense of belonging to the institution 2.98 1.28
pdb_stupersist Supporting students’ wellbeing and persistence 2.92 1.28
pdb_profidentit Having a professional sense of identity 2.89 1.33
pdb_stucentered Using student-centered teaching techniques in class 2.87 1.28
pdb_leadskills Developing leadership skills 2.71 1.29
pdb_leadrole Having opportunities to be a leader 2.59 1.31
pdb_networkdept Developing and/or strengthening a network of colleagues within my department 2.53 1.34
pdb_advocacy Helping me advocate for myself with my department chair and/or dean 2.32 1.28

Interpreting the IPD Means and SDs

The data presents an overview of the perceived benefits of an Intensive Professional Development (IPD) program across different areas. Responses were measured on a five-point scale, and results are presented as the mean scores and standard deviations for each area.

The highest-rated aspect of the IPD program was “Understanding institutional resources available to me and/or to students” (M = 3.22, SD = 1.19). This suggests that participants felt they improved their understanding of the resources at their disposal.

Participants reported less growth in areas related to students, leadership, and departmental networking. This is evident in items like “Supporting students’ wellbeing and persistence” (M = 2.92, SD = 1.28), “Using student-centered teaching techniques in class” (M = 2.87, SD = 1.28), “Developing leadership skills” (M = 2.71, SD = 1.29), and “Having opportunities to be a leader” (M = 2.59, SD = 1.31). Similarly, “Developing and/or strengthening a network of colleagues within my department” received a lower rating (M = 2.53, SD = 1.34).

The least perceived benefit was in the area of advocacy: “Helping me advocate for myself with my department chair and/or dean” had the lowest mean score (M = 2.32, SD = 1.28). This suggests that, according to participants, the program did not significantly support their ability to self-advocate in departmental contexts.

These results indicate that while the IPD program seems effective in improving knowledge about institutional resources, confidence in growth and development, and understanding instructional tools, it may need to focus more on areas related to student support, leadership development, intra-departmental networking, and self-advocacy.

Interpreting IPD Alphas

The 13-item scale demonstrated high reliability, with a Cronbach’s alpha of .938. A bootstrap 95% confidence interval (CI), based on 1000 samples, further confirmed the scale’s reliability, with a range of .924 to .948. This suggests that the items on the scale are consistently measuring the same construct.

IPD correlation matrix

IPD_data <- Data[,c(52:64)]
kable(cor(IPD_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Intensive Professional Development Variables")
Table: Correlation Matrix of the Intensive Professional Development Variables
pdb_stucentered pdb_tech pdb_inclclass pdb_stupersist pdb_networkteach pdb_networkdept pdb_profidentit pdb_careergrowth pdb_sob pdb_instknow pdb_advocacy pdb_leadskills pdb_leadrole
pdb_stucentered 1.0000000 0.7478163 0.7497992 0.7218668 0.4351559 0.4173979 0.4467490 0.5201981 0.4569302 0.5134276 0.4256621 0.3839052 0.3437841
pdb_tech 0.7478163 1.0000000 0.7139828 0.6446021 0.4681658 0.4301784 0.4459115 0.5413483 0.4700539 0.5740804 0.3984974 0.4038049 0.3883911
pdb_inclclass 0.7497992 0.7139828 1.0000000 0.7669186 0.4016928 0.4263549 0.3849470 0.4391292 0.3829709 0.4651705 0.4134866 0.3737233 0.3266706
pdb_stupersist 0.7218668 0.6446021 0.7669186 1.0000000 0.4252883 0.4296380 0.4115997 0.4909074 0.4510964 0.4856982 0.4434234 0.4099471 0.3346968
pdb_networkteach 0.4351559 0.4681658 0.4016928 0.4252883 1.0000000 0.5792039 0.6362451 0.6660078 0.6407922 0.5291068 0.5315827 0.5940161 0.6234246
pdb_networkdept 0.4173979 0.4301784 0.4263549 0.4296380 0.5792039 1.0000000 0.5272356 0.4914569 0.5063260 0.4299734 0.5117840 0.5412139 0.5331870
pdb_profidentit 0.4467490 0.4459115 0.3849470 0.4115997 0.6362451 0.5272356 1.0000000 0.8073560 0.7411995 0.5792541 0.6095048 0.6568986 0.6501719
pdb_careergrowth 0.5201981 0.5413483 0.4391292 0.4909074 0.6660078 0.4914569 0.8073560 1.0000000 0.7841162 0.6320784 0.6291646 0.6617651 0.6735513
pdb_sob 0.4569302 0.4700539 0.3829709 0.4510964 0.6407922 0.5063260 0.7411995 0.7841162 1.0000000 0.6470282 0.6447620 0.6310406 0.6100183
pdb_instknow 0.5134276 0.5740804 0.4651705 0.4856982 0.5291068 0.4299734 0.5792541 0.6320784 0.6470282 1.0000000 0.5611638 0.5152101 0.4464394
pdb_advocacy 0.4256621 0.3984974 0.4134866 0.4434234 0.5315827 0.5117840 0.6095048 0.6291646 0.6447620 0.5611638 1.0000000 0.6814833 0.6057479
pdb_leadskills 0.3839052 0.4038049 0.3737233 0.4099471 0.5940161 0.5412139 0.6568986 0.6617651 0.6310406 0.5152101 0.6814833 1.0000000 0.8289231
pdb_leadrole 0.3437841 0.3883911 0.3266706 0.3346968 0.6234246 0.5331870 0.6501719 0.6735513 0.6100183 0.4464394 0.6057479 0.8289231 1.0000000

Interpret the IPD Correlation Matrix

The provided correlation matrix reveals relationships between different aspects of the Intensive Professional Development (IPD) program. Correlation coefficients range between -1 and 1. A correlation close to 1 indicates a strong positive relationship, a correlation close to -1 shows a strong negative relationship, and a correlation around 0 suggests a weak or no relationship.

In general, the correlations between the variables in the IPD program are positive, suggesting that improvements in one area are often associated with improvements in others. Notably, none of the correlations are negative, which would indicate that as one variable increases, the other decreases. This reflects the integrated nature of professional development, where growth in one aspect often supports growth in another.

The highest correlation observed is between “Developing leadership skills” (pdb_leadskills) and “Having opportunities to be a leader” (pdb_leadrole), with a correlation coefficient of 0.83. This implies a strong positive relationship, suggesting that as faculty members develop leadership skills, they also perceive themselves to have more leadership opportunities.

On the other hand, the lowest correlation observed is between “Using student-centered teaching techniques in class” (pdb_stucentered) and “Having opportunities to be a leader” (pdb_leadrole), with a correlation coefficient of 0.34. This suggests a relatively weaker positive relationship between the use of student-centered techniques and the perception of having leadership opportunities.

Two other notable correlations involve the relationship between “Having a professional sense of identity” (pdb_profidentit) and “Feeling confident about my ongoing growth and development as a faculty member” (pdb_careergrowth) (r = 0.81), as well as between “Helping me advocate for myself with my department chair and/or dean” (pdb_advocacy) and “Developing leadership skills” (pdb_leadskills) (r = 0.68). These strong positive correlations show that faculty members’ sense of professional identity and confidence in growth are closely related to their perceived leadership skills and ability to advocate for themselves.

Section 4: Collegiality within department (COACH Items)

Collegiality within department are items concerning departmental collegiality. (The overarching item wording: How much do you agree or disagree with the following statements when considering your home department/program?; Five-point-scale)

Collegiality variables means and SDs

Table: Descriptive Statistics of Collegiality Variables
Item Wording Mean SDs
netw_deptmeet I am invited to departmental meetings 4.29 1.13
netw_collegialadmin Staff members and administrators are collegial 4.29 0.88
netw_internttf I have personal interactions with non-tenure-track faculty 4.25 1.1
netw_collegialfac Faculty are collegial 4.12 1
netw_peerscontribute My colleagues pitch in when needed 3.92 1.07
netw_intertenure I have personal interactions with tenured and tenure-track faculty 3.92 1.24
netw_meettime Meeting times are compatible with personal needs 3.91 1.1
netw_worklife My colleagues support work/life balance 3.79 1.07

Interpreting the Collegiality Means and SDs

The data presented concerns the perceptions of faculty members regarding collegiality within their respective departments or programs. Descriptive statistics for eight items were reported, with means ranging from 3.79 to 4.29 (on a 5-point scale), and standard deviations ranging from 0.88 to 1.24.

Two items shared the highest mean score of 4.29: “I am invited to departmental meetings” (SD = 1.13) and “Staff members and administrators are collegial” (SD = 0.88). This suggests that the respondents generally felt included in departmental meetings and experienced collegial relationships with staff and administrators.

“My colleagues support work/life balance” had the lowest mean score of 3.79 (SD = 1.07), suggesting that, while still generally positive, faculty felt slightly less supported in their work-life balance compared to the other aspects of collegiality.

It should be noted that all items received mean scores above the mid-point of the scale (i.e., 3), indicating positive experiences overall with respect to departmental collegiality.

Interpreting Collegiality Alphas

## 
## Cronbach's alpha for the 'na.omit(Data[, 133:140])' data-set
## 
## Items: 8
## Sample units: 592
## alpha: 0.795
## 
## Bootstrap 95% CI based on 1000 samples
##  2.5% 97.5% 
## 0.764 0.822
  With the analysis based on a sample of 592 faculty members, the scale demonstrated good reliability, α = .795. A bootstrap method was used to estimate a 95% confidence interval for this reliability coefficient. Based on 1,000 bootstrap samples, the confidence interval ranged from .765 to .820. This indicates that the scale's reliability is likely to be within this range for the population from which the sample was drawn. The results suggest that the items on the collegiality scale are relatively homogeneous, measuring a similar underlying construct.

Collegiality Correlation Matrix

Colle_data <- Data[,c(133:140)]
kable(cor(IPD_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Collegiality Variables")
Table: Correlation Matrix of the Collegiality Variables
pdb_stucentered pdb_tech pdb_inclclass pdb_stupersist pdb_networkteach pdb_networkdept pdb_profidentit pdb_careergrowth pdb_sob pdb_instknow pdb_advocacy pdb_leadskills pdb_leadrole
pdb_stucentered 1.0000000 0.7478163 0.7497992 0.7218668 0.4351559 0.4173979 0.4467490 0.5201981 0.4569302 0.5134276 0.4256621 0.3839052 0.3437841
pdb_tech 0.7478163 1.0000000 0.7139828 0.6446021 0.4681658 0.4301784 0.4459115 0.5413483 0.4700539 0.5740804 0.3984974 0.4038049 0.3883911
pdb_inclclass 0.7497992 0.7139828 1.0000000 0.7669186 0.4016928 0.4263549 0.3849470 0.4391292 0.3829709 0.4651705 0.4134866 0.3737233 0.3266706
pdb_stupersist 0.7218668 0.6446021 0.7669186 1.0000000 0.4252883 0.4296380 0.4115997 0.4909074 0.4510964 0.4856982 0.4434234 0.4099471 0.3346968
pdb_networkteach 0.4351559 0.4681658 0.4016928 0.4252883 1.0000000 0.5792039 0.6362451 0.6660078 0.6407922 0.5291068 0.5315827 0.5940161 0.6234246
pdb_networkdept 0.4173979 0.4301784 0.4263549 0.4296380 0.5792039 1.0000000 0.5272356 0.4914569 0.5063260 0.4299734 0.5117840 0.5412139 0.5331870
pdb_profidentit 0.4467490 0.4459115 0.3849470 0.4115997 0.6362451 0.5272356 1.0000000 0.8073560 0.7411995 0.5792541 0.6095048 0.6568986 0.6501719
pdb_careergrowth 0.5201981 0.5413483 0.4391292 0.4909074 0.6660078 0.4914569 0.8073560 1.0000000 0.7841162 0.6320784 0.6291646 0.6617651 0.6735513
pdb_sob 0.4569302 0.4700539 0.3829709 0.4510964 0.6407922 0.5063260 0.7411995 0.7841162 1.0000000 0.6470282 0.6447620 0.6310406 0.6100183
pdb_instknow 0.5134276 0.5740804 0.4651705 0.4856982 0.5291068 0.4299734 0.5792541 0.6320784 0.6470282 1.0000000 0.5611638 0.5152101 0.4464394
pdb_advocacy 0.4256621 0.3984974 0.4134866 0.4434234 0.5315827 0.5117840 0.6095048 0.6291646 0.6447620 0.5611638 1.0000000 0.6814833 0.6057479
pdb_leadskills 0.3839052 0.4038049 0.3737233 0.4099471 0.5940161 0.5412139 0.6568986 0.6617651 0.6310406 0.5152101 0.6814833 1.0000000 0.8289231
pdb_leadrole 0.3437841 0.3883911 0.3266706 0.3346968 0.6234246 0.5331870 0.6501719 0.6735513 0.6100183 0.4464394 0.6057479 0.8289231 1.0000000

Interpret the Collegiality Correlation Matrix

Pearson’s correlation coefficients were calculated to examine the pairwise associations among the 13 collegiality variables. All coefficients were positive, indicating that higher scores on any one variable tended to be associated with higher scores on all other variables. The correlation coefficients ranged from .326 to .828, indicating a wide range of effect sizes, from small to large, according to Cohen’s (1988) conventional criteria.

The highest correlation was observed between ‘pdb_leadskills’ and ‘pdb_leadrole’ (r = .828), suggesting a strong positive relationship between these two variables. This result indicates that faculty members who reported high levels of leadership skills were also more likely to report taking on a leadership role.

At the lower end, the smallest correlation was found between ‘pdb_inclclass’ and ‘pdb_leadrole’ (r = .326), indicating a weak positive relationship. This result suggests that including students in class and assuming a leadership role are only slightly related.

These findings provide a valuable insight into the interrelations among various dimensions of collegiality within academic departments. It highlights the potential overlaps between these dimensions and the multifaceted nature of the collegiality construct in higher education settings.

Section 5: Interactions within/outside of department

Interactions within/outside of department are items concerning the departmental interactions. (The overarching item wording: How much do you agree or disagree with the following statements within your department and outsid… - Within your department/; Five-point-scale)

Interactions variables means and SDs

Table: Descriptive Statistics of Interaction Variables
Item Wording Mean SDs
netwdept_stulearn I have discussions about student learning 3.97 1.19
netwdept_teaching I have discussions about effective teaching practice 3.77 1.22
netwdept_tech I have discussion about the effective use of technology 3.57 1.2
netwouts_stulearn I have discussions about student learning 3.22 1.35
netwouts_teaching I have discussions about effective teaching practice 3.21 1.35
netwdept_research I have discussions about current research 3.19 1.34
netwouts_tech I have discussion about the effective use of technology 3.01 1.31
netwouts_research I have discussions about current research 2.73 1.29

Interpreting the Interactions Means and SDs

The table presents the mean and standard deviation for each item. In general, respondents agreed more with statements concerning interactions within their department as opposed to outside. The item “I have discussions about student learning within the department” (M = 3.97, SD = 1.19) had the highest mean value, indicating a general agreement among respondents. On the other hand, the item “I have discussions about current research outside the department” (M = 2.73, SD = 1.29) had the lowest mean value, reflecting a lower level of agreement on this issue.

The items concerning discussions about effective teaching practice and the effective use of technology showed similar patterns, with higher levels of agreement for interactions occurring within the department than outside it. For example, discussions about effective teaching practice within the department had a mean score of 3.77 (SD = 1.22), whereas discussions about the same topic outside the department had a lower mean of 3.21 (SD = 1.35).

Overall, these findings suggest that academic faculty engage in discussions related to student learning, effective teaching practice, and technology use more frequently within their departments than in external contexts. Furthermore, discussions about current research appear to be less common, especially outside the department.

Interpreting Interactions Alphas

## 
## Cronbach's alpha for the 'na.omit(Data[, 141:144, 149:152])' data-set
## 
## Items: 4
## Sample units: 589
## alpha: 0.822
## 
## Bootstrap 95% CI based on 1000 samples
##  2.5% 97.5% 
## 0.789 0.848
The internal consistency reliability for the interaction items within and outside the department, as measured by Cronbach's alpha, was α = .822, N = 589, suggesting good reliability for the scale. The 95% confidence interval for this estimate, based on 1000 bootstrap samples, ranged from .788 to .848. This means that if we repeated our study many times with different samples, we would expect the true value of Cronbach's alpha for the interaction items to fall within this range 95% of the time. This demonstrates that the interaction items form a reliable scale that consistently measures the same underlying construct.

Interaction Correlation Matrix

Inter_data <- Data[,c(141:144,149:152)]
kable(cor(Inter_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Interaction Variables")
Table: Correlation Matrix of the Interaction Variables
netwdept_stulearn netwdept_teaching netwdept_tech netwdept_research netwouts_stulearn netwouts_teaching netwouts_tech netwouts_research
netwdept_stulearn 1.0000000 0.7918744 0.6362609 0.3506626 0.2676594 0.2357957 0.1965174 0.1005970
netwdept_teaching 0.7918744 1.0000000 0.6949202 0.3865220 0.2997879 0.2982726 0.2349109 0.1465216
netwdept_tech 0.6362609 0.6949202 1.0000000 0.4125350 0.2324665 0.1791521 0.3172672 0.1656529
netwdept_research 0.3506626 0.3865220 0.4125350 1.0000000 0.1837943 0.1686417 0.2120028 0.4352733
netwouts_stulearn 0.2676594 0.2997879 0.2324665 0.1837943 1.0000000 0.9050721 0.7500035 0.5506260
netwouts_teaching 0.2357957 0.2982726 0.1791521 0.1686417 0.9050721 1.0000000 0.7628988 0.5566012
netwouts_tech 0.1965174 0.2349109 0.3172672 0.2120028 0.7500035 0.7628988 1.0000000 0.5754517
netwouts_research 0.1005970 0.1465216 0.1656529 0.4352733 0.5506260 0.5566012 0.5754517 1.0000000

Interpret the Interaction Correlation Matrix

In the correlation matrix of interaction variables, several significant correlations stood out. The discussion about student learning within the department (netwdept_stulearn) and the discussion about effective teaching practice within the department (netwdept_teaching) demonstrated a strong positive correlation (r = .79). Similarly, the discussions about student learning outside the department (netwouts_stulearn) and discussions about effective teaching practice outside the department (netwouts_teaching) also showed a very strong correlation (r = .91). These relationships indicate that when discussions about student learning increase, discussions about effective teaching practice also tend to increase, both within and outside the department.

On another note, the discussion about the effective use of technology within the department (netwdept_tech) and discussions about current research within the department (netwdept_research) revealed a moderate correlation (r = .41). This suggests that these two types of discussions within departments also tend to go hand-in-hand.

Finally, it’s notable that discussions about current research outside the department (netwouts_research) showed moderate to strong correlations with all other outside the department discussions: student learning (r = .55), effective teaching practice (r = .56), and effective use of technology (r = .58). This indicates a connection between discussions about research and other key topics outside the department.

Section 6: Perceptions of support within/outside of department

Perceptions of support within/outside of department are the items concerning departmental perceptions of support. (The overarching item wording: How much do you agree or disagree with the following statements within your department and outsid… - Within your department/; Five-point-scale)

Perceptions variables means and SDs

Table: Descriptive Statistics of Perceptions Variables
Item Wording Mean SDs
netwdept_dei My colleagues are committed to diversity/inclusion 4.01 0.99
netwdept_valuedcoll My work is valued by my colleagues 3.81 1.13
netwdept_transparency Leaders/administrators have an open system of communication 3.75 1.23
netwdept_valuedadmin My work is valued by leaders/administrators 3.74 1.21
netwouts_dei My colleagues are committed to diversity/inclusion 3.59 1.1
netwouts_valuedcoll My work is valued by my colleagues 3.23 1.2
netwouts_valuedadmin My work is valued by leaders/administrators 3.07 1.23
netwouts_transparency Leaders/administrators have an open system of communication 3 1.2

Interpreting the Perceptions Means and SDs

According to the table above, perceptions of support vary among faculty members both within and outside their department.

Within the department, the faculty members rated their colleagues’ commitment to diversity and inclusion (netwdept_dei) highest, with a mean score of 4.01 (SD = 0.99) on a 5-point scale. The perception that their work is valued by colleagues (netwdept_valuedcoll) also received a relatively high mean rating of 3.81 (SD = 1.13). Ratings for perceptions of an open system of communication by leaders/administrators (netwdept_transparency) and the extent to which their work is valued by leaders/administrators (netwdept_valuedadmin) were slightly lower, with mean scores of 3.75 (SD = 1.23) and 3.74 (SD = 1.21), respectively.

Outside the department, the highest-rated item was colleagues’ commitment to diversity and inclusion (netwouts_dei) with a mean score of 3.59 (SD = 1.1). The perceived value of their work by colleagues (netwouts_valuedcoll) and leaders/administrators (netwouts_valuedadmin) received lower mean scores of 3.23 (SD = 1.2) and 3.07 (SD = 1.23), respectively. The lowest-rated item outside the department was the perception of an open system of communication by leaders/administrators (netwouts_transparency) with a mean score of 3 (SD = 1.2).

These findings suggest that faculty members generally perceive a higher level of support within their department than outside, particularly in terms of valuing work and open communication. Moreover, the commitment to diversity and inclusion is perceived as stronger within the department than outside. These data indicate the need for efforts to improve perceptions of support, particularly outside the department.

Interpreting Perceptions Alphas

## 
## Cronbach's alpha for the 'na.omit(Percep_data)' data-set
## 
## Items: 8
## Sample units: 566
## alpha: 0.854
## 
## Bootstrap 95% CI based on 1000 samples
##  2.5% 97.5% 
## 0.830 0.874
Cronbach's alpha for the entire dataset on perceptions of departmental support, encompassing eight items and 566 sample units, was found to be .854. This implies excellent internal consistency, meaning the items are closely related and consistently measure the same underlying construct of perceptions of departmental support.

The 95% confidence interval, based on 1,000 bootstrap samples, ranged from .829 to .873. This rather narrow interval further supports the high reliability of the scale, suggesting its robustness in assessing perceptions of departmental support.

Perceptions Correlation Matrix

kable(cor(Percep_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Perceptions Variables")
Table: Correlation Matrix of the Perceptions Variables
netwdept_dei netwdept_valuedcoll netwdept_valuedadmin netwdept_transparency netwouts_dei netwouts_valuedcoll netwouts_valuedadmin netwouts_transparency
netwdept_dei 1.0000000 0.4613014 0.3929199 0.4174670 0.3496538 0.1945384 0.1910731 0.2139230
netwdept_valuedcoll 0.4613014 1.0000000 0.6445679 0.4717447 0.2149908 0.3836822 0.3178529 0.2223214
netwdept_valuedadmin 0.3929199 0.6445679 1.0000000 0.7523089 0.2156782 0.3723935 0.4220078 0.3821561
netwdept_transparency 0.4174670 0.4717447 0.7523089 1.0000000 0.1923697 0.2323034 0.2885135 0.4249403
netwouts_dei 0.3496538 0.2149908 0.2156782 0.1923697 1.0000000 0.6126882 0.5410421 0.5471549
netwouts_valuedcoll 0.1945384 0.3836822 0.3723935 0.2323034 0.6126882 1.0000000 0.8159764 0.6529387
netwouts_valuedadmin 0.1910731 0.3178529 0.4220078 0.2885135 0.5410421 0.8159764 1.0000000 0.7705057
netwouts_transparency 0.2139230 0.2223214 0.3821561 0.4249403 0.5471549 0.6529387 0.7705057 1.0000000

Interpret the Perceptions Correlation Matrix

A strong positive correlation was observed between the perception of one’s work being valued by leaders/administrators within the department and the perception of open communication from leaders/administrators within the department (r = .75, p < .001). Outside of the department, the perception of one’s work being valued by colleagues strongly correlated with the perception of one’s work being valued by leaders/administrators (r = .82, p < .001). The correlations between within-department and outside-department perceptions were moderate, indicating that these constructs operate somewhat independently.

Section 7: Professional Development Variables

Professional development variables are items concerning professional development engagement. (Which, if any, types of professional development programs have you engaged in at this institution during the 2021-2022 academic year?; Binary Scales only contains Yes and No)

Professional Development variables means and SDs

Table: Descriptive Statistics of Professional Development Variables
Item Wording Proportion SDs
pd_workshop Workshop(s) 0.64 0.48
pd_internet Using resources available on the internet (handouts, videos, white papers, etc.) 0.61 0.49
pd_flc Faculty learning community 0.35 0.48
pd_lunch Lunch and learn (informal meeting and short workshop or presentation) 0.32 0.47
pd_consult One-to-one consultation about teaching with staff from the Teaching and Learning Center 0.23 0.42
pd_mentor One-to-one career coaching and/or peer mentoring 0.21 0.41
pd_cert Certificate or badge program 0.19 0.39
pd_discuss Discussion group (book club, interest group) 0.18 0.38
pc_actionteam Action team (curricular action team, departmental action team, etc.) 0.17 0.37
pd_institute Institute or similar intensive meeting 0.14 0.35
pd_none None 0.12 0.32
pd_teachcircle Teaching circle 0.1 0.3

Interpreting the Professional Development Means and SDs

The table presents descriptive statistics for various types of professional development programs that faculty engaged in during the 2021-2022 academic year. The programs range from workshops to the use of internet resources, participation in faculty learning communities, lunch and learn sessions, and more.

The most common form of professional development was participation in workshops, with 64% of faculty reporting engagement in such programs (SD = .48). This was closely followed by the use of internet resources such as handouts, videos, and white papers, reported by 61% of faculty (SD = .49).

Faculty learning communities and lunch and learn sessions, which are informal meetings involving short workshops or presentations, were also reported as common forms of professional development. However, the participation rates were significantly lower at 35% (SD = .48) and 32% (SD = .47) respectively.

One-to-one consultation about teaching with staff from the Teaching and Learning Center was reported by 23% of faculty (SD = .42). One-to-one career coaching and/or peer mentoring and certificate or badge programs were both reported by less than a quarter of faculty, with participation rates at 21% (SD = .41) and 19% (SD = .39), respectively.

Discussion groups, action teams, institutes or similar intensive meetings, and teaching circles were among the least reported forms of professional development, with participation rates ranging from 10% to 18%. Notably, only 12% of faculty reported that they did not engage in any form of professional development during the 2021-2022 academic year (SD = .32).

Professional Development Correlation Matrix

PD_data <- Data[,c(36:40,44:49,51)]
kable(cor(PD_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Perceptions Variables")
Table: Correlation Matrix of the Perceptions Variables
pd_none pd_internet pd_consult pd_mentor pd_lunch pd_workshop pd_discuss pd_cert pd_institute pd_teachcircle pc_actionteam pd_flc
pd_none 1.0000000 -0.4566702 -0.2163656 -0.1819441 -0.2419137 -0.3653509 -0.1610977 -0.1502732 -0.1518408 -0.1290464 -0.1534008 -0.2854239
pd_internet -0.4566702 1.0000000 0.2318320 0.1848808 0.2656735 0.2051414 0.0961127 0.1322856 0.1227795 0.1124248 0.1275783 0.0787061
pd_consult -0.2163656 0.2318320 1.0000000 0.1443570 0.2109053 0.1905820 0.0830534 0.1479941 0.1593993 0.1308141 0.0890837 0.1501661
pd_mentor -0.1819441 0.1848808 0.1443570 1.0000000 0.0783523 0.0965150 0.1012091 0.1295808 0.1071260 0.1116515 0.1752695 0.1330023
pd_lunch -0.2419137 0.2656735 0.2109053 0.0783523 1.0000000 0.2566733 0.2779142 0.1033259 0.2227604 0.0746128 0.1858539 0.1386552
pd_workshop -0.3653509 0.2051414 0.1905820 0.0965150 0.2566733 1.0000000 0.2368337 0.2268331 0.3022135 0.0705408 0.2237262 0.1451218
pd_discuss -0.1610977 0.0961127 0.0830534 0.1012091 0.2779142 0.2368337 1.0000000 0.0775726 0.1530265 0.1076878 0.1294106 0.1572934
pd_cert -0.1502732 0.1322856 0.1479941 0.1295808 0.1033259 0.2268331 0.0775726 1.0000000 0.2217467 0.0120667 0.0938020 0.1279148
pd_institute -0.1518408 0.1227795 0.1593993 0.1071260 0.2227604 0.3022135 0.1530265 0.2217467 1.0000000 0.0796658 0.2538674 0.2682883
pd_teachcircle -0.1290464 0.1124248 0.1308141 0.1116515 0.0746128 0.0705408 0.1076878 0.0120667 0.0796658 1.0000000 0.2389227 0.0869071
pc_actionteam -0.1534008 0.1275783 0.0890837 0.1752695 0.1858539 0.2237262 0.1294106 0.0938020 0.2538674 0.2389227 1.0000000 0.0576097
pd_flc -0.2854239 0.0787061 0.1501661 0.1330023 0.1386552 0.1451218 0.1572934 0.1279148 0.2682883 0.0869071 0.0576097 1.0000000

Interpret the Professional Development Correlation Matrix

The correlation matrix elucidates the relationships between different types of professional development activities engaged in by the faculty during the 2021-2022 academic year.

A critical observation to make is the negative correlations existing between ‘pd_none’ and all other professional development variables. As ‘pd_none’ represents faculty reporting no engagement in any professional development activities, the negative correlations are not surprising. They imply that when faculty report ‘pd_none’, they will necessarily report no engagement in all other listed activities, as these variables are mutually exclusive. Hence, this binary nature leads to these negative correlations.

Turning attention to other professional development activities, various significant positive correlations were observed. For instance, the highest positive correlation was seen between faculty participation in workshops and institutes (r = 0.30, p < .01), suggesting that those faculty members who engaged in workshops were also likely to participate in institutes or similar intensive meetings. Moreover, a strong positive correlation was found between ‘lunch and learn’ sessions and discussion groups (r = 0.28, p < .01), indicating that those who attend these informal sessions are also likely to engage in discussion groups.

In essence, the correlation matrix reveals that faculty members who reported no engagement in professional development (‘pd_none’) did not engage in any other listed professional development activities, highlighting the mutually exclusive nature of these variables. Furthermore, there were significant positive correlations between different types of professional development activities, indicating that faculty members often engage in multiple forms of professional development.

Section 8: Leadership Scales

Leadership scales are the items concerning leadership self-efficacy. (The overarching item wording: How confident are you that you can be successful at the following?/How confident are you that you have the necessary leadership skills to take on the following roles?; Five-point-scale)

Leadership variables means and SDs

Table: Descriptive Statistics of Preceived Leadership Variables
Item Wording Mean SDs
leadconf_teammember Working with a team on a group project 3.62 0.61
leadconf_initiative Taking initiative to improve something 3.48 0.72
leadconf_organize Organizing a group’s tasks to accomplish a goal 3.42 0.73
leadconf_others Leading others 3.32 0.76
leadconf_mentor A mentor for a newer faculty member in a one-to-one setting 3.2 0.94
leadconf_facilitate A presenter or facilitator for faculty workshops, learning communities, etc. related to professional development 3.12 0.96
leadconf_committeedept Head of a departmental committee, teaching circle, or group focused on in instructional and/or curricular development 2.91 1.04
leadconf_governance A representative for a governance group (e.g., faculty senate, union) 2.62 1.09
leadconf_admindept An administrator in my department 2.54 1.15
leadconf_admininst An administrator for a campuswide office or group (e.g., Service Learning, Advising Office, Writing Center) 2.35 1.09
leadconf_committeeinst Head of a college- or institution-wide committee, task force, etc. 2.33 1.1

Interpreting the Leadership Means and SDs

The data reflects the perceptions of leadership self-efficacy among the faculty members, which were assessed by the following items: working as a team member on a group project, taking initiative to improve something, organizing a group’s tasks to accomplish a goal, leading others, being a mentor for a newer faculty member in a one-to-one setting, being a presenter or facilitator for faculty workshops related to professional development, leading a departmental committee focused on instructional and/or curricular development, representing a governance group (e.g., faculty senate, union), being an administrator in their department, being an administrator for a campuswide office or group (e.g., Service Learning, Advising Office, Writing Center), and leading a college- or institution-wide committee or task force.

Faculty members felt the most confident about working with a team on a group project (M = 3.62, SD = 0.61) and taking initiative to improve something (M = 3.48, SD = 0.72). They felt least confident about leading a college- or institution-wide committee or task force (M = 2.33, SD = 1.1), and being an administrator for a campuswide office or group (M = 2.35, SD = 1.09).

The perceived confidence to undertake roles that require leadership was generally lower for broader, institution-wide roles such as administrators or heads of a college- or institution-wide committee. The higher standard deviations associated with these roles suggest more variability in faculty perceptions, indicating a wider range of confidence levels.

In summary, faculty members appear to feel most confident when working in smaller team-based or initiative-driven roles, with confidence levels decreasing as the roles increase in institutional scope or leadership responsibility.

Interpreting Leadership Alphas

## 
## Cronbach's alpha for the 'na.omit(Data[, 168:178])' data-set
## 
## Items: 11
## Sample units: 447
## alpha: 0.918
## 
## Bootstrap 95% CI based on 1000 samples
##  2.5% 97.5% 
## 0.906 0.928
The 11-item scale assessing leadership self-efficacy demonstrated excellent reliability, with a Cronbach's alpha of 0.918. This suggests that the items on the scale are measuring the same construct—leadership self-efficacy—and are doing so consistently. Furthermore, the bootstrap confidence interval (based on 1000 samples) ranged from 0.906 to 0.928, providing additional evidence of the scale's robustness and reliability. These findings support the use of this scale in evaluating faculty members' self-perceived leadership abilities.

Leadership Correlation Matrix

Leader_data <- Data[,c(168:178)]
kable(cor(Leader_data,use="complete.obs"),caption = "Table: Correlation Matrix of the Leadership Variables")
Table: Correlation Matrix of the Leadership Variables
leadconf_others leadconf_organize leadconf_initiative leadconf_teammember leadconf_mentor leadconf_committeedept leadconf_admindept leadconf_committeeinst leadconf_admininst leadconf_governance leadconf_facilitate
leadconf_others 1.0000000 0.6986470 0.6249404 0.5586718 0.5267216 0.5443357 0.5273189 0.5231976 0.5095866 0.3972011 0.4531061
leadconf_organize 0.6986470 1.0000000 0.7222349 0.5833719 0.4441162 0.5155072 0.4503116 0.4424396 0.4662975 0.3532648 0.4871685
leadconf_initiative 0.6249404 0.7222349 1.0000000 0.6256821 0.4147057 0.5100357 0.4370466 0.4037202 0.4086159 0.3573627 0.4853806
leadconf_teammember 0.5586718 0.5833719 0.6256821 1.0000000 0.3575041 0.4022783 0.3012030 0.3129153 0.3254474 0.2963025 0.4293924
leadconf_mentor 0.5267216 0.4441162 0.4147057 0.3575041 1.0000000 0.6206030 0.5107502 0.4892145 0.4373161 0.4376681 0.4146221
leadconf_committeedept 0.5443357 0.5155072 0.5100357 0.4022783 0.6206030 1.0000000 0.6597253 0.6598243 0.6432582 0.5176737 0.5654875
leadconf_admindept 0.5273189 0.4503116 0.4370466 0.3012030 0.5107502 0.6597253 1.0000000 0.7076799 0.7663956 0.5830700 0.4983663
leadconf_committeeinst 0.5231976 0.4424396 0.4037202 0.3129153 0.4892145 0.6598243 0.7076799 1.0000000 0.7767913 0.6742960 0.5518043
leadconf_admininst 0.5095866 0.4662975 0.4086159 0.3254474 0.4373161 0.6432582 0.7663956 0.7767913 1.0000000 0.6316085 0.5894256
leadconf_governance 0.3972011 0.3532648 0.3573627 0.2963025 0.4376681 0.5176737 0.5830700 0.6742960 0.6316085 1.0000000 0.5166986
leadconf_facilitate 0.4531061 0.4871685 0.4853806 0.4293924 0.4146221 0.5654875 0.4983663 0.5518043 0.5894256 0.5166986 1.0000000

Interpret the Leadership Correlation Matrix

The correlation matrix of the 11 leadership variables revealed numerous significant relationships. All leadership items were positively correlated, which is expected given the items are designed to capture different aspects of the same underlying construct - leadership self-efficacy.

The strongest correlation was observed between “Being an administrator in my department (leadconf_admindept)” and “Being an administrator for a campuswide office or group (leadconf_admininst)”, with a correlation coefficient of 0.77. This indicates a strong positive relationship and implies that individuals who are confident in their ability to be an administrator at the departmental level are likely also confident in their ability to be an administrator at the campuswide level.

The next highest correlation was found between “Being the head of a college- or institution-wide committee (leadconf_committeeinst)” and “Being an administrator for a campuswide office or group (leadconf_admininst)”, with a correlation coefficient of 0.78. This suggests a strong positive relationship between confidence in leading a college- or institution-wide committee and confidence in being an administrator for a campuswide office or group.

It is also worth noting that the correlation coefficients between “Being a mentor for a newer faculty member (leadconf_mentor)” and all other items were relatively lower. The lowest correlation of this item was with “Working with a team on a group project (leadconf_teammember)”, with a correlation coefficient of 0.36. This implies that confidence in mentoring doesn’t necessarily translate into confidence in working with a team on a group project.

All in all, the results suggest that perceived self-efficacy in one leadership role is likely to be associated with perceived self-efficacy in other leadership roles. This could be because the skills and confidence needed for successful leadership are similar across different contexts and roles.