Statistical Modeling of Prosocial Behavior and Health Protocols Compliance of ID120 and ID121 Senior High School Students of
DLSU-Manila
Angelo M. Alberto1, Mikaela Chiara C. Cheng1*, Charisse B. Custodio1, Liam M. Magadia1, and Rynne Renee B. Reselva1
1De La Salle University Integrated School (Manila) [email protected]
Abstract: Countries around the globe have been affected in many ways by the Coronavirus Disease 2019 (COVID-19) pandemic (Gregorio, 2020). Citizens are encouraged to practice health protocols such as social distancing, handwashing, and mask-wearing. Since these practices can help lower infection rates and therefore benefit the whole community, even though they can cause inconvenience, compliance with them can be deemed a prosocial act. Filipinos have shown prosocial behavior through donating to community pantries (Suazo, 2021). The researchers were keen on studying the motivations of Filipinos to comply with health protocols and engage in prosocial acts. Herein, they focused on psychological factors, Empathy and Egoism. They used Weigel et al.’s (1999) model, the Interpersonal Reactivity Index (1983), and Galang et al.’s (2021) questionnaire for measuring Egoism, Empathy, and health protocol compliance, respectively, and devised their own questionnaire to measure prosocial acts. Results showed that Egoism was significantly correlated with belief in social distancing, but only with that one variable. The Empathy subscales, Empathic Concern (EC) and Perspective Taking (PT) significantly correlated with mask-wearing and social distancing. EC and PT also had a significant correlation with prosocial behavior and belief in social distancing, respectively. However, only the regression model with PT and social distancing had a statistically significant predictor. Overall, results suggest new information on human behavior amidst the COVID-19 pandemic.
Keywords: COVID-19 pandemic; health protocol compliance; prosocial behavior; Egoism;
Empathy
1. INTRODUCTION
1.1. Background of the Study
On January 30, 2020, the Department of Health (DOH) reported the Philippines’ first case of the novel coronavirus or COVID-19 (Gregorio, 2020). Nazario (2022) wrote that about two years later from January 11 to 17, the seven-day moving average of daily cases reached 34,923, exceeding the peak in 2020 of 4,421 cases by more than seven hundred percent. Hence, it is only imperative that Filipinos follow protocols that will help lower the prevalence of the virus and new variants. Thus, it may be
useful to determine the psychological factors that can affect people’s motivation to comply with these protocols.
Prosocial behavior has been exhibited in communities around the Philippines in direct ways. To aid those in dire need of basic goods like food and utilities, especially those who lost their livelihoods during the pandemic, ordinary Filipino citizens donated what they can to community pantries (Suazo, 2021). Since compliance to protocol lowers infection rates and therefore benefits the community at large, it can also be considered a prosocial act.
Empathy as a general concept is defined as the ability to relate to another individual's emotional experiences and sensations (Davis, 1983). Various studies have listed Empathy as a motivation for compliance to health protocols and prosocial behavior because it has a propensity to influence the way people act (De Ridder et al., 2021; Galang et al., 2021). According to the Interpersonal Reactivity Index (IRI) by Davis (1983), Empathy has four subscales: Perspective Taking (PT), Empathic Concern (EC), Personal Distress (PD), and the Fantasy Scale (FS). PT is the propensity of an individual to take another person’s point of view into their perspective and EC is the tendency of an individual to experience compassion and concern, for other people going through negative experiences. These two subscales were accounted for as they take a more targeted approach to cognitive empathy. Meanwhile, FS and PD were not defined as it is not in line with how the researchers defined Empathy for the purpose of this article.
On the other hand, Egoism (EGO) is a personality trait linked to self-centered and self-enriching behaviors (Weigel et al., 1999). This trait may have an effect on people’s compliance with safety protocols, yet it has not been known to measure compliance to COVID-19 in studies.
It is in the interest of the researchers to examine the possible motivations or deterrents for health protocol compliance and prosocial behavior. This study aims to determine the relationship between the two factors—EGO and Empathy—and behaviors during the COVID-19 pandemic—prosocial actions and compliance to COVID-19 safety protocols. The independent variables are EGO, EC, and PT, while the dependent variables are Social Distancing (SD), Mask-wearing (MW), Belief in the Effectiveness of Social Distancing (SD-B), Belief in the Effectiveness of Mask-wearing (MW-B), and Prosocial Behavior (PSB). Due to the COVID-19 pandemic, the researchers were only able to conduct online surveys. The population of the study is limited to Senior High School students in De La Salle University - Integrated School (DLSU-IS) Manila (A.Y. 2021-2022). Results can help the administrators of DLSU-IS Manila and other learning institutions decide whether or not to conduct face-to-face classes during the pandemic based on how responsible SHS students are with health protocol compliance. Furthermore, promotion of said compliance may be tailored to be more
effective based on the psychological profiles of the population.
2. METHODOLOGY
2.1. Data Collection MethodThe initial sample size was calculated with a 95%
confidence level, an assumed proportion of 0.5 (50% of participants follow health protocols), and a margin of error of 0.03, for an initial sample size of 1068. An assumed proportion of 0.5 was chosen because this gives the maximum calculated sample size. However, participants were taken from a finite population of 330, so the Finite Population Correction (FPC) factor was used to get a final calculated sample size of 253. The sample units were divided by categorizing them by grade level and strand (Ex.
Grade 12, STEM students) for a total of 10 strata. Since the sample units were taken from the list of students who agreed to participate in the study given by the administrators of De La Salle University Integrated School - Manila, they were taken through simple random sampling proportional to the stratum sizes, as shown in Table 2.
Table 1
Population Stratum sizes Finite
Populatio n
ADT ABM HUMS
S
STE
M SPT
G11 3 33 3 49 2
G12 20 19 27 159 15
Considering the population frequencies in Table 1, and their total which is 330, the sample size of each stratum was calculated as follows:
Table 2
Sample Size of Each Stratum AD
T ABM HUMSS STEM SPT
G11 2 25 2 38 2
G12 15 15 21 122 12
2.2. Data Collection Instruments
A survey was administered via Google Forms to all strands of ID 120 and ID 121. The survey questionnaire
measured a variety of variables. Information on the respondents’ consent, grade level, and strand were also collected. To measure the independent variables, EGO and Empathy, Weigel et al.'s (1999) EGO model and the IRI by David (1983) were used, respectively. In the IRI, the researchers did not take into account the FS and PD subscales due to their lack of relevance to the current study.
The aforementioned measuring materials were gathered for free and have been previously used and tested in multiple studies. The researchers took questions from Galang et al., (2021) to measure health protocol compliance. They opted not to add questions regarding faceshield-wearing after the Philippine government lifted this requirement (Espinosa, 2021). The researchers created their own questions to measure PSB. A 5-point Likert scale where five is
“strongly agree” and one is “strongly disagree” was used to measure all of the independent and dependent variables. A questionnaire was considered reliable if it has an alpha value of at least 0.70. The Cronbach's Alpha test, indeed, managed to prove that for all tests for the variables EGO, PT, EC, and PSB. The EGO got the lowest alpha value of 0.73 The questionnaire for measuring SD, MW, SD-B, and MW-B were not tested for reliability because each of these variables was measured with only one question.
2.3. Data Analysis
The researchers got the median and interquartile range (IQR) of the dependent and independent variables.
They then used two correlational tests: Goodman and Kruskal’s gamma, and Kendall’s tau, as well as ordinal logistic regression, to assess the relationships of the independent variables with the dependent variables.
Goodman and Kruskal’s gamma and Kendall’s tau were chosen because they are appropriate for ordinal values.
2.3.1. Hypothesis Testing
Statistica version 14.0.0.15 was the statistical software used to run Kendall’s tau and Goodman and Kruskal’s gamma. Since the literature thus far suggests that higher Empathy levels and lower EGO levels result in higher health protocol compliance and PSB, the relationships involving PT and EC were expected to have positive correlations, while the relationships involving EGO were expected to have negative correlations. Level of significance used was 0.05.
For the relationships involving PT and EC, the researchers formulated the following hypotheses on the population correlation coefficients:
𝐻0: γ = 0, 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒𝑟𝑒 𝑖𝑠 𝑛𝑜 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛
𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑡𝑤𝑜 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
𝐻1: γ > 0, 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒𝑟𝑒 𝑖𝑠 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑡𝑤𝑜 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
The following hypotheses are for the relationships involving EGO:
𝐻0: γ = 0, 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒𝑟𝑒 𝑖𝑠 𝑛𝑜 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛
𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑡𝑤𝑜 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
𝐻1: γ < 0, 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒𝑟𝑒 𝑖𝑠 𝑎 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑡𝑤𝑜 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
2.3.2 Regression Modelling
The regression model used for this research is ordinal logistic regression. The software implemented by the researchers, SPSS Standard GradPack 28, was bought online from OnTheHub eStore. It uses the following equation to perform ordinal regression:
(Formula 1)
ln 𝑙𝑛 𝑝𝑝(𝑌≤1)
1−𝑌≤1
( )
( )
= 𝑏0− 𝑏1𝑋where
𝑖 = 1, 2, 3, 4, 5
𝑋 = 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒
𝑝(𝑌≤1)= 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑠𝑐𝑜𝑟𝑖𝑛𝑔 𝑒𝑞𝑢𝑎𝑙 𝑡𝑜 𝑜𝑟 𝑙𝑒𝑠𝑠 𝑡ℎ𝑎𝑛 𝑖 𝑖𝑛 𝑡ℎ𝑒 𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒
𝑏0= 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 𝑏1= 𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 ln 𝑙𝑛 𝑝𝑝(𝑌≤1)
1−𝑌≤1
( )
( )
𝑤ℎ𝑒𝑛 𝑋 𝑖𝑠 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 (𝑙𝑎𝑏𝑒𝑙𝑙𝑒𝑑 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛)The final regression models were compared to the intercept models with the Model Fitting Information and Goodness of Fit tests. In the Model Fitting Information test, the null hypothesis is that the final model produces less error than the intercept model. On the other hand, the Goodness of Fit tests evaluate the null hypothesis that the final model is a better fit than the intercept model. The Test of Parallel Lines was also done to test the assumption or ordinal regression models that the effect of a category of an independent variable is constant for all values of the
dependent variable. For a final model to be considered a better fit than the intercept model, the Model Fitting Information test should have𝑝 < 0. 05, while the Pearson and Deviance chi-squares in the Goodness of Fit table and the Test of Parallel Lines should have𝑝 > 0. 05.
3. RESULTS AND DISCUSSION
3.1. Participant Demographics
Of the 330 who were sent the survey questionnaire, only 135 respondents answered it. The researchers were unable to reach the target sample size of 253 to have a confidence level of 95%, so the confidence level is lower than that value. Of the participants, 70.4% of the respondents were in grade 12, and 57.8% were in the STEM course.
3.2. Descriptive statistics
Table 3 shows that the participants’ median score in EGO was neutral, but were high in EC and PT. All of the independent variables have low IQR. Their medians in SD, MW, SD-B, and MW-B were the highest possible value.
The median score of the participants in PSB is neutral, but this has the highest IQR among the variables, so the responses to this are more spread out.
Table 3
Median and Interquartile Range (IQR) of Independent Variables
Independent Variables Median IQR
Egoism (EGO) 3 0.5
Empathetic Concern (EC) 4 0
Perspective Taking (PT) 4 0
Social Distancing (SD) 5 1
Mask Wearing (MW) 5 0
Belief in Social Distancing (SD-B) 5 1
Belief in Mask Wearing (MW-B) 5 0
Prosocial Behavior (PSB) 3 2
3.3. Hypothesis Testing
3.3.1. EGO Results
It can be seen in Table 4 that all correlation coefficients between EGO and the dependent variables were negative, but only SD-B has a significant correlation in the Goodman and Kruskal’s gamma and Kendall’s tau tests.
Table 4
Correlation Coefficients between EGO and the Dependent Variables
Dependen t Variable
Goodman and Kruskal’s
gamma
Kendall’
s tau
(G) (𝛕)
SD-B -0.2403* -0.1203*
MW-B -0.2824 -0.0897
PSB -0.1613 -0.1118
MW -0.1613 -0.0467
SD -0.1188 -0.0705
*significant at 0.05 level of significance 3.3.2. Empathic Concern
For the correlation coefficient between EC and the dependent variables, all correlational coefficients were positive, as seen in Table 5. Only MW, SD, and PSB have significant correlations in the Goodman and Kruskal’s gamma and Kendall’s tau test.
Table 5
Correlation Coefficients between EC and the Dependent Variables
Dependen t Variable
Goodman and Kruskal’s
gamma
Kendall’s tau
(G) (𝛕)
MW 0.4959* 0.1523*
SD 0.2974* 0.1573*
PSB 0.1982* 0.1227*
MW-B 0.2378 0.0608
SD-B 0.1689 0.0759
* significant at 0.05 level of significance.
3.3.3. Perspective-Taking Results
Referring to Table 6, all correlation coefficients between PT and the dependent variables were positive. SD
has significant correlation in all three tests, but MW and SD-B’s correlations are only significant in the Goodman and Kruskal’s gamma and Kendall’s tau tests.
Table 6
Correlation Coefficients between PT and the Dependent Variables
Dependen t Variable
Goodman and Kruskal’s
gamma
Kendall’s tau
(G) (𝛕)
SD 0.4624* 0.2547*
MW 0.4448* 0.1262*
SD-B 0.3083* 0.1487*
MW-B 0.1387 0.0386
PSB -0.0046 -0.003
* significant at 0.05 level of significance.
3.4. Regression Results
After running a Spearman’s rho test on the independent variables and finding that EC is significantly
correlated with EGO (𝑟 ) and PT (
𝑠=− 0. 31, 𝑝 < 0. 05
), the researchers only performed 𝑟𝑠= 0. 47, 𝑝 < 0. 05
multiple regression with EGO and PT only.
3.4.1. Social Distancing EGO and PT
As seen in Table 7, when SD is modeled with PT and EGO simultaneously, only PT is a significant predictor.
The independent variable, EGO, was removed to form the final model.
Table 7
Parameter Estimates for EGO and PT Link Function: LOGIT
Effect Estimat e
Standar d Error
Wald Stat.
Intercept
s SD=3 -3.483 0.738* 22.258
* SD=4 -1.315 0.677* 3.776*
Location PT=3 -1.935 0.657* 8.683*
PT=4 -1.204 0.512* 5.534*
PT=5 0
EGO=1 19.791 .000
EGO=1.
5 19.65 .000
EGO=2 -0.13 0.693 0.035
EGO=2.
5 -0.503 0.798 0.398
EGO=3 0.19 0.57 0.111
EGO=3.
5 -0.672 0.893 0.567
*significant at 0.05 level of significance.
PT
Table 8 shows that when PT is 3 or 4, the SD log odds decreases by 1.858 (𝑝 = 0. 003) and 1.339 ( ), respectively, compared to when PT is 5. This 𝑝 = 0. 007
means that as PT gets higher, SD gets higher as well.
Table 8
Parameter Estimates for PT Link Function: LOGIT
Effect Estimate Standard Error
Wald Stat.
Intercept
s SD=3.0 -3.57 0.535* 44.46*
SD=4.0 -1.461 0.451* 10.496*
Location PT=3.0 -1.858 0.622* 8.934*
PT=4.0 -1.339 0.5* 7.176*
*significant at 0.05 level of significance
It can be seen in Table 9 that the model with PT as the independent variable and SD as the dependent variable also passes the Model Fitting Information test. The p value of the final model is also lower than 0.05, which means that the model is a good predictor of SD. The p values of Pearson and Deviance in the Goodness of Fit test are also higher than 0.05, and thus the goodness of fit of the model is not rejected. The significance value in the Test of Parallel Lines is greater than 0.05, so the assumption that the effect of each category of PT is the same for all values of SD is held.
Table 9
Model Fitting Information, Goodness-of-Fit, and Test of Parallel Lines
Link Function: LOGIT
Model -2 Log
Likelihood
Chi-Squar e Intercept Only 31.490
Final 20.022 11.467*
Pearson 0.025
Deviance 0.024
Null Hypothesis 20.022
General 19.998 0.024
*significant at 0.05 level of significance
EC
Referring to Table 10, it can be seen that the model with EC as the independent variable and SD as the dependent variable does not pass the Model Fitting Information test, so the model does not fit the data well.
Table 10 Tests For EC-SD Link Function: LOGIT Model
Chi-Sq uare Intercept Only
Final 4.409
Pearson 1.585
Deviance 1.417
Null Hypothesis
General 1.417
Values of EC that are significantly different from reference value (<0.05)
EC=3.
0
*significant at 0.05 level of significance
3.4.2. Mask-wearing, Belief in the Effectiveness of Social Distancing, Belief in the Effectiveness of Mask-wearing, Prosocial Behavior
Referring to Table 11, none of the models involving the rest of the dependent variables MW, SD-B, MW-B, and PSB, passed the Model Fitting Information test. Only the EGO-PSB model is given further detail in
Table 11 as it is the only model here that has significant parameter estimates.
Table 11
Tests For EGO-PSB Link Function: LOGIT
Model Chi-Squar
e Intercept Only
Final 11.555
Pearson 17.131
Deviance 19.404
Null Hypothesis
General 19.404
Values of EGO that are significantly different from reference value (p<0.05)
EGO=1.0, 1.5
*significant at 0.05 level of significance 3.7. Discussion
EGO did not turn out to be a significant predictor of any of the dependent variables in the regression models, but it significantly correlated with SD-B in the Goodman and Kruskal’s gamma and Kendall’s tau tests (Table 4).
This signifies that the less prevalent the egoistic tendencies of an individual are, the more likely they are to believe in the efficacy of social distancing and vice versa. However, EGO did not significantly correlate with any other variables, even with SD. It may be that in the studied population, people are more conducive to public health messaging and believe that health protocols are also for their own good. Future studies can focus on the relationship between health protocol compliance and trust in academic and government authorities. Surprisingly, EGO did not correlate significantly with PSB (Table 4). This may be explained by some egoistic people not partaking in altruistic activities because they give them personal distress (Dovidio & Banfield, 2015), while other egoistic people perform altruism to receive praise and status (Siem &
Stürmer, 2018).
With regards to the empathy subscale of EC, while it was not a significant predictor in any regression models, it positively correlated with MW, SD, and PSB (Table 5) significantly in the Goodman and Kruskal’s
gamma and Kendall’s tau tests. It can be concluded that higher EC is associated with higher compliance with mask-wearing and social distancing protocols. These results are in line with Galang et al.’s (2021) and Pfattheicher et al.’s (2020) findings, which also found positive correlations between these variables. It is therefore suggested to promote EC among the DLSU-IS SHS students by making them feel an emotional connection with others, especially with those who are at higher risk of contracting severe cases of COVID-19, like senior citizens and persons with comorbidities.
The other empathy subscale that was studied, PT, was a significant predictor of SD in the ordinal logistic regression mode (Tables 8-9). It also significantly correlated with SD in both correlational tests (Table 6).On the other hand, PT significantly correlated with MW and SD-B only in the Goodman and Kruskal’s gamma and Kendall’s tau tests (Table 6). For the studied population, encouraging people to relate to those most heavily affected by COVID-19 on an intellectual level may promote compliance to health protocols more than relating to them on an emotional level. A similar study discovered that interventions (such as displaying videos of a student model explaining they had asthma or the like) which required the use of understanding others’ situations to promote social distancing had noteworthy results (De Ridder et al., 2021).
The association between PT and SD must be strong since their relationships were significant in the regression and in both correlational tests. Interestingly, the correlation between PT and SD is stronger than the correlation between PT and MW in both correlational tests. This may be because mask-wearing may have less to do with free will as it is more heavily enforced.
4. CONCLUSION
In conclusion, both PT and EC positively correlate with both mask-wearing and social distancing. In addition, PT is a significant predictor of social distancing in regression modeling. This finding further supports the results of De Ridder et al.’s (2021) and Pfattheicher et al.’s (2020), suggesting that it may be wise to promote compliance by appealing to feelings of concern for and one-ness with those most affected by the pandemic.
However, EGO only shared a significant negative correlation with SD-B. This statement may be subject to change as the participants grow older due to underlying factors, like lack of freedom from being under the
supervision of parents and guardians. Overall, results may help administrators of DLSU-IS Manila to decide on whether or not to conduct face-to-face classes. It is to be noted that the research is limited only with participants from DLSU-IS Manila SHS students. Therefore, the researchers recommend future research on a similar study with different populations.
5. ACKNOWLEDGEMENTS
The researchers would like to thank Mr. Angelo M. Alberto and Miss Leah D. Madrazo for all of their help.
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