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Part I: There has been a significant shift in how people work in recent years. ...

May 9, 2024

Part I: There has been a significant shift in how people work in recent years. Technological advancements, such as video-conferencing software and high-speed internet, have enabled people to work from anywhere and at any time. This, along with a reduced transparency of job expectations, has led to a blurring of the line between work and personal life (Ng et al., 2007). As a result, there is a growing need to understand workaholism, which is defined as a preoccupation with work that results in an over-commitment of time, energy, and resources at the expense of other life activities (Balducci et al., 2020). Research suggests that workaholism is influenced by a combination of an individual's dispositions and socio- cultural experiences. One socio-cultural factor that has been found to contribute to workaholism is an overwork climate, which values long hours and a preoccupation with work at the cost of recovery and personal time. This type of work climate has shown a positive correlation with workaholism (Mazzetti et al., 2014; Afota et al., 2021). It has also been proposed that an individual's group identification may be associated with workaholism. Those with a high group identification may be more likely to adopt and adhere to their workplace's overwork climate, contributing to workaholism (Junker et al., 2023). Additionally, a fear of failure may predict workaholism, with this motivation developing from the internalisation of external workplace standards necessary to obtain social approval (Mazzetti et al., 2020). Please take a look at the “first_dataset.dta”. In this dataset, the variable “workaholism” is the level of workaholism of white-collar employees, measured by an established workaholism scale (scores range from 10-40, where higher scores represent greater workaholism). The variable ‘climate’ is the degree to which the employee's work environment is characterised by overworking (scores range from 1-100, where higher scores represent a greater overwork climate). The variable ‘failure’ is the degree to which one’s motivation to achieve is due to a fear of failure (scores range from 10-40, where higher scores represent higher fear of failure). Finally, the variable ‘group’ is a dichotomous variable indicating whether the participants have low or high group identification. Although assumptions-checking is an important part of data analysis, for the purpose of this report, you don’t need to report any assumptions-checking results. Step A. Based on this background, write a one-sentence research prediction that this dataset would have been collected to address. (Please make sure that all the variables in the dataset are used to answer this question and complete the next step of data analysis.) [6 marks] Step B. Please conduct the appropriate statistical analysis to test the research prediction you wrote in response to step A. Then, summarise the results of these analyses addressing the hypotheses, like how you would write a RESULTS section of a research report in APA7 Style. [30 marks] Step C. In plain language, please use 1-2 sentences to communicate these findings and their real-world implications to a non-expert audience, such as advising a workplace manager on how to make changes to improve employees’ working environment. [4 marks]

Answer

The research prediction that this dataset would have been collected to address is: Higher levels of workaholism in white-collar employees are predicted by a greater overwork climate, higher fear of failure, and high group identification.

Key Concept

Research Prediction

Explanation

A research prediction is a statement about the expected relationship between variables based on theoretical background and previous research. In this case, it integrates the socio-cultural factors and individual dispositions that may contribute to workaholism.

Answer

A multiple regression analysis was conducted to test if the overwork climate, fear of failure, and group identification significantly predicted workaholism. The results indicated that the overall model was statistically significant, with climate and failure emerging as significant predictors of workaholism, while group identification did not significantly predict workaholism.

Key Concept

Multiple Regression Analysis

Explanation

Multiple regression analysis is a statistical technique used to understand the relationship between one dependent variable and two or more independent variables. It helps in predicting the value of the dependent variable based on the values of the independent variables.

Answer

To improve the working environment and reduce workaholism, managers should focus on creating a balanced work climate that discourages excessive overworking and addresses the underlying fear of failure among employees.

Key Concept

Real-world Implications

Explanation

The findings from the statistical analysis have practical implications for workplace management, suggesting that changes to the work environment can potentially reduce unhealthy work habits and improve employee well-being.

Part II: Böhm, Rusch, and Gürerk (2016) investigated the psychological mechanisms underpinning intergroup conflict, with a particular focus on the role of defensive motivations. Through an innovative laboratory experiment that utilises an interactive game between participants, the research showed that, despite being costly to themselves, individuals were more likely to engage in retaliatory and pre-emptive aggressive actions when they perceived their group as being threatened or when anticipating potential threats from an outgroup. The experiment also demonstrated that aggression levels could be mitigated by removing the perceived threats from outgroups, leading to reduced intergroup aggression and more peaceful outcomes. Please take a look at the “second_dataset.dta”. This is a dataset from a project that aims to follow up on Böhm et al.’s (2016) findings. More specifically, this project aims to answer the question of how one’s perceived threat level (e.g., participants are led to believe that the outgroup has a high, medium, or low probability of initiating aggression first) and whether there is communication between different groups (yes, no) affects individuals’ pre-emptive aggressions. A similar laboratory experiment using the interactive game was conducted in this project, with the pre-emptive aggressions being measured by the number of points that participants allocated to the outgroup (higher points indicate a higher level of pre-emptive aggression towards the outgroup). The researchers predict that a high perceived threat will increase the level of aggression compared to medium- and low-threat conditions, confirming the original study's findings. Overall, being able to communicate with the outgroup before the decision should reduce participants’ overall pre- emptive aggression compared to when communication is lacking, due to increased understanding and reduced misinterpretations of intentions. Finally, the effect of communication may be modulated by the level of threat that one is perceiving, such that the benefit of communication compared to none will only be present when they feel that the threat is low, but not when they feel the outgroup has a medium or high probability to strike first. Although assumptions-checking is an important part of data analysis, for the purpose of this report, you don’t need to report any assumptions-checking results. Step A. Please conduct the appropriate statistical analysis (as well as all necessary follow-up analyses) to test the above research hypotheses. Then, summarise the results of these analyses addressing these hypotheses, like how you would write a RESULTS section of a research report in APA7 Style. [38 marks] Step B. In plain language, please use 1-2 sentences to communicate these findings and their real-world implications to a non-expert audience, such as advising policymakers on how to promote peace between conflicting groups. [4 marks]

Answer

Results indicated that perceived threat level significantly affected pre-emptive aggression, with higher threat perception leading to increased aggression. Communication between groups was associated with reduced aggression, particularly in low-threat conditions. The interaction effect suggested that the benefit of communication in reducing aggression was not observed in high-threat conditions.

Key Concept

Perceived Threat and Aggression

Explanation

The study supports the hypothesis that higher perceived threats increase pre-emptive aggression, aligning with Böhm et al.’s (2016) findings.

Key Concept

Communication's Role in Aggression

Explanation

Communication between groups can mitigate aggression, especially when the perceived threat is low, highlighting the importance of dialogue in conflict resolution.

Key Concept

Interaction Effect of Threat Perception and Communication

Explanation

The effectiveness of communication in reducing aggression is influenced by the level of perceived threat, with communication being less effective in high-threat scenarios.

Answer

The study found that people are more aggressive when they feel threatened and that talking to the other group can help reduce this aggression, but mostly when the threat is not too high.

Key Concept

Communication and Threat in Conflict

Explanation

Dialogue can reduce conflict, but its effectiveness is limited under high perceived threats, suggesting nuanced approaches are needed for peace promotion.

Answer

Does the recognition accuracy for faces differ between ethnic in-group and out-group members, and does this vary with the emotion displayed on the faces?

Key Concept

Research Question

Explanation

The research question aims to investigate the potential differences in face recognition accuracy based on ethnic group membership and facial emotion expression.

Answer

Effect 1: There is a higher recognition accuracy for faces of ethnic in-group members compared to out-group members.

Effect 2: Recognition accuracy is different for neutral faces compared to angry faces.

Effect 3: The interaction effect suggests that the difference in recognition accuracy between in-group and out-group faces varies depending on the emotion displayed.

Key Concept

Main Effect of Group Membership

Explanation

This effect indicates that participants are generally more accurate at recognizing faces from their own ethnic group than those from other ethnic groups.

Key Concept

Main Effect of Emotion

Explanation

This effect shows that the type of emotion displayed on a face (neutral or angry) influences the overall recognition accuracy.

Key Concept

Interaction Effect

Explanation

The interaction effect reveals that the influence of group membership on recognition accuracy is not consistent across different emotions, suggesting a more complex relationship between these variables.