HRER512: Research Methods in Human Resources and Employment Relations I

 

In the last lesson, you learned about writing a literature review in an ordered and accessible way. We will now be changing our focus somewhat, discussing two distinct, but related, areas of concern when carrying out research with or about people (who, in research, are usually referred to as human subjects): ethics and politics. Research ethics is concerned primarily with the prevention of any harm that might result during the research, especially harm to the research subjects. Politics in research refers to the power relationships that exist between the researcher and other stakeholders in the research process and how those relationships are enacted, particularly in relation to data access.

The lesson will also continue the discussion of hypothesis testing; you will learn how to tell whether a research hypothesis should be accepted or rejected based on calculated confidence intervals.

 

Objectives

After successfully completing this lesson, you should be able to do the following things:

• Describe several fundamental ethical principles.
• Apply ethical principles to your proposed fictional research project with ESC.
• Operationalize ethical principles to ensure they are not contravened in research.
• Understand your obligations in relation to data management and protection.
• Recognize and deal with conflicts of interest.
• Determine whether research hypotheses should be accepted or rejected based on calculated confidence intervals.

 

Activity Planner

To help you plan your studies this week, here is a table of the activities you will be working on. Remember that these timings are approximate, and you will need to allow time for reflecting on what you have learned. Check the Course Schedule for specific due dates.

 

Table 6.1. Lesson 6 Activity Planner

Activity Category	Topic/Activity	Time
	Read Chapter 6 research methods book	90 minutes
	Read the Lesson 6 commentary	30 minutes
	Read “The Grey Area—Ethical Dilemmas in HR Analytics: Perspectives From the Global Workforce”	45 minutes
	Complete Lesson 6 Quiz	20 minutes
	Engage in project-related activity	120 minutes
	Work through Chapter 4 in the Excel textbook	120 minutes

 

 

Research Ethics and Obligations

Research in the social sciences frequently involves gathering data from people. This, in many instances, raises questions about how researchers should treat the people who provide them data; these questions generally relate to research ethics. In essence, research ethics is concerned with preventing physical, psychological, financial, or any other kind of harm during a research study, which of course is particularly important if your research involves human participants.

At its most basic, ethics is defined in the Merriam-Webster online dictionary as “the discipline dealing with what is good and bad, and with moral duty and obligation; a set of moral principles; a theory or system of moral values” (n.d.-a). As such, ethics-related decisions occur within specific contexts that employ differing rules and norms. Cameron and Price (2009) suggest that researcher conduct is guided by a number of different obligations:

• Legal obligations apply to the country or countries in which researchers conduct studies, collect data, and store data. These activities don’t always occur within the same country. Consider, for example, the impact of the GDPR (General Data Protection Regulations) on using employee data from a European subsidiary of an American company.
• Professional obligations are established by professional associations (e.g., the Academy of Management or the American Psychological Association) to guide member conduct. If they violate these rules of conduct, researchers can be sanctioned for disgracing the professional association or discipline.
• Cultural obligations refer to informal rules that set appropriate behavior. For example, if a researcher wants to interview the chief executive of a large company, in some societies, particularly those with low power distance (Hofstede, 2003), it would be perfectly acceptable for the researcher to simply contact them directly via LinkedIn, Twitter, or some similar platform. However, in countries with high power distance (Hofstede, 2003), this would be frowned upon and considered very disrespectful. 
• Personal obligations refer to the beliefs, values, and subsequent behavioral choices that individuals make of their own free will. For example, to what extent does someone believe that it is acceptable to cause minor harm to one person in order to prevent major harm to another? What level of harm is acceptable in this case? Does it depend on who the people are? Their ages? Their current quality of life?

Any researcher is doing their research within the context of all these obligations. Cameron and Price (2009, p. 121) employ a useful diagram to illustrate this (Figure 6.1).

 

 

Ethical Principles Relating to Participants or Respondents

Some particular ethical issues tend to recur in business and management research. Diener and Crandall (1978) divided these into four main areas, which are still useful today:

• harm to participants,
• informed consent,
• invasion of privacy, and
• deception.

These issues are dealt with in some depth in the course textbook, so we won’t belabor the points here. Generally, universities and professional associations have codes of ethics that govern the research process. The principles are usually quite sensible and relatively straightforward (if sometimes time-consuming) to apply.

Penn State provides a code of ethics. However, this code does not normally apply to research projects that are completed simply to get a grade, rather than to add to a broader body of knowledge. Nonetheless, if you were gathering primary data for research purposes in the future, you should bear the following principles in mind, understanding how they relate to your participants and acting accordingly:

• protection from harm: You must make every effort to minimize the risks of any harm to research participants.
• informed consent: You must always inform potential participants in advance (and in understandable terms) of any benefits, risks, inconveniences, or obligations associated with the research that might reasonably be expected to influence their willingness to participate.
• respect: The research should not undermine or contradict the values, credibility, or roles and responsibilities of your participants. For example, does the research require them to share information that they should keep confidential or to do something with which they are uncomfortable? Being respectful also involves considering the power dynamic between you, as the researcher, and the participant. If the researcher is the potential participant's boss, would they feel comfortable refusing to participate? No one should ever be coerced into participating in research.
• openness and integrity: You must be open and honest about the purpose and content of any research project, always behaving professionally. Participants should be given opportunities to access the outcomes of the research they have participated in and should be debriefed, if appropriate, after they have provided data.
• confidentiality: Except where participants have given explicit written consent, the confidentiality of their identities and data must always be respected and preserved.

 

 

Ethical Principles Relating to Others in the Research Process

Two topics in research ethics that tend to be underrepresented in the literature are harm to non-participants and harm to the researcher.

Non-participants

Often, consideration is not paid to those who might be affected by the outcomes of a piece of research, but who don’t participate in it directly. For example, if large-scale research subsequently influences a government’s social policies, such as those relating to education and health, non-participants’ lives could be greatly affected. If research has not been conducted properly—perhaps it is not reliable or valid, results were falsified, or participants were coerced into giving specific answers—very real harm can follow, even affecting the general population.  

The Researcher

Potential harm to the researcher ought to fall within the sphere of research ethics. This makes sense insofar as it would be unethical for research to proceed if the researcher were likely to be significantly endangered by it. For example, if a researcher wanted to test the comparative safety of two different vehicles when driven into a wall at 60 miles per hour, it would be unethical to allow the researcher to act as the test dummy. A slightly less ridiculous example relates to researchers carrying out interviews in unknown people’s homes: To mitigate potential risk, the researcher might agree to report their location to a supervisor at several predetermined times throughout the day. If they fail to check in, the supervisor will contact them. If this fails, a search will begin.

These kinds of safety issues, usually considered in research study protocols, are designed to protect the safety of the researcher, making the research more ethically sound.

 

 

 

Ethics and Data Analytics

It is important to note that, in general, data analytics projects using existing properly gathered employment data don’t raise too many red flags.

In terms of this course, there are no ethical issues relating to data. This is because the data is fictional and therefore, no one can be harmed by its use. However, at a broader level, there are significant concerns about the ethical use of big data in private organizations. Big data can be described as very large data sets that can explain and predict, through statistical and quantitative analysis, human behavior and interactions. In 2010, after extensive research with several large and multinational organizations, the Centre for Information Policy Leadership developed a set of guidelines to help organizations use the data available to them in an ethical fashion. Included here are the guidelines, adapted from Schwartz (2010), that are particularly relevant in the context of HR analytics. Schwartz argues that, in its use of data analytics, an organization should do the following things (adapted from pp. 2–3):

• Comply with legal requirements.
• Assess, beyond legal requirements, whether its use of analytics reflects cultural and social norms about acceptable activities.
• Assess the impact of its use of analytics on the trust in the company held by a wide range of stakeholders, including consumers, employees, other businesses, government, and non-governmental policymakers.
• Use analytics through accountable processes. Accountability begins with acknowledging that analytics can have positive and negative effects on individuals.
• Develop internal policies that focus on information management and training of personnel.
• Implement appropriate safeguards to protect the security of the information used in analytics. Data security should be reasonable when measured against the kind of information that is collected and processed, as well as the decisions that are made with it.
• Assess whether its use of analytics involves sensitive areas and, if so, accompany it with reasonable safeguards proportionate to the risk.

 

 

The Politics of Research

Keltner, Gruenfeld, and Anderson (2003) describe power as the ability to influence someone by either assisting them or withholding something they value. According to French and Raven (1959), the sources of such power can develop from position, control of resources, information, expertise, social connections, and personal characteristics.

Politics can be defined as "competition between competing interest groups or individuals for power and leadership" (Merriam-Webster, n.d.-b). Combining the ideas of power and politics provides us with the context for politics in research: In essence, politics in research relates to someone’s ability to exert influence over the researcher by assisting or hindering their research.

Cameron and Price (1999) discuss politics in research in four different spheres:

• control: Essentially, this sphere of research politics determines what you can or cannot research. Powerful stakeholders, such as those who fund your research or (if you are performing research within your own organization) your boss, can tell you what you should or should not study.
• access: Access to information, data, or the people who can provide information or data is the lifeblood of research. In business and management research, one of the easiest ways to exert influence is to restrict access to resources.
• honesty: Research participants can influence research based on their willingness to provide honest answers. Participants might choose not to be honest for several reasons, primarily fear of adverse consequences if their superiors were to find out what they reported. Participants can refuse to answer questions, provide partial truths, or lie outright, which can significantly influence research outcomes.
• research as a political threat: Often, business and management research results in an improved understanding of why something is not working as expected (e.g., why an enhanced commission system has not resulted in increased sales) or a criticism of current processes and systems (e.g., the current management style results in disengaged employees). Certain people within an organization might feel threatened by the potential outcomes of your research, attempting to use the politics of control, access, and honesty to prevent or dilute it.

 

Under normal circumstances, an imbalance of power exists in business and management research, and it does not necessarily favor the researcher. The researcher cannot change this, but understanding how power can be exercised (and by whom) can afford them the opportunity to develop strategies to get what they need despite it. It will also be important for them to consider how the power imbalances within an organization might influence the process and outcomes of the research. Information of this sort is usually recognized as a limitation in any research project.

 

 

 

All business and management research is carried out within a particular context. As such, it is very rare to find a project with which there are no ethical or political concerns/issues. Sometimes the topics are innocuous, and concerns are lessened, but because absenteeism is an issue that costs organizations and economies huge sums of money (e.g., according to Integrated Benefits Institute, workplace absenteeism cost US employers $575 billion in 2019 [IBI, 2020]), this is an important and emotive topic. Another difficulty, when dealing with absenteeism specifically is that there are moral issues in terms of fairness and honesty. Also, there are many reasons why a person might be absent, some of which are outside of their control and others that they can control. Reliably distinguishing between these is very difficult. 

In any case, you set out to consider what ethical issues there might be and how you might deal with them, bearing in mind that sometimes risk cannot be eliminated, but can usually be managed.

The things you initially think about are as follows:

• Can individuals be uniquely identified? How? By whom? What might the implications be if individuals with particularly poor absence records were identified? How can you avoid giving information about individuals to management if requested?
• Could the media become aware of the study and how might the public feel about the results? How might the employees, management, shareholders, potential/future employees feel about the results? Might they act differently because of them?
• If controversial findings emerge, (e.g., women are for more likely to be absent than men, workers aged from 25–34 are twice as likely to be absent as those in the 45–54 age category), what should/will/can the employer do?
• What if one particular work group has very high absenteeism rates, does this reflect on the manager of that group? If so, how? Is this fair? Could this impact their career progression? If that manager does have poor leadership skills, will they see it as a learning opportunity, or will they blame the employees and become an even more difficult manager to work for?
• What if an employee who has had significant levels of absence hears that the study is happening and panics about the outcome for them personally?
• What if a union or employee representative group becomes involved and is very unhappy about this research being carried out?
• What if employees don’t want you to use their data for research purposes, given that it was not primarily gathered for that purpose?
• How do you ensure that you safely manage the data that you hold so that it doesn’t fall into the wrong hands?
• What if the HR manager feels that the findings of this study will show them in a poor light and wants to obstruct access to the data?

As you can see from the above questions, the potential issues are manifold. And if you thought further about it, you would surely find more. Clearly, these issues are not relevant to your ESC project (as it is a fictional organization with fictional data). However, it is still important to consider what ethical/political issues might arise if you were researching in a real organization, to give you a sense of the types of issues you’d need to consider.

 

 

Lesson 6 focused on two potentially contentious, but very important, topics in business and management research: ethics and politics. The lesson considered ethics in terms of

• the obligations that influence them,
• ethical considerations for research participants (and others),
• the ethics of data analytics, and
• the specific types of ethical issues likely to arise in HR analytics.

Finally, the lesson discussed potential political influences on research projects.

You also further developed your understanding of hypothesis testing in Microsoft Excel and should now be able to determine whether to accept or reject research hypotheses, a key skill in quantitative research and data analytics.

The next lesson will explore two distinct topics. Firstly, it will consider the nature of quantitative research, specifically discussing

• the main steps in quantitative research,
• concepts and their measurement,
• reliability,
• validity, and
• critiques of quantitative research.

The second topic is sampling in quantitative research, which is important if you do not have the resources to access the entirety of the relevant population (e.g., all employees in a company), but still want to be able to reach robust conclusions about this population. The lesson will discuss

• the importance and relevance of sampling,
• sampling error,
• types of sampling (probability and non-probability), and
• sample size.

In terms of data analysis, you will be learning about

• one-sample t-tests,
• their purpose,
• when it’s appropriate to use them, and
• how to accept or reject hypotheses based on them.

 

 

 

 

References and Resources

 

Cameron, S., & Price, D. (2009). Business research methods: A practical approach. CIPD.

Diener, E., & Crandall, R. (1978). Ethics in social and behavioral research. University of Chicago Press.

French, J., & Raven, B. (1959). The bases of social power. In D. Cartwright (Ed.), Studies in social power (pp. 150–167). Institute for Social Research.

Guenole, N., Feinzig, S., & Green, D. (2018). The grey area—ethical dilemmas in HR analytics: Perspectives from the global workforce. https://www.ibm.com/watson/talent/talent-management-institute/ethical-dilemmas-hr-analytics/hr-ethical-dilemmas.pdf 

Hofstede, G. (2003). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage Publications.

Keltner, D., Gruenfeld, D. H., & Anderson, C. (2003). Power, approach, and inhibition. Psychological Review, 110(2), 265–284.

Merriam-Webster. (n.d.-a). Ethics. In Merriam-Webster.com dictionary. Retrieved April 7, 2021, from https://www.merriam-webster.com/dictionary/ethics?utm_campaign=sd&utm_medium=serp&utm_source=jsonld 

Merriam-Webster. (n.d.-b). Politics. In Merriam-Webster.com dictionary. Retrieved April 16, 2021, from https://www.merriam-webster.com/dictionary/politics

Schwartz, P. (2010) Data Protection Law and the Ethical Use of Analytics, The Centre for Information Policy Management. Retrieved April 19, 2021, from http://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/data_protection_law_and_the_ethical_use_of_analytics__paul_schwartzwhite_paper_2010_.pdf