Emotional Intelligence at Work

18-year journey of a researcher

Chi-Sum Wong

• Publisher: Routledge
• Year: 2015
• ISBN:
  9781317377740
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

Emotional intelligence is a very popular concept since it was made known to the general public in 1995. However, it was under severe criticisms among scientific researchers and a lot of them did not believe that it should be accepted by scientists as true knowledge.

The author of this book, who is one of the pioneers in this topic, spent sixteen years to study this concept. Together with other researchers, they gradually changed the conclusion of early researchers. Using rigorously scientific standards, this research team demonstrated that emotional intelligence is an intelligence dimension that has significant impact on various life outcomes such as life satisfaction and job performance. They developed testable theoretical framework for emotional intelligence in the workplace, and attempted to show that the trainability of emotional intelligence is larger than traditional intelligence concept.

The book looks at, not only the scientific reports, but all the stories behind some of the rigorous scientific studies in the author's 18-year journey. Their choice of research designs and how the designs are suitable to provide scientific evidence to demonstrate the validity of emotional intelligence are also described. Through this book, the process of scientific enquiry and important issues concerning the emotional intelligence concept are revealed in details by vivid stories and rigorous scientific reports.

1
Basic concepts of scientific research

This book is mainly a collection of research studies on emotional intelligence that I and my colleagues have conducted in the past. In conducting the research projects, we followed the scientific standards in our research area – organizational behavior and human resource management. To facilitate the reading of the research papers, some understanding of the scientific approach and related methods is necessary. I am not trying to make this chapter a comprehensive introduction to the scientific approach and methods in this area. In fact, it is out of the scope of one chapter, and some ideas introduced in this chapter may still be debatable among scientists. What I want to introduce are some basic concepts in science and quantitative research that are used and reported in the papers. I will present some of my understandings of science and the scientific method that have been very useful to me. I regard these understandings as basic, and whenever I have doubts, I can go back to the basics and make sure that I am on the right track in conducting the studies. I believe they should have some reference value for people who do not need to conduct scientific research projects as well.

Science and its assumptions

The first important understanding is about the nature of science, especially for social science. What is science? There may be a lot of answers to this question. Similar to other academic disciplines, we can regard science as a particular type of knowledge generated by some kind of predetermined methods. Many people think that science is about rational thinking and knowledge generated from systematic methods such as mathematical proofs and experiments. Some even think that science is the only type of knowledge that is objective and systematic and thus is the most important type of knowledge. All other types of knowledge, such as theology, literature, and history, have a certain amount of subjectivity and are thus not as good as scientific knowledge. Although my training is in social science, I think there is no need to overstate the role of science and the scientific method. To me, science is similar to other types of knowledge, but it specializes in describing the reality in a particular way.

What makes science different from other academic disciplines is the method it uses to describe phenomena that human beings encounter. The particular method that science uses to describe phenomena is related to its assumptions and purpose, which are not the same as for other academic disciplines. When I was a doctoral student, I needed to take several research methodology courses. Interestingly, even though we covered the topic of philosophy of science, very little discussion was about the assumptions of science. This created some difficulties when I tried to discuss with people from other academic disciplines. I gradually realized that in order to clarify the differences between science and other academic disciplines, at least two important assumptions of science have to be noted.

The first important assumption of science is that the phenomena encountered by human beings are real and objectively exist. While individuals may have different perceptions and understandings of the phenomena, we have to assume that the phenomena themselves are real and objective. This makes science different from some academic disciplines such as arts and literature. Arts and literature are about the expression of people’s subjective thinking and feelings, and there is no need to assume the existence of real and objective phenomena.

The second assumption of science is that there are underlying rules or principles behind the phenomena that human beings encounter. For examples, there are principles governing how materials are attracted to each other in the physical world, and these principles are applicable to different types of materials; there are common reasons for why some employees are happier in their jobs, and these reasons are applicable to different types of companies. In academic disciplines such as theology or even history, there is no need to make this assumption.

Recognizing the two assumptions of science can make us understand why science is different from other academic disciplines. As other academic disciplines do not need to make these two assumptions, we should not compare the knowledge generated by them with science. We can also realize the scope and limitation of science. We will no longer believe science can provide answers to all questions, such as whether God exists or not. This is not a scientific question because before science can study a particular phenomenon, we have to assume the objective existence of that phenomenon first. So, whether God exists is an assumption instead of a scientific question. Similarly, a famous ancient Chinese philosopher once said that he had a dream, and in the dream he was a butterfly. After waking up, he wondered whether he was a man having a dream of being butterfly or was in fact a butterfly having a dream of being a man. Science definitely cannot provide an answer to this question because it is not in the scope of science.

With the above two assumptions, we can get an idea of what science and scientific knowledge are about. Science is the attempt to discover the underlying rules or principles behind the “real and objective” phenomena that human beings encounter. In other words, we are trying to describe the underlying rules or principles of the phenomena using languages that can be understood by human beings. The purpose of this description is to explain and predict the phenomena. Once we have a description of a particular phenomenon, we could systematically collect data from the “real and objective” phenomenon and see whether the data confirm our description. If the data do not confirm our description, we may need to revise our description because it is not an accurate representation of the reality. The advantage of scientific knowledge is that we can examine whether it is correct or not from the data collected from the “real and objective” phenomenon. In other words, there is a standard to check the validity of scientific knowledge, which is unique when compared to knowledge from other academic disciplines.

Scientific theories

In order to describe the underlying rules and principles of the phenomena, we have to work on variables. My scientific training is in the organizational behavior and human resource area, and so I will use this as an example. In my area of scientific knowledge, we are trying to discover the underlying rules and principles that can explain and predict the attitudes and behaviors of individual employees and work teams in the workplace. Variables refer to some characteristics of the objects (i.e., individual employees or work teams in my area) that differ among them. For example, age is a variable that differs among individual employees, and it may be one of the factors contributing to their differences in attitudes and behaviors in the workplace. Age is objective and easy to measure. As long as the employees are not telling lies, we can ask them directly, and the responses should accurately reflect their differences in this variable.

By specifying the relationships among variables, such as the impact of employees’ age on their attitudes and behaviors, we can describe and explain phenomena such as why employees have different attitudes and behaviors. However, some variables are not as simply and directly measurable as age. For example, how can we describe differences in employee attitudes? Attitudes are too broad, and thus we need to make this variable more specific and focused. “Job satisfaction” is an example of such an attempt to make a variable clear and focused. In its simplest sense, it refers to the extent to which an employee is happy with his/her job. It certainly will vary among employees: some will be happier, and some will be less happy. It is an attitudinal variable but not directly observable or measurable. This variable needs some abstract thinking to define and conceptualize. It is created to help us to understand the phenomena, and so it is labeled as a concept or a construct.

To make it simple, science is using relationships among variables to describe phenomena so that we can explain and predict the phenomena. As explained above, some variables need abstract thinking to define and conceptualize. A more comprehensive system to describe the phenomena using relationships among variables is called a “scientific theory.” This system includes at least (1) basic assumptions about the reality (e.g., the human nature of seeking happiness and avoiding pain), (2) clear definitions of variables (including abstract constructs such as job satisfaction), (3) relationships among variables and the reasons those relationships exist, and (4) situations where these assumptions and relationships will be valid. This definition of “theory” may be subject to debate because some scholars may include some other requirements for a scientific theory. However, most scholars will agree that the development and refinement of theory so that they can more accurately describe phenomena is the work of scientists.

Qualitative and quantitative research

In order to develop and refine scientific theories, we need to conduct empirical studies to test whether the theory accurately describes reality. Most of my training is about conducting empirical studies in the area of organizational behavior and human resource management. There are two important parts of conducting empirical studies. The first part is the development of testable hypotheses in terms of relationships among variables derived from existing theories, logical deduction, and direct observation of the phenomena. The second part is the systematic collection of data from the real world and testing of whether the expected relationships among variables exist or not.

In collecting data, there are two major approaches. The first approach usually involves very in-depth observations and understandings of the objects under investigation. Usually, it does not involve direct measurement of the variables, and so differences among objects are not represented by numbers, and the relationships among the variables are not tested with mathematical methods. Instead, through detailed understanding of the objects, it can be judged whether they are consistent with the principles prescribed by the theory. As this approach does not require tests by mathematical methods, we refer to this approach as “qualitative research.”

The second approach is to systematically study a relatively large sample of objects. For each object, we measure the variables and represent them with numbers. After measuring the variables for all objects, we can examine the relationships among the variables by mathematical methods. We refer to this approach as “quantitative research.” My training is mostly in the quantitative approach, and so most of the studies reported in this book utilize this approach. The most common way I used to collect data was by surveys. That is, I contacted the participants of the study and measured variables with questionnaires. Then I examined the expected relationships among the variables using various statistical techniques.

Reliability and validity of measurement

In conducting quantitative research, the first important issue concerns the accuracy of our measurement of variables. We need to have evidence showing that our measurement is reliable and valid, that is, that we are actually measuring what we want to measure, especially for those abstract constructs. For example, if we use several questions to measure job satisfaction and come up with a number representing the job satisfaction level of a particular employee by the average response to the questions, we need to show that the employee’s responses are related to his/her actual job satisfaction level. That is, employees who get a high mean score should have a high job satisfaction level, and vice versa. In most of the research papers in my area, we need to report evidence of reliability, which represents the stability of responses to those questions. This is the minimum requirement. If the responses are not stable, we will not be sure what the final number represents.

One common indicator of reliability is internal consistency reliability (or coefficient alpha). It means that if we use several questions to ask about the same construct, the responses to these questions from the same employee should be similar. There is a specific mathematical formula to calculate this reliability coefficient, which ranges from 0 to 1; the closer to 1, the higher the estimated reliability. Other ways of showing reliability may be to ask the same questions again after a short period of time. For example, we might ask the same set of questions concerning job satisfaction again one week later. If the responses have changed greatly, it means the measurement may not be valid. This indicator is labeled as “test–retest reliability.” Another possibility is to have multiple judges. For example, we can ask both the employees and at least one of their colleagues. If the two scores are measuring the same thing, they should be consistent. We call this reliability indicator “inter-rater reliability.”

Reliability only means stability, but it does not guarantee that we are measuring what we intended to. Thus, reliability is only the minimum requirement for accuracy. The concept of validity refers directly to accuracy. Unfortunately, unlike reliability, we do not have one single indicator for validity. We usually need to examine the relationship between the scores that resulted from our measurement scale for a particular construct and other variables in order to gather evidence that the measurement is valid. For example, salary should be related to job satisfaction. If our measurement scale of job satisfaction cannot come up with scores that are related to salary, then the measurement may not be valid. Salary here is referred to as the “criterion variable.” When we have multiple criterion variables and their relationships with our measurement scores are in the expected directions, we can be more confident that our measurement scores are in fact representing the variable we want to measure.

In demonstrating the validity of a measurement scale for a particular construct, we usually use various forms of criterion validity. The most rigorous way is the multi-trait multi-method approach. That is, for multiple constructs, we use more than one method to measure them. Scores from different methods but for the same construct should be highly related. This demonstrates convergent validity. Scores from the same method for different constructs should not be highly related. This demonstrates discriminant validity.

Studies using questionnaire surveys to collect data usually will ask several questions in order to measure one construct. As mentioned, if the questions are set adequately, questions intended to measure the same construct should have consistent answers. One standard statistical tool used to test the consistency of the questions on the same construct is factor analysis. In factor analysis, questions that have similar answers will form the same factor. Technically, we refer this as “loading on” the same factor by different items. Mathematically, loadings can range from −1 to +1. If the loading of a particular measurement item is 0, it means that this item has nothing to do with the factor. If the loading is closer to −1 or +1 that means that particular item is reflecting the underlying factor.

Thus, if we are measuring more than one construct, and each with multiple questions, we can test whether the questions are set adequately by means of factor analysis. That is, multiple factors should be formed, and each factor should consist mainly of the corresponding questions. Mathematically, this means the loadings of those questions are high on the same factor and low on other factors. If this is not the case, then something may be wrong. This is a common statistical tool used to show reliability and validity of the survey questionnaire items. It is also applied to analyze data collected by the multi-trait multi-method approach.

Descriptive and inferential statistics

After demonstrating the accuracy of measurement, the next step is to show the relationships among variables. Here, we need the help of statistics. The most common statistical concept to show the relationship between two variables is the correlation coefficient. It ranges from −1 to +1. When it is 0, it means the two variables are unrelated to each other. The closer it is to −1 or +1, the stronger the relationship. The sign of positive and negative indicates whether the relationship is in the same or the opposite direction. In statistical terms, we usually use the amount of variance explained to describe the closeness of the relationship. If the correlation coefficient equals 1 (or −1), it means the variation of one variable can be fully explained by the variation of the other variable. If it is not 0 but less than 1 (or −1), this means that only part of the variation of the variable can be explained by the variation of the other variable.

There is one problem in showing whether the relationships among variables actually exist when we have only a sample of objects under investigation. We can never be sure that the correlation coefficient calculated from this sample is the same as for the whole population which includes all objects of interest. Thus, we try to infer whether the relationship exists or not in the population by the data we collected from the sample. We call the reporting of the sample data descriptive statistics. In inferring the population relationships, we refer to it as inferential statistics, which involves statistical tests or hypothesis testing.

We cannot be sure about the situation of the population because we have data only from the sample. However, we can calculate the probability that the population correlation coefficient is not zero based on the sample data. In social science research, we want to be conservative. Thus, only when the probability is less than 5% will we conclude that the relationship between two variables actually exists in the population. In statistical terms, we will say our evidence from the sample is significant enough for us to make this conclusion. As a common standard, the symbol * or p < .05 is used to represent statistical significance at this predetermined probability of 5%. Otherwise, we will say the evidence from the sample is not significant, and we cannot conclude that our expected relationship between the two variables really exists. We probably need more evidence in order to draw such a conclusion.

Regression analysis and cross-level predictors

It is common that attitudes and behaviors will not be affected by only one factor. Therefore, our hypotheses about the relationships among variables usually will not be limited to only one predictor (usually labeled independent) variable and one criterion (usually labeled dependent) variable. If we have several independent variables to explain the variation of one dependent variable, we can use the statistical technique of regression. Basically, it will produce a statistic called R², ranging from 0 to 1. The meaning of R² is similar to that of the correlation coefficient, but it means the proportion of the variation of the dependent variable that can be explained by all the independent variables. Of course, after testing the significance of the population R² from the data of the sample, we can also test whether each independent variable has an effect on the dependent variable. The statistic is called the...

Table of contents

• Cover
• Title
• Copyright
• Contents
• List of figures
• List of tables
• Preface
• 1 Basic concepts of scientific research
• 2 Beginning of the journey: is emotional intelligence a worthwhile construct?
• 3 Sufficient evidence to make yourself comfortable?
• 4 Provide convincing evidence after the initial stage
• 5 Alternative measures of emotional intelligence
• 6 Cultural issues concerning the emotional intelligence construct
• 7 Emotional intelligence and human performance
• 8 Emotional intelligence training
• 9 Emotional intelligence and leadership
• 10 Alternative conceptualizations of emotional labor
• 11 Summary and review
• Appendix: what’s next?
• References
• Index