Women and Leadership Project
edwardgoe
CH1_IntroductiontoWomenandLeadership.pptxIntroduction to Women & Leadership
Week 1
Chapter 1: We Are the Leaders We’ve Been Waiting For
Welcome to your Women and Leadership course! This is the first slideset for Week 1.
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Goals
The goals of this presentation are to get a sense for what this course will be covering throughout the semester, and go over some really important scientific terminology that will be important for your understanding of the research we will be discussing in this course and of the assigned course readings.
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Intro to course
Scientific terminology
We are the leaders we’ve been waiting for
Chapter 1
The assigned text for this course is quite nice in that it is a fairly easy read given that its intended audience is college students. The first chapter of this book briefly discusses the contents of the rest of the book, so we’re just going to go over a few key points that are important to note early on. While this book is a good read, I do feel that it glosses over a lot of really important terms and key concepts relevant to a course on women and leadership, so each week I supplement this book with other readings that provide a lot more in-depth information on important concepts, in the form of empirical research and chapters from other texts on women and leadership. While those additional readings are not required, they will help you develop a deeper understanding of much of what we’ll be talking about in this course. They are also going to be extremely useful in helping you craft your responses to the weekly discussion post questions, so I do encourage you to, at the very least, skim over those additional readings posted on Canvas each week.
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This is one of my favorite TV shows… If you haven’t watched it, give it a try! (In case the video doesn’t work: https://www.youtube.com/watch?v=6abmPxZdzxs&feature=emb_logo)
In this scene, Jessica (played by Zooey Deschanel) is frustrated with her new job, where her boss is excluding her from some seemingly important and private weekly group meeting. Her best friend, Cece (played by Hannah Simone), recounts her own experience with having to pump her breastmilk while at a meeting because the men on the team did not want to reschedule. At the end of the episode, it’s revealed that Jessica wasn’t actually missing out on anything– the weekly group meetings she was never invited to were for divorced, single fathers! Nonetheless, this is a great example of the types of situations women are faced with in the workplace.
Does this New Girl scenario seem reminiscent of a time in your life? If not, consider some of these other scenarios:
You care deeply about women’s issues. Members of your family refer to you as a femi-Nazi. (I can relate to this– my brothers use “feminist” as a taunt all the time to tease me!)
You disagree with a colleague at work and after you voice your objections someone asks if you are having your period.
You are the only woman on a team working on a group project in class. The teacher suggests someone in your group takes notes. Your group members all turn to you and hand you a pen.
We often experience these very subtle forms of stereotyping and discrimination in our culture. Some discrimination is even perceived as benevolent, such as when people tell you that women are more compassionate. This type of backwards compliment can actually be used to get you to take on unpaid emotional labor in the workplace.
These types of statements also support binary thinking about both gender and leadership. If women are, by nature, caring and collaborative, then the opposite must also be true– that men are naturally more assertive and decisive, so they are built for leadership. This effectively leaves women out of the equation.
In this class, we will be challenging and investigating these assumptions.
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Current Moment for Women and Leadership
We are living in a critical moment for thinking about both gender and leadership
The news, and likely your social media, are filled with stories about the #MeToo and Time’s Up movements, issues of sexual harassment in the workplace, the attack on reproductive health and rights, issues of female representation in politics and across industries, pay disparities, sexual violence and rape culture, and the global status of women, to name a few.
You may have also read about people challenging traditional binary approaches to gender, such as parents deciding not to assign gender to their children at birth, people asking for options on government forms that reflect a third, nonbinary gender option, and the ongoing debate about access to gender inclusive bathrooms in schools.
While these issues are not new, they have gained considerable attention in recent years. Many suggest that we are living in a moment of cultural reckoning where online organizing, renewed activism, and heightened awareness of discrimination are reinvigorating conversations about women’s issues, feminism, and a dire need for inclusive and representative leadership.
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Leadership Can and Should be Developed
Much of the literature on women in leadership glorifies strong competent women who overcame tough circumstances to lead and make a difference
“SHE-roes”
One of the side effects of SHE-ro narratives is that, while they may inspire some, they may discourage others
“I will never be a leader if I have to always be put together and know exactly who I am and what I want in life”
This will be especially true if you do not identify with the identities or experiences of the SHE-roes
One of the main points in Chapter 1 of We are the Leaders We’ve Been Waiting For is that leadership can and should be developed. Leadership is not a trait that some are born with, and others are not. Anyone can be a leader, and being a leader can mean very different things!
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In this class…
We will tackle the following questions:
What is women’s status as leaders in the workplace, politics, and in the global community?
How is gender constructed?
What is the relationship between gender and social power?
What kinds of behaviors/traits/roles do we associate with leadership?
What are the social, cultural, psychological, and structural barriers women face as leaders?
What is your personal theory of leadership and to what extent does it involve your gender?
How can we advance women leaders and end prejudice against female leaders?
Refresher on scientific terminology
Now, let’s turn over to a refresher on scientific terminology. This might be super redundant information for some of you who have already taken several psychology and statistics courses, but it’s always good to hear and see these concepts again because it helps you to remember them better. For those of you who may not be psychology students, this might be entirely new information to you! So, we’re going to start with the foundation of research: the scientific method.
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The Scientific Method
The scientific method is a scientist’s way of thinking- the logic used in systematically asking and answering questions
Science is a method of inquiry, or a way of thinking, that uses:
Empirical observation
Theory
Public methods
Reproduceable results
Steps to the scientific method:
Develop a research idea or hypothesis based on theory
Choose a research design
Choose subjects
Decide on variables and ways to assess or manipulate them
Conduct study
Analyze results
Report results
Empirical Research
The term “empirical” denotes information gained by means of:
Observation
Experience
Experiment
A central concept in the scientific method and in science generally is that all evidence must be empirical, or empirically-based
Dependent on evidence or consequences that are observable by the senses
In this course, we will rely heavily on empirical research in the social sciences, and you will use it to add support to your arguments in the discussion posts you write.
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Terms in Science and Statistics
2 ways to approach the study of relationships between variables…
Correlational method: determines the extent to which 2 or more variables COVARY or correlate
Experimental method: direct manipulation and control of variables to determine extent of causality
If you’ve already taken research methods II as a psychology major, you’d be familiar with the experimental method. Remember that you had an independent variable (or two) that you manipulated to see how it affected your dependent variable? That’s an experimental method!
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Correlations
Take a look at this photo and hold it in your mind as we go through the next couple of slides– we’re going to be returning to it.
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Defining a Correlation
Correlation tells us two variables are related
Types of relationships reflected in correlation:
X causes Y or Y causes X (causal relationship)
X and Y are caused by a third variable Z (spurious relationship)
Positive and Negative Correlations
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When you plot the relationship between two variables on a graph to test the correlation between the two, you might get something that looks like these graphs. The graph on the left indicates a positive correlation between the two variables. As one variable increases, so does the other. The graph on the right indicates a negative correlation, such that as one variable increases, the other decreases. Let’s use a real example to better understand these graphs.
Let’s say we are interested in examining the relationship, or correlation, between video game usage and aggression levels in children. A positive correlation between these two variables would suggest that as the value of video game usage increases, the value of aggression also increases. A negative correlation, on the other hand, would suggest that as the value of video game usage increases, the value of aggression decreases.
Variables with No Correlation
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You can also have no correlation or relationship between two variables. So let’s go back to our example of video game usage and aggression levels. It could be the case that there is actually no relationship between the two. Video game usage is not associated with higher or lower levels of aggression– in other words, there just isn’t a relationship there. These two graphs show what variables with no correlation would look like when plotted. See how there is no clear direction the dots are going in? It just looks like a blob of dots. The previous two graphs showed all the little dots, or data points, following a clear positive or negative direction.
Correlation vs. Causation
Causation means that whenever there is a change in an explanatory (independent) variable, it should cause a change in the response (dependent) variable.
Correlations: Correlations between two variables are extremely common and easy to find. However, saying that two variables are correlated in NO way guarantees that there is causation.
Put another way: Having correlation without causation means that changing the explanatory variable will NOT guarantee a change in the response variable.
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Let’s look a little closer at this photo from earlier…
This photo suggests that there is a correlation between eating ice cream and sunburns..
However, knowing what we now know, this does not mean that eating ice cream causes sunburns! Summer weather is the explanatory variable here.
That is, summer weather leads to an increase in ice cream sales and in sunburns, resulting in a relationship between ice cream and sunburns.
Very important!
A correlation does not mean that there is causation
As you know, correlation means that there is a relationship between two variables. Causation means that if you see a change in your explanatory variable, it should cause a change in the response variable
Even if a correlation is very strong, this is not by itself good evidence that a change in x will cause a change in y
Even if one thing seems like it should cause the other, you can’t tell just by knowing that they are correlated
In the real world…
Often (very, very often!) people report “associations” (i.e. correlation) between two variables. Yet upon further examination, it turns out that there is not ANY causation whatsoever!
As humans, though, upon hearing about “associations” we often jump to an assumption of correlation.
Most of the time, the causation simply is NOT there.
This is why it’s especially important to think like a scientist or researcher when assessing the credibility and truthfulness of headlines like the ones shown on this slide. Had you not known any better, you would have assumed that eating bacon causes leukemia, or that drinking beer makes you live longer, which is certainly not the case! Understanding how research works makes you a more informed consumer of information.
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Example of Correlation vs. Causation
‘‘The correlation between workers’ education levels and wages is strongly positive”
Does this mean education level “causes” higher wages?
We don’t know for sure!
Recall: correlation tells us two variables are related BUT does not tell us why
Let’s take a look at this example: the correlation between workers’ education levels and wages is strongly positive. Does that mean that education level causes higher wages? Well, we can’t determine that based on a correlation alone! Remember that correlations tell us two variables are related, but they do not tell us why they are related.
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Example of Correlation vs. Causation
Possibility 1
Education improves skills, and skilled workers get better paying jobs
Education causes wages to
Possibility 2
Individuals are born with quality A which is relevant for success in education and on the job
Quality (NOT education) causes wages to
In the last example, there are two potential explanations for the correlation between education level and wages. The first possibility is that education improves skills, and skilled workers can land higher-paying jobs. In this case, education is causing wages to increase.
The second possibility is that some people are born with a certain trait that is relevant for success in education and in the workplace. In this case, the trait, not education, is causing salaries to increase.
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Another Example…
Let’s say there is a correlation between social status and health. There are 3 possible explanations for this correlation, or relationship, between these two variables. The first explanation is that social status leads to health. Perhaps the wealthier you are, the healthier you are (because you have access to healthcare, healthy foods, healthy lifestyle, etc.). Alternatively, health could lead to social status. If you’re always sick, maybe you can’t keep a stable job and are financially unstable, so your social status declines. The final explanation for this correlation could be that both social status and health are actually explained by a third variable, Z (i.e., education level).
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Without proper interpretation, causation should not be assumed, or even implied.
In order to know if one variable causes another, a true experiment must be performed.
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Second level
Third level
Fourth level
Fifth level
Experimental Research
So let’s talk a little bit about experimental research, which we’re going to be referring to a lot throughout this course. A lot of the research in the social sciences is experimental in nature, so it’s important to understand what this type of research entails.
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What is an Experiment?
So, an experimental is a type of research method in which conditions are controlled so that one or more independent variables can be manipulated to test a hypothesis regarding a dependent variable. Experiments allow for the evaluation of causal relationships among variables, while eliminating or controlling all other variables (typically called “confound” variables).
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Research method in which:
conditions are controlled
so that one or more independent variables
can be manipulated to test a hypothesis
about a dependent variable
Allows:
evaluation of causal relationships among variables
while all other variables are eliminated or controlled
Independent and Dependent Variables
Let’s go over what dependent and independent variables are. An independent variable is manipulated and is independent, like its name suggests, of any other variable. The dependent variable, on the other hand, is the criterion by which the results of the experiment are judged. This variable is expected to be dependent, again like its name suggests, on the manipulation of the independent variable.
So think about our previous example when discussing correlations: identifying the relationship between video game usage and aggression levels in children. If we wanted to test this using an experimental method, we might have two conditions: one in which children play with video games every day, and one in which children do not play with any video games. Here, our independent variable is the amount of time spent playing video games, and we have two levels of our independent variable: either play, or no play. The dependent variable in this example is our outcome of interest. What are we interested in measuring depending on whether kids play or do not play with video games? Aggression levels. So, our dependent variable in this case is aggression levels, because this will change depending on our manipulation of the independent variable.
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Dependent Variable
Criterion by which the results of the experiment are judged
Variable that is expected to be dependent on the manipulation of the independent variable
Independent Variable
Any variable that can be manipulated, or altered, independently of any other variable
Hypothesized to be the causal influence
Components of an Experiment
There are a couple of key components in an experiment. The first is the manipulation of independent variables, referred to as experimental treatments. The second is the use of an experimental and control group. An experimental group is a group of participants who are exposed to the experimental treatment. The control group, on the other hand, is not exposed to the experimental treatment. Experiments also use randomization, which is the random assignment of participants to the experimental or control group.
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Experimental Treatments
Alternative manipulations of the independent variable being investigated
Experimental Group
Group of subjects exposed to the experimental treatment
Control Group
Group of subjects exposed to the control condition
Not exposed to the experimental treatment
Randomization
Random assignment of participants to the experimental or control group
Randomization
Assignment of subjects and treatments to groups is based on chance
Provides “control by chance”
Random assignment allows the assumption that the groups are identical with respect to all variables except the experimental treatment
Randomization in experimental studies allows for “control by chance” and allows the assumption that the groups are identical with respect to all variables except the experimental treatment. If a study does not use random assignment, it is not an experimental design. This would actually be called a quasi-experimental design, which does not use random assignment but shares all of the other key components of an experimental design.
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This is a conceptualization of how random assignment works in experimental studies. We have a sample of participants that we want to randomly assign to one of two groups: the experimental or the control group. We can randomly assign participants to one of two groups using a variety of methods, such as rolling a die (even number, you get experimental group; odd number, you get control group) or picking a number or letter out of a hat.
That is, half of our participants will be randomly assigned to the experimental condition, which in this case is watching violent TV. The other half of our participants will be randomly assigned to the control group, which is watching nonviolent TV. For both groups, we are measuring levels of aggression after watching TV.
Can you guess what the researchers in this scenario are interested in examining based on that information? Think about it for a minute…
The researchers are interested in understanding how watching violent TV influences levels of aggression. What do you think about that relationship? Would you hypothesize that watching violent TV leads to more aggression, or less aggression?
A (brief) history of the field of psychology
That’s it for scientific terminology! Not too bad, right? Now, let’s super briefly go over the field of psychology and its history as a womanless science.
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Early Psychology: A Womanless Science
Check this out: if you Google “historical psychologists”, your top results will all be men
This is always fun to do: if you Google “historical psychologists”, you’ll notice that your top results will all be men. And no, it’s not just a coincidence!
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Early Psychology: A Womanless Science
Psychology as a field was itself a “womanless” field for some time
It dismissed women’s perspectives and did not include them as researchers or even as participants in their research!
Take a look at these quotes from historical psychology figures:
“We must start with the realization that, as much as we want women to be good scientists or engineers, they want first and foremost to be womanly companions of men and to be mothers.” (Bettelheim, 1965, p. 15)
“Women’s somatic design harbors an ‘inner space’ destined to bear the offspring of chosen men, and with it, a biological, psychological, and ethical commitment to take care of human infancy.” (Erikson, 1964, p. 586)
In these analyses, women’s motivations did not extend beyond marriage and childrearing
The field of psychology was a “womanless” field for a very long time– it dismissed women’s perspectives and did not include them as researchers, or even as participants in research! If you look at very early studies published in the psychology literature, the samples used consisted of only men! Women were completely excluded from being examined in research.
To give you a better sense of what psychology looked like back in the mid-1960s, take a look at these quotes from some psychologists.
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The Good News for the Field of Psychology Today
More journals and research on sex and gender
More women scientists
More studies including men and women as participants
Some even include women only!
More examinations of gender similarities
The good news is that the field of psychology has made tremendous strides in its research on sex and gender. In fact, there are now more journals focusing on sex and gender than ever before, there are more women scientists, more empirical research is including men and women as participants (some even including women only!), and more research is focusing on gender similarities, rather than differences.
In case you were unsure, journals are online databases where empirical research articles are published. Depending on the discipline, there could be several different journals that publish research.
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Here’s a visual representation of what we just discussed. It’s a little outdated, as it stops at 2009, but still makes an important point. You can see the x-axis is the publication year, and the y-axis is the number of articles published in psycINFO, an online database that stores thousands of articles published in various journals. This graph shows that the number of articles published that focus on all genders has steadily increased with the passage of time, while the number of articles published focusing on human sex differences and/or main sex and gender terms is actually declining. Moreover, these articles seem to be published at a much lower rate than articles focusing on all genders, suggesting that the field of psychology is moving towards more inclusive research as a whole.
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End
This is the end of the first slideset in Week 1. Typically, each week there will be one or two slidesets posted for you to view. I recommend viewing or listening to the audio for both slidesets each week prior to completing the weekly quizzes and discussion assignments, as these are based on your understanding from both lectures. I also recommend downloading the powerpoint-only versions of each slideset, as there are a lot of notes, references, and links that I include in the “notes” section of each slide. You’d only be able to see them if you download the powerpoints directly onto your computer. Downloading these slides and having them easily accessible will come in handy when you have to take your quizzes, too.
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