Module 1

Module 1

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Module 1: Introduction and Methods in Psychology Wednesday, July 5 – Monday, July 10

Required

Reading/Viewing:

Principles of Psychology, Chapters 1–2 & Appendix

(Pages 1-6; 29-59; 581-604)

Review APA (American Psychological Association)

Ethical Principles (page 1–middle of page 4; section

8–8.09 (middle of page 10–page 11) [Available via

the Course eReserves.]

Module 1 online content

Discussions: Discussion 0: Introduce Yourselves

Module 1 Discussion:

Initial responses due Friday, July 7, 9:00 AM ET

Two peer response due Monday, July 10, 9:00

AM ET

Leader response due Wednesday, July 12, 9:00

AM ET

Assignments: None

Assessments: Syllabus Quiz due Friday, July 7, 5:00 PM ET

Live Classrooms

(Faculty):

Wednesday, July 5, 7:30–9:00 PM ET

Activity: Complete Module 1 Review and Reflect, due

Tuesday, July 11, 11:59 PM ET

Module 1A: A Model for Understanding Psychology

QUESTION 1 OF 2

What are some of the examples of trauma that were mentioned? Check all that apply:

Tsunamis Accidents Fires Terrorism Storms

CHECK ANSWER

VIDEO FOLLOW-UP

Learning Objectives

By the end of the session students will be able to:

1. Describe a multi-level framework for considering phenomenon in the field of psychology and give an example.

2. Describe types of studies in psychology and outline their uses, strengths, and weaknesses.

3. Describe the difference between correlational (non-experimental) and experimental psychological research and gives examples of each.

4. Describe the research process – from idea to peer review.

5. Describe the three principles governing research ethics. Give examples of each.

Introduction to the Model for Understanding Psychology

Remember back to the course introductory video that was on the course home page, and see if you can respond to the question below.

What do you think of when you hear the word “psychology”?

I always like this cartoon, as I think it represents a common perception of psychology—a therapeutic endeavor where we seek to understand how early

experiences caused folks to have problems (often, how it’s all your parents’ fault!!). Although humorous, it’s a pretty poor idea of what psychology is.

Instead, psychology covers many things, but, at its heart, it is a science of behavior. It shares many principles with other sciences. Psychological

science can be basic—when we try to understand how and why individuals behave the way they do. This is really knowledge for the sake of

knowledge, and that’s important. For example, we might want to understand how human memory works, what factors are associated with healthy child

development, how individuals develop anxiety problems. Psychological science can also be applied—when we try to use that basic knowledge to

change (hopefully improve) human behavior. For example, we may want to use what we know about human memory to improve student performance;

we may want to use what we know about healthy child development to improve early childhood education or parenting; we may want to use what we

know about how anxiety develops to better treat and prevent problems with anxiety.

One of the things that makes psychology so challenging as a field is its tremendous breadth. Psychologists are interested in how the brain works, how

social situations impact individual performance, how early experiences influence the later development of mental health problems, and lots more. The

sheer range of potential questions is overwhelming. This is what can be referred to as the CONTENT (what we know). This also means that we have a

broad range of strategies and methods that are part of psychological research. This is what can be referred to as the PROCESS (how we come to know

it). Throughout this course we will focus on some of these strategies and methods.

But we need to start with a framework—a framework for thinking about complex problems in psychology. The model that you see below is an old but

still influential one—Uri Bronfenbrenner published this model in 1979. What does it show? It illustrates how we have to consider the individual in

context. In his model the individual is in the middle, and they are embedded within other systems. The family, schools, neighborhoods, political

systems, social structures all surround this individual. Now Bronfenbrenner, as a sociologist, was really most interested in these larger systems.

Bronfenbrenner Model of Ecological Systems Theory

Source: Adapted from Hchokr at English Wikipedia

As psychologists, our focus is on the individual. Still, we must think of this individual in context. So, in next illustration, I include an adaptation of this

model that focuses on individual functioning—this will be our framework throughout the semester. Each of these circles is a “level of analysis”; to

understand complex ideas in psychology we often need to understand them at each of these levels:

Understanding Behavior Model Click each of the six labels to learn more below

Examples of the Model for Understanding Psychology

I’d like to illustrate how we can think about complex topics in psychology using this kind of general framework. So, I’m going to use two topics to

illustrate: youth aggression and anorexia nervosa.

Let’s talk about youth aggression

We need a little background. Aggression is a common problem. What’s the time of life when we experience the highest level of individual aggression?

Many of you probably guessed the teenage years, but actually it’s those toddler years! Toddlers are cute, but they can kick, hit, thrown things, bite!

Thank goodness it gets better pretty quickly . . ., and they are small enough that the adults around can typically manage it. Aggression in teens can

lead to many problems, like delinquency and criminal incarceration. The age when a youth begins to show significant aggression begins is often

important in predicting future aggressive behavior. Kids who have high levels of early aggressive behavior (elementary school and younger) seem to be

a different, and potentially more worrisome group, than those who have high levels of aggressive behavior beginning in the teen years (which is usually

more short-term and associated with peer influences). We can also define aggression in various ways—physical, verbal, and/or relational. Physical

aggression is pretty clear—hitting or assaulting others are examples. In verbal aggression we insult, demean or threaten others. In relational

aggression, relationships are manipulated—isolating, shunning, spreading rumors, destroying reputations. Aggression can take place in person or

online; indeed, online or cyberaggression is associated with negative health impacts on youth victims.

Think about youth aggression - In what ways have you experienced this?

Compose your thoughts below and click "save" for the option to review your response later.

Compose your thoughts here

Jot Your Thoughts

Save 

So, let’s use our framework to understand youth aggression:

1. The biological level

Wow, we could consider many things, and here are just three. First, human aggression, and that of many other

mammals, is generally higher in males then females, particularly after puberty. Second, we also know that the

male sex hormone testosterone is associated with higher aggression. Administration of testosterone in mice leads

to increases in aggression. Third, we also know that levels of serotonin—a neurotransmitter in the brain—can be

associated with higher aggression. So we know the biological level is important.

2. The psychological level.

Let’s back up a little. Those who study youth aggression distinguish between two additional types: Reactive and

Proactive. Reactive aggression what you might imagine—folks who are being attacked often respond in

kind. When one child hits another child and the second child hits back, that second child is engaging in reactive

aggression. You think someone has insulted you, maybe you insult back. Reactive aggression is emotional and is

in retaliation to perceived slights or aggression from another individual. Proactive aggression, also called

instrumental aggression, is when an individual uses aggression to get something they want or achieve some other

end. Proactive aggression is goal oriented. When a child twists another child’s arm behind their back to force them

to give up their lunch money—that’s proactive aggression. Which one seems a little more concerning to you? (I

hope you said proactive). Although, proactive aggression is also used in war situations and in some sports

situations—understandable.

We know that the psychological processes involved in these two types of aggression are different. Individuals who

are prone to reactive aggression often have heightened “threat perception”—they expect others to be aggressive

toward them. You could say “their antennae” are up; they expect to be attacked (physically, verbally, or

relationally), and sometimes when we expect to see something we see it even if it’s not there! Let’s say you have

a line of 3rd graders all jostling to see something and one child bumps into another. The child that gets bumped

can think “that child bumped me because they got bumped by another classmate” or “that child bumped me by

mistake” or “that child bumped me on purpose”. The child who makes that last assumption—the “on purpose”

assumption—is more likely to respond with reactive aggression —maybe bumping or hitting the other child back in

response.

The situation is different for proactive aggression. Remember in proactive aggression, the perpetrator uses

aggression to get something. Research has demonstrated that children who tend to use proactive aggression

evaluate problem solutions a little differently than children who don’t tend to use proactive aggression. In one

study children were all presented problems and potential solutions. Proactively aggressive kids tended to evaluate

aggressive solutions more favorably than non-aggressive kids. So proactive aggression seems to be a strategy.

This distinction between reactive and proactive aggression also applies to individuals who engage in domestic

violence or intimate-partner abuse. In domestic violence an intimate partner, say a husband, is aggressive toward

the other intimate partner, say his wife. Some spouses do this because they feel they are being “attacked”,

insulted, or demeaned by their spouse and respond with aggression. In this case they are engaging in reactive

aggression. For others, they use aggression to get what they want in the relationship—bullying or abusing their

partner. These folks are engaging in proactive aggression. This is not to say that one type of aggression is

“better”; but by understanding the psychological level we can understand more about how and why aggression

occurs. Maybe, we can also be more effective in STOPPING aggression.

3. The family level

We know that individuals develop their thought processes and emotions within the context of close relationships.

Interestingly, aggression tends to run in families. Children who are raised in physically abusive families are more

likely than those raised in non-abusive families to go on to either be abusive or be in abusive relationships. You

could say that we learn aggression at our parents’ knees. One way this happens is through modeling—we see how

our parents solve problems and go on to solve problems in a similar fashion, and sometimes that’s an abusive

fashion. Another way this happens is through shaping our expectations—we may learn to expect to be attacked or

demeaned, or we may learn at home that aggression can work for us. That is to say, these experiences at the

family level shape the psychological level. Finally, we know that parental monitoring is important—high aggression

and other delinquent behaviors are less likely when parents know what their kids are up to. We hear a lot about

“helicopter parenting” these days (when parents are overly involved), but lack of parental involvement can be a

real problem too! Family involvement and parental monitoring are important.

4. The larger group level

Some groups consider aggression to be a valid way to solve problems. Think about the mafia or violent street

gangs. In this groups violence is seen as a legitimate way to solve problems and get one’s needs met. This larger

group level of analysis can impact the psychological level—when your group says it is ok you begin to believe that

as well. Of course the larger group level can also impact the family level by influencing the ways in which parents

relate to their children and model behavior.

A particularly interesting line of research has examined “deviancy training” among young delinquent men, where

they reinforce and “one up” one another for their aggressive behavior. You can also see this happening for sexual

aggression—with one young man boasting about his sexual conquests, leading to more and more outlandish

boasts and “legitimizing” more egregious, harassing, aggressive (and possibly illegal) behavior.

5. The cultural level

In some cultures, violence is more normative than in other cultures. If you look at violent crime rates you see

startling differences across countries. Indeed, some cultures glorify violence and aggression. In the USA we

frequently see movies where violent solutions are dramatized, heroes use violence to solve problems, and athletic

events glorify aggression. We believe in “letting him [our adversary] know who is the bosss”—dominance is a big

deal in American culture. Other cultures emphasize getting along, playing your role in the larger, very highly

valued group.

An interesting example of a powerful cultural factor impacting aggression is what is known as a “Culture of

Honor”. In this system men (almost always men) feel that threats to their honor or reputation (i.e, culturally

unacceptable behavior of a family member, insults, threats, etc) must be answered with violence to re-establish

that honor or reputation. For example, in some cultures female chastity is highly valued, and a woman having

sexual relations outside of marriage (whether consensual or not) is a stain on the family’s honor. In this cultural

framework, killing one’s own female family member may be considered appropriate aggression to restore the

family’s honor.

So there are important cultural factors that impact aggression.

Another example 

What do you know about anorexia nervosa? Anorexia nervosa is a mental health problem that impacts about 0.6% of individuals. Symptoms include self-starvation, severe weight loss and low

body weight. It tends to emerge during the teenage years and to predominantly impact young women. Although not the most prevalent mental health

disorder, Anorexia Nervosa has the highest mortality rate of any mental health problem—about 20% of those with a diagnosis die as a result of it. Even

when not fatal, it can have terrible consequences. It’s been said that it impacts the “three B’s"— bones, brains, and babies. Let’s focus on bones.

Bones

When young people are teenagers their bones are growing and strengthening, with calcium increasing bone health. Individuals with Anorexia

Nervosa aren’t taking in enough calcium and may end up with brittle bones—osteoporosis can emerge very early in individuals with a history

of Anorexia Nervosa.

Brains

Young people are also undergoing tremendous growth in their brains, and fat in the diet is important in this process, as it promotes

myelination of axons in the cells of the brain. We will talk more about this process when we learn about the brain. If individuals do not have

enough fat in their diets, brain development may not be optimal.

Babies

In order for young women to menstruate, they have to have a certain percentage of body fat. One common complication of Anorexia Nervosa

is a lack of menstruation or what we call amenorrhea; this can have a long-term impact on young women’s fertility. Overall, AN can have

long-term health impacts even when it gets better or resolves.

So how can we use our model to understand Anorexia Nervosa? Again, the framework:

1. The biological level

The hypothalamus is a small region deep within the brain that controls some aspects of our physical functioning

and our behavior.

Experiments with rats demonstrate that damaging a certain part of the hypothalamus can lead to a rat overeating

to a point of severe obesity—an enormous rat! Damaging a different part of the hypothalamus gives you a rat that

starves itself. So the hypothalamus seems to be very important in the control of eating behavior.

Studies using brain scanning (MRI) suggest that other areas of the brain may be involved as well, including the

dorsolateral prefrontal context, parietal cortex and anterior cingulate gyrus. You won’t need to know the particular

regions, but I do want you to recognize that this level of analysis (biological level) is important.

2. The psychological level

Self-Perceptions are crucial as well. One thing we’ve learned is that people with anorexia nervosa tend to see

themselves, not as they are, but in a distorted way. You and I may look at the person and think “what an

extremely thin person!” whereas they may look in the mirror and think “I’m still so heavy!” They may focus on

one part of the body that seems like a problem to them— a thigh perhaps—and focus almost exclusively on that.

Personality plays a role too. Several personality characteristics may predispose someone to develop AN,

particularly perfectionism. Individuals who are perfectionistic have very high standards for themselves and little

room for mistakes or errors. Internalization of a thin ideal—when folks have thoroughly accepted the idea that

thin is beautiful – also places a person at increased risk for developing anorexia nervosa. Understanding this

psychological level has really helped in the development of better treatments for anorexia nervosa.

3. The family level

Although not always, anorexia nervosa seems to happen more in families where parents have very high

expectations for success—this is a disorder that is more common in higher socio-economic groups. Also, anorexia

appears to occur more when parents emphasize appearance as an important value—perhaps this is where the

internalization of the thin ideal begins. Again, this level of analysis impacts other levels, including the

psychological level.

4. The larger group level

Certain groups are more likely to develop anorexia nervosa. In general, rates of anorexia nervosa are higher in

groups where appearance (and especially a thin appearance is valued)—models, dancers, gymnasts. Gay men

appear more likely than non-gay men to develop eating disorders. Group values about appearance and its

importance may be internalized into one’s own value system—part of the psychological level. Again, one level can

shape others.

5. The cultural level

In our culture, appearance is something that is more highly valued in women (as compared to men), and youthful

appearance (especially for women) is particularly valued. We see this in the media all the time.

As an interesting case study and an example of the importance of cultural factors, rates of eating disorders like anorexia nervosa were exceedingly rare

in Fiji prior to 1995, and behavior like self-induced vomiting and binge eating were low. When researchers returned in 1998 following the introduction

of Western television shows, they found greatly increased rates of these behaviors in a sample of girls, and the majority considered themselves “too

fat”. This speaks to the important role of media in shaping self-perceptions and potentially risk of eating disorders like anorexia nervosa. These cultural

variables also impact parenting. So the levels of analysis interact with one another—one influencing the other.

Important Considerations about Culture

Psychology has been criticized for NOT considering culture adequately. Many of the studies that are conducted do not represent the broader population.

Indeed, psychology (among other disciplines) tends to focus on what are known as WEIRD populations—Western, Educated, Industrialized, Rich, and

Democratic. Thus, it is challenging to generalize to different populations. In the United States of America, research has tended to focus on White

middle- and upper-class populations; lower-income and people of color are not well-represented in our research studies. There has been more effort

made in recent years to include more diverse populations in research AND to consider their perspectives and needs. We have a long way to go as a

discipline in this regard.

Module 1A Summary

So to summarize, I’ve presented a model today in which complex psychological phenomenon must be understood at a variety of levels. I’ve also

provided several examples. In your opinion, what is the best level at which to understand psychological phenomena? What’s the right level of analysis?

Well, I would say that it depends on your goals.

Give this some thought before you show the answer.

Let’s say you want to develop a program to reduce bullying in elementary schools. You want to figure out what factors influence school bullying. Which is the best level? 

a. Biological

b. Psychological

c. Basic Social Interactions

d. Larger Social Group

e. Culture

Show Answer

Let’s say you want to develop a program to help students to develop better study habits. You need to understand how study strategies influence memory for course material. Which is the best level?

a. Biological

b. Psychological

c. Basic Social Interactions

d. Larger Social Group

e. Culture

Show Answer

Do You Remember?

See what you can remember from the previous material by matching the terms to their definitions.

Module 1B: Methods in Psychology

The science of psychology relies on research to uncover knowledge. This portion of the module will focus on the methods of research used in

psychology, but we will also return to this important topic throughout the course. Let’s focus on some basic strategies or designs.

1. Naturalistic Observation

One can systematically observe the real world. You can learn a whole lot by systematically observing the real world. Perhaps some of you know who

Jane Goodall is. She studied chimpanzees in Africa, and by observing their behavior in the real world, she learned a lot that has guided our

understanding of primates. Naturalistic observation has been used in psychology to help us form theories and develop hypotheses for future research.

As another example, let’s say I want to understand more about factors impacting children’s aggression. I could observe them on playgrounds and

carefully track all their aggressive behavior—hitting, kicking, spitting, name calling, all that. I could observe and deduce the situations that tend to lead

to this behavior, the characteristics of those individuals who were aggressive, and the consequences of their actions. I can learn a great deal from this

careful naturalistic observation.

Advantages Disadvantages Best Used

1A Matching Activity

Click the terms (colored purple and bold) and definitions to identify matches. Once you finish the activity you will see the full term list with correct definitions.

Two types of

aggression: proactive and reactive

If you look at violent crime rates you see startling differences

across countries.

When we try to use that knowledge to change (hopefully improve) human

behavior.

Some groups consider aggression to be a valid way to

solve problems.

Individuals develop their thought processes and

emotions within the context of close

relationships.

When we try to understand why

individuals behave the way they do.

Human aggression is generally higher in

males then females. The male sex

hormone testosterone is associated with

higher aggression.

Applied psychological

science

The larger group level

Basic psychological science The biological level

The family level The psychological level

The cultural level

Example of an outlier—Shaquille O’Neal's size 22 shoe Taken by David on Flickr: Some rights reserved.

Advantages Disadvantages Best Used

It’s real life Observer bias! The fact that someone is

watching can change behavior. For example, do

you think that children may behave differently

when they know they’re being observed? It’s

also hard to control the many variables that

are there as well

Naturalistic observation is often best used when

first learning about a particular phenomenon and

we need to develop hypotheses. For example, this

method led to much greater understanding of child

development and enabled us to conduct more

specific research.

2. Case Study

Here we chose one specific case and study it in depth. It could be a particular individual, or it could be a classroom or work setting or another “case”.

Perhaps I want to learn more about geniuses—maybe I could do a case study of the late, great astrophysicist Stephen Hawking. If I want to learn how

to best teach English as a second language, I could find a very successful classroom where children learn very effectively and quickly become fluent in

English. This would be a case study of a specific instance (of language learning) or situation. The case study has a long and storied history in

psychology, and we’ve learned much through this approach. For example, by looking at individuals with very specific, unusual brain injuries, we’ve

learned a lot about how the brain works.

Advantages Disadvantages Best Used

We can measure many things,

study in depth, collect lots of

data about our particular case.

This is a major advantage of the

case study to be able to look at

things in depth

Maybe it doesn’t

generalize! How

do we know if it

is typical of

others or unique

to that situation?

The best use of case studies is often for unusual and hard to

study phenomenon. There are case studies out there that have

looked at the impact of very severe child abuse on language

and emotional development. The impact of this abuse was, of

course, documented after the abuse was discovered and

stopped.

These cases have taught us important things about

development. These cases are horrible, thankfully uncommon

(although even one case is too common), and certainly not

something we can (or would want) to study in more controlled

ways.

On the other hand, what if we wanted to study the impact of

dyslexia (a reading disability) on students’ emotional

adjustment? Dyslexia is not rare and not difficult to study; a

case study would be a poor choice here. We would probably be

better off identifying a larger sample of kids with dyslexia and

follow them over time.

3. Survey

We can ask questions of large groups of participants. Examples include the US Census or the Monitoring the Future Survey, which has been used since

1975 to examine teens’ behaviors, beliefs, and attitudes.

Advantages Disadvantages Best Used

We can collect LOTS

of information and

examine

relationships within

that data. For

example, maybe we

want to look at how

religious affiliation is

related to particular

attitudes.

We always have to be careful about response bias! Not

everyone responds to a survey, and we want to make

sure that our sample is representative! How many of

you have gotten surveys in the mail and not

completed them? Maybe some people are more likely

to respond to surveys than others. So are our survey

results really representative?

When you are trying to evaluate the validity of survey

results you always want to look at the rate of

response. If only 20% of folks who were surveyed

responded, can you really trust it? Also, we have to

rely on folks' honesty in responding to questions, and

for some things (like questions about sexual activity or

drug use) this may not be something we can rely on.

So we have to be careful about how we rely on

surveys.

Surveys are best used in situations

where the research wants to gather

information on characteristics,

attitudes, opinion or experiences of

a large population of people. For

example, surveys are often used to

learn about people's religious

affiliations, political ideas, or health

behaviors.

Correlational vs. Experimental Research

In our field we need to make an important distinction between correlational and experimental studies.

What is a Correlational Study?

Here I collect data and look at associations between variables. This can be a terrific way to do research, but it has its limitations.

Let’s start with an example. I want to study the impact of exposure to

aggressive video games on kids' aggressive behavior. For the purposes of

discussion, let’s say I want to conduct my study among 3rd graders. Even

within a correlational study design, I can do this in a variety of ways.

One way I could do it is to use a survey of parents to determine how

much time kids spend on which video games; better yet, maybe I could

install an app on their computers and phones that will track their

videogame time and activity—that would be a good way, eh? (Of course,

we would need parental permission and child assent, because, after all,

we need to be ethical in our research.)

I could then also collect data on aggressive behavior, using observation

and/or getting parent and teacher reports. I then analyze my data and, lo

and behold, I find that they are related. More exposure to

violent/aggressive video games is associated with more aggressive

behavior.

Can I conclude that exposure to violent video games caused the 3rd graders to be more aggressive? If you said “no”, congratulations you are correct!

It could be that the causal direction goes the other way, maybe more aggressive kids seek out more aggressive video games. It could also be a totally

different thing that causes both—something we call the "third variable". Maybe parents who don’t monitor their kids as much as they should end up

with both more aggressive kids and kids who play more violent video games; maybe parents who have more permissive attitudes, or even positive

attitudes toward aggression, have kids who both play more violent video games and engage in more aggressive behavior. There are lots of potential

causes here. The important point is that we can’t determine it—a correlational study cannot tell me be about causality.

What is an experimental study?

Say I want to actually determine causation. Well, now I need an experimental study. Remember my topic is the relationship between exposure to

violent video games and aggressive behavior. To do an experimental study I will need to assign participants to particular conditions.

The key things here are:

experimental control. I “manipulate” the independent variable, which in this case is exposure to violent videogames.

random assignment to conditions. Half of my sample of kids get assigned to the “violent” videogame condition, half get assigned to the “non-

violent” videogames condition, and assignment is by chance (that is, random; you can use the “flip of a coin” to decide who gets assigned to each

condition).

Let’s say I am going to assign 100 3rd graders to one of two conditions

that I, as the experimenter, have preselected.

Fifty of the third graders will be assigned to play two hours a day of

a non-violent video game, maybe something boring like Tetris.

The other 50 of my third graders will be assigned a violent video

game, let’s say Grand Theft Auto—hardly a game appropriate for

third graders. Still, I’m the experimenter and I’m deciding what my

study will be.

Obviously parents have to consent to have their children participate in the

study. So the kids play these games for two hours a day for one month,

and then I evaluate their aggressive behavior.

Like before, I might evaluate it using teacher reports, parent reports, and

a behavioral observation. If I find at the end of my study that those who

play the violent video game, Grand Theft Auto, show more aggressive

behavior during the observations and their parents and teachers report

more aggressive behavior, I can say that my study shows that exposure

to these violent video games can cause youth to show more aggression.

In this kind of EXPERIMENTAL study, I can speak about causation.

This is not to say that I’ve explained all the reasons that youth can show aggressive behavior, and there may be some youth in my study who show

more aggressive behavior than others, but I can say that exposure to the violent video game caused them to show greater increases in aggressive

behavior then did exposure to a non-violent video game.

What's the difference between the two kinds of studies?

The big difference between correlational and experimental research is experimenter control. In the first instance I have just measured behavior and

looked at its association with other behavior. In the second instance I have control as an experimenter over the experimental conditions, and I

randomly assign participants to these conditions. By using random assignment, I can control for differences naturally occurring in my participants. That

is to say some of my kids may be high in aggression naturally, but by using random assignment I maximize the likelihood that some of those children

will be in each of my violence exposure groups.

It’s not whether I use sophisticated technology or techniques that makes

research experimental; rather, it is experimenter control.

What Do We Study?

In conducting research in psychology, we are often interested in the big ideas out there. These are called constructs—things like altruism, empathy,

self-interest, intelligence. These are big ideas and not always easy to measure. We need to pick measures that really “get at” what it is we want to

study. More specific than the constructs are the variables we use in a particular study; to operationalize (or include operational definitions) is to

define our variables for the purpose of a particular study.

Constructs and Variables

So, going back to something I discussed earlier. Say I want to understand more about youth aggression and how it comes about. Aggression is the

construct I’m interested in. So how do I measure this? What variables do I use? Do I use self-report? Do you think youth or their parents might not

disclose everything? Most of us want to look good and might alter our reports to look good—also known as the social desirability bias. We might do

the same for our kids. Do you think teachers always know who the aggressive kids are? On the one hand, teachers see a lot of kids, so they are pretty

good at knowing who is and isn’t extreme in some way. On the other hand, some of the most aggressive kids might be pretty good at hiding it from

teachers, possibly limiting it to when teachers are not looking.

None of these methods of measuring is foolproof. For my study, I’m going to include the following variables (with operational definitions in

parentheses): youth self-reports (Youth Self Report Form), parent report and teacher reports (Child Behavior Checklist), and peer impressions (Peer

Nominations).

One of the things I’m careful to do here is to use standardized measures that have been shown to work well in past research and to include reports

from a variety of sources (kids, parents, teachers)—none of which is perfect by itself. I could also design a task where youth can choose between

potential problem solutions and add up their aggression scores, with more aggressive solutions scored higher. As you can see, there are number of

ways to operationalize our aggression variables; some are better than others, and none is perfect.

Who Do We Study? Populations and Samples

We also need to carefully consider who we want to study—what is our population? In the study we previously discussed looking at youth aggression

and exposure to violent video games, the population of interest was all children in the 3rd grade. After all, maybe I’d like to be able to use my research

to develop programs to intervene with 3rd graders to reduce aggression.

Let’s think of another example. Let’s say, I want to develop methods for helping people with Type 1 diabetes more effectively manage their medical

condition. The population here would be all people with Type I diabetes. When I finish my research, I’d like to be able to generalize my findings to the

population of people with Type I diabetes.

Now I can’t study everyone in that population, so I need to select a sample of that population—a smaller number of people that I can study. I want my

sample to be a good representation of the population. The best sample is one that’s randomly selected.

Pretend it’s like a lottery: I have the name of every person in the USA with Type I diabetes and I can randomly select 100 of them for my study. That

would be amazing—too bad it’s totally unrealistic—there is not such list and how would I, practically, be able to study people from all 50 states? (I’d

need a big travel budget) So I need to find another way.

Let’s say then that in order to find people with Type I Diabetes I try to get referrals for all patients who were hospitalized for diabetes complications in

the last year at Joslin Diabetes Center here in Boston.

1. Is this a good strategy? a. Yes

b. No

Show Answer

Here's a better idea. What if I work for a health maintenance organization? I can go into the records of all the hundreds of thousands of members and

identify all those who are taking medications commonly prescribed for Type I Diabetes, and my organization can send them letters inviting them to

participate in the study. This is a pretty good strategy.

It’s not perfect; those with no health insurance won’t be represented; those who don’t want to be in a study won’t be represented. Still, it’s pretty good

and maybe the best I can get. All in all, we want to consider generalizability always in choosing how to obtain our sample. That is, will my findings

generalize to the larger population of folks I want to study? Sometimes we stratify our sample. To make sure my findings generalize to both men and

women, I include an equal number of both men and women in my sample. In this case I’m stratifying by gender.

How Do I Summarize My Data? Descriptive Statistics

Well let’s try another example. Perhaps I’m interested in starting a clothing and shoe boutique for BU undergraduates, and I want to know something

about their sizes and their preferences. Maybe I do a survey to obtain this information. I collect data on their gender, height, shoe size, number of

shoes, and wardrobes (number of ties, t-shirts, pairs of jeans), and preferences (how much they like the colors red and green. What now? What do I

do with my data?

First, I want to be able to just look at my data. I can graph my data—with potential values on the x-axis and the number of cases on the y-axis. If you

look at Figure 1, you can see that I have graphed shoe size for a small sample of BU undergraduates. This graph is the distribution of my data on

shoe size. If you examine it you will see that 2 people wore a size 6, 5 wore a size 7, and so on.

Figure 1

In Figure 2, I did the same thing for how much the students liked the color green,

Figure 2

and in Figure 3, I examine the distribution for student height.

Figure 3

Examining the distribution of your data is helpful in identifying odd cases; for example, the old basketball star

Shaquille O’Neal wore a size 22 shoe. He’s what we would call an “outlier”—he just doesn’t fit into our distribution

of scores.

The Normal Curve

An interesting phenomenon is the normal curve. When you get a large number of cases, the distribution starts to

take on the “bell” shape. There are more cases toward the middle and fewer in the extremes—typical of a large

number of phenomenon and very useful in psychology. As examples, height is a good example. There are a few

people who are very tall and a few people who are very short, but many more people fall in the middle. That’s the typical bell curve. Now think about

intelligence. There are a lot of folks who are around the middle, and a lot fewer folks who have extreme scores on intelligence, either very high or very

low.

Source: By Inglesenargentina (talk) (Uploads) - Own work, Public Domain, Link

Measures of Central Tendency

Secondly, I want a number to tell me what the “typical” subject looked like. These numbers (or statistics in this case) are known as “measures of

central tendency”, and there are three of them.

Measures of Central Tendency Mode is the most common score in the group.

Median divides the distribution in half with the remaining 50% of scores

above and 50% of scores below it.

Mean is the arithmetic average. You add up all the scores and divide by

the total number of scores. You can probably compute an average in your

sleep.

These are used in different circumstances. For categories (say for instance gender) we tend to use the mode. We might say “the modal (or typical)

introductory psychology student is female”. We might use the median for data that doesn’t really fit the bill curve very well. We often use the median to

describe the typical home price in a community. Let’s say $500,000 is the median; this is useful because there may be homes that are much, much

more expensive, but the $500,000 is much more typical.

So in this case the median gives us a pretty good idea of what is typical. Most often in psychology we use the mean. That’s because many phenomena

take on characteristics of the normal curve, and this allows us to use a number of statistical procedures that we’ve found useful.

Optionally, and for another look at measures of central tendency you can view the video below.

Watch on

Mean, Median, and Mode: Measures of Central Ten… Share

Measure of Central Tendency

More Descriptive Statistics

Commonly Used Measures of Variability Thirdly, I need a number that will tell me how much the different participants in my research differ from one another. Are they all similar on a particular

characteristic? Or are they very different? To find this out, I use a measure of variability.

Take a look at this next graph, as it gives you an example of what I mean by variability.

Examples of Variability

Let’s ignore the green line for the time being, and focus instead on the blue, yellow, and red lines, as each of these represent different distributions.

Interestingly for each of these, the mean is exactly the same. The blue distribution has little variability, and scores for most folks are fairly similar to

one another. The red line is a different distribution, and there’s a wider range of scores. The yellow distribution is very wide. So although the typical

score for each of these distributions is the same the amount of variability is quite different.

Here are three measures of variability that are often used: the range, variance, and the standard deviation. Let me tell you a little about each.

Range

The range is the lowest score subtracted from the highest score. So let’s say in my data that the largest shoe size is a 13 and the smallest shoe size is

a 6; so the range here would be 7 (13-6). The range tells me a little bit, but it can be influenced a lot by an extreme score. Add Shaquille O’Neal to the

class and suddenly the range goes up to 16 (22-6).

Variance

The variance is a little tricky to explain, and I’ve included an example for you to delve into. What the variance does is it takes each score and subtract

the mean from that score. Then each of those resulting scores is squared; the squared scores are all added up; and we divide by the number of scores.

So what you end up with is the average of the squared deviations from the mean. Now if that sounds confusing read it again. You will not be the only

one confused. This is a tricky concept. Although the variance can be handy for some statistical techniques, it’s a little hard to interpret. How do we

figure out what it means?

Standard Deviation

One of the ways that was developed to help interpret the variance is to convert it into the standard deviation. The standard deviation is the square root

of the variance. So to calculate the variance you squared all the deviations from the mean and took the average, so now you just take the square root

of that number. It’s a little easier to understand the idea of the standard deviation.

Now, just to drive you a little crazy I’m going to ask you to learn a little bit about the relationship between the standard deviation and the normal

curve. Look at the image below, as it shows you how the standard deviation is related to the normal curve.

Watch on

How to Calculate Standard Deviation Share

How to Calculate Standard Deviation

 

Attributes of the Normal Distribution

What it tells you is that if you go from the mean of the distribution to one standard deviation above the mean, you will have included 34.1% of all

scores. If you include one standard deviation above and below the mean you have included 68.2% of all scores. If you go out even further and include

two standard deviations on either side of the mean, that is two standard deviations above the mean and two standard deviations below the mean, you

will have included another 26.4% of the scores in your distribution (13.2% between one and two standard deviations above the mean, and 13.2%

between one and two standard deviations below the mean). So that means that you have included approximately 96% of all scores within two standard

deviations on either side of the mean. I would never ask you to do math on a test, but I do want you to remember these particular percentages. These

numbers remain the same across all distributions, and knowing them can help you compare scores. As an example, most intelligence (IQ) tests have a

mean of 100 and a standard deviation of 15. So here’s a test for you!

2. What is the score for one standard deviation above the mean on the IQ test?

a. 130

b. 115

c. 85

d. 110

e. 70

Show Answer

3. What is the score for one standard deviation below the mean on the IQ test?

a. 130

b. 115

c. 85

d. 110

e. 70

Show Answer

3. What percentage of people have a score between 85 and 115 on an IQ test?

a. 44%

b. 96%

c. 100%

d. 68%

e. 25%

Show Answer

So back to our data on the clothing habits and preferences of undergraduates! In the next two tables, I have calculated and show you the mode,

median, mean, range, variance, and standard deviation for some data I collected from my PS 101 students on height, shoe size, and how much they

liked the color green on a scale from 1 to 7.

Research Methods: Shoe Size

Central Tendency Variability

Mode = 5

Median = 4

Mean = 3.94

Range = 7 (6–13)

Variance = 3.13

Standard Deviation = 1.77

Research Methods: Color Green

Central Tendency Variability

Mode = 5

Median = 4

Mean = 3.94

Range = 6 (1 – 7)

Variance = 3.85

Standard Deviation = 1.96

Just another word about outliers. We always need to be careful to check our data. Imagine I decide to look at the data on how many shoes students

own. I could calculate the mean, median, variance, and standard deviation here. Now, I just had someone add the class, and her name is Imelda

Marcos. Do any of you know who she was? Interestingly, she was the wife of the Philippine president Ferdinand Marcos. They were excessively rich

(although the Philippine people were certainly not) and it was said that she owned over 3000 pairs of shoes.

I realize that she is dead, but for the sake of argument let’s say that she really wanted to take PS 101 at Boston University. Imagine that Imelda just

added my class, and I’ve included her data in my distribution. Oh no, what impact does that have? Well, the mean variance and standard deviation

would be affected in a pretty striking way, because Imelda “carries more than her weight” in shoes. Again, she’s what we would call an outlier, and the

mean and the standard deviation can be seriously impacted by her extreme score.

Do Variables Go Together? Understanding Correlation

Now that I’ve looked at my data, I have a sense of what the distributions of the variables are, what the typical participant looks like, and how much

variability there is in my sample. Next I want to look at how some of these variables might be related to one another. Look at this next graph.

Scatterplot: Association of Shoe Size and Height

In this slide I have graphed students' heights on the y-axis against their shoe size on the x-axis. Each of the dots in this graph represents one

participant. You can figure out his or her shoe size by going down from the dot to the shoe size on the x-axis and you can figure out his or her height

by going from the dot to that position on the y-axis.

You might think that shoe size and height would be associated, and you would be right. The dots seem to form a pattern, with many of them falling in

an area from the left bottom of the graph to the right top of the graph. That is to say, as height goes up so goes shoe size. But the next graph is a bit

different.

Scatterplot: Association of Shoe Color and Liking Green

Here I have graphed my subjects' shoe size on the y-axis and how much they like the color green on the x-axis. Perhaps not surprisingly, there really is

no pattern here. But who would expect shoe size to be related to how much one likes the color green?

Correlation and Scatterplots

When we talk about correlation we are talking about the degree of association between two variables—and the scatterplot is basically a visual

representation of correlation. By looking at the pattern we can get some idea of the degree to which the two variables are correlated.

The

 A few important points about the correlation coefficient 1. It ranges from +1.0 to -1.0

2. 0 means no association

3. A positive correlation means that the scores go up and down

together.

For example, the more hours you study the higher your grade;

the more sodas you drink the more weight you gain.

4. A negative correlation means that as one score goes up the other

goes down.

Some people make the mistake in thinking that a negative

correlation means things are not related, and this is not the

case. You can have a strong negative correlation and it means

that the two things are strongly related. For example, the more

hours you study the fewer mistakes you make on an exam and

on your homework.

Here’s another good example. Let’s say I want to design a test

that will indicate how good you are at golf. I think my test is a

pretty good one, and I think that a high score will predict how

good you are when you go out to play a round of golf. In case

you are not a golfer, a good golfer has a low score, not a high

score, when playing the golf course. If you have a high score on

my golf proficiency test (suggesting you are a good player) then

you should have a low score on the course, and that’s a

negative correlation.

Remember, if my test is a good one I will be able to predict your

score.

5. Perfect correlation occurs rarely, if ever, in nature. No two things are

perfectly related. If I know something about your height, I have a

general sense of your shoe size, but I wouldn’t know it for sure.

There is still variability.

Watch this for a further explanation of correlation and causation.

Watch on

Correlation CAN Imply Causation! | Statistics Misc… Share

Correlation CAN Imply Causation! | Statistics Misconceptions

How Do I Compare Groups? T-Tests

Let’s talk about another statistical strategy. What if I want to compare two groups to see if they are different on a particular variable? Students T-test is

a strategy that I can use when one of my variables is nominal or categorical and one of them is continuous (that is it can have a range of values).

In this case I can compare men and women (gender is a categorical variable) on both height and shoe size (both are continuous, as they can take a

range of values). In this case my hypothesis is that men will have bigger shoes and will be taller than women.

Here I’m comparing the distribution of scores for men to the distribution of scores for women. Of course there is variability for both men and women—

some have larger feet and are taller, some have smaller feet and are shorter.

The Students T-test compares the variability within each of the distributions, the one for men and the one for women, to the variability between men

and women. I don’t want you to worry about how to do this test, I just want you to understand what it does. The Students T-test allows me to

determine whether there is greater variability between genders then within genders. Look at the results below.

I’ve listed the mean and the standard deviation for shoe size and height for both men and women and I calculated a t-test for both height and shoe

size. What I want you to focus on is something called the P value. The P value in the case of height it says P less than .001, and in the case of shoe

size it says P less than .005. Let’s focus on height for minute. What the P value means is that if I conducted this study a thousand times, I would only

find a difference this big by chance—one time by chance. So it’s very, very, unlikely that such a big difference in height would simply be found by

chance.

Let’s focus on shoe size for minute. What the P value means here is that if I conducted this study a thousand times, I would only find a difference in

shoe size and this big by chance five times. Again, this is very, very unlikely to be found just by chance. On the other hand, it’s very likely that men

and women actually do differ in height and shoe size—and that it is a real and replicable finding.

This is what we mean by statistical significance. Statistical significance means that it is very unlikely that such a finding is a result of chance. Now to

say that a finding is statistically significant is not to say that it is important or interesting, only that it is very unlikely to be a chance finding. Honestly,

the idea that men and women differ in their shoe size is really neither interesting nor important (unless you are in the shoe manufacturing business),

but it’s very unlikely to be due to chance. So I want you to understand the idea of statistical significance as it will be very important going forward in

your psychology studies.

Do You Remember?

See if you can identify the methods of research used in psychology and the differences between correlational and experimental studies by completing

the exercises below.

How Do I Ask More Complicated Questions? Advanced Statistics

All in all, there are many statistical strategies for examining data and asking sophisticated and complicated questions. The ideas of correlation and the

Students T-test are just the tip of the iceberg.

Structural equation modeling

In fact, there are many new strategies that allow us to examine complex data. Two of them I’ll just mention briefly. First, structural equation modeling

uses the idea of correlation to simultaneously look at many relationships at the same time. Often times we want to examine complex questions that

involve many variables at once. Structural equation modeling can help us to do that.

For example, we may want to look at how adjustment to college is related to the distance from home, relationships with family members, social

supports available in the college setting, and the course of study. We could use structural equation modeling to look at this.

Longitudinal modeling

Second, longitudinal modeling allows us to examine change over time. One of the main questions we often ask is how people change over time as a

function of experience. As one example, we may want to look at how children learn to read, and what factors might predict success in reading. As

another example, we might want to look at how individuals respond to certain kinds of treatments for anxiety or other mental health challenges. By

using longitudinal modeling, we can address these questions.

The best statistical approach

You might ask what’s the best statistical approach? Well, it always depends on what the question is you want to ask! This is where in psychology, as in

so many other sciences, it’s essential to have hypotheses.

The Big Picture: Research Process in Psychology

The image below illustrates the process of doing research in the field of psychology.

In the first step, you have a topic that you’re interested in and you review what is known about that topic. You learn what others have found in their

research and figure out where the gaps are. In the second step, you develop hypotheses, or predictions, that guide your research. In the third step,

you select a research method and your participants. Then you collect your data. In the fourth step, you analyze your data and you either accept or

reject your hypotheses. Were your predictions good? Or did they not work out as you planned? You might do some follow-up analyses to figure out

more about why. In the fifth step, you submit your findings for what we call peer review. You write up your paper, select a journal that you’d like to

publish it in, and submit to the editorial board. The editor sends it out for review to experts in your field who know a lot about what it is you’re

studying. This is a great way to get feedback on your work. If you’ve done your study well and your findings are interesting, they get published for

many others to read. Those findings become part of the larger research literature. Then you start the process again. This is how the science works. As

researchers we all contribute to the larger body of knowledge.

I hope this lecture gives you a sense of some of the basic methodology in the field of psychology and provides a good springboard as we move forward

to the many topics that we will be discussing throughout this course.

Review and Reflect

Module 1 Review and Reflect

Three things you didn’t know before:

Compose your thoughts here

Two things you want to continue to learn more about:

Compose your thoughts here

Your PS101 Progress

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1B Matching Activity

Click the terms (colored purple and bold) and definitions to identify matches Once you finish the activity you will see the full term list with correct definitions.

Color

Value that can seriously impact the mean and standard

deviation

Weight

Study design in which I want to

actually determine causation.

The lowest score subtracted from the

highest score Ranges from +1.0 to

-1.0

Advantages: It’s real life. Disadvantages: Observer bias! The fact that someone is watching can change

behavior. It’s also hard to control the many variables that

Advantages: We can measure many things, study in

depth, collect lots of data about our

particular case. This is a major advantage of the case study to

Range Continuous variable

Outlier Case study

Experimental Naturalistic observation

Nominal variable Correlation coefficient

correlation coefficient is a statistical indicator of the degree of association.