Chapter Two: Research Methods – Notes Summary



Chapter Two: Research Methods – Notes Summary

Lesson One

Why is replication of research important in psychology?

Describe –

Predict –

Explain -

Control –

In Class Question: Using the below example, explain how a psychologist might address the person’s issues using the describe, predict, explain, and control process.

“John is a fifth grader that gets into fights regularly at school. At home his father punishes him and his mother by using violence. School psychologists are looking for ways to help John.”

Describe –

Predict –

Explain -

Control –

Hypothesis –

Experiment -

|Independent Variable |Dependent Variable |

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In Class Question: Create a “word problem” and select both the Independent variable and dependent variable in your example.

|Example: |

• IV:

• DV:

What is a good way to remember the difference between IV and DV?

Homework Assignment: Find the IV and DV in each of the below examples. If you are having trouble, the answers and explanations can be found below.

Independent / Dependent Variable Examples: Class Activity

Example 1

A researcher is studying the effect of sleep on aggression, thinking that less sleep will lead to more aggression. She has some people sleep 6 hours per night, some people sleep 3 hours per night and some people sleep as much as they want. She then monitors aggressive behavior during basketball games among participants.

IV - DV -

Example 2

A researcher is curious to find out what effect classical music has on people’s level of relaxation (as measured by heart rate). He suspects that listening to classical music will make people feel more calm and relaxed. He lets one group listen to classical music for one hour. He lets another group sit in a quiet room for one hour (i.e they hear no music). After one hour, he monitors the heart rate of each participant to measure their level of relaxation.

IV - DV -

Example 3

A researcher conducts an experiment to assess the effects of alcohol on people's sense of balance. He divides his subjects into three groups: in one group the participants drink one ounce of alcohol, in another they drink two ounces of alcohol and in a third group the participants drink soda. He then watches as each participant tries to walk on a straight line from one corner of the room to the next and notes how many times they stumble outside the line.

IV - DV -

Example 1 ANSWER

Aggression (or aggressive behavior) is the dependent variable. The researcher is studying the effect of sleep on aggression. In other words, the level of aggression depends on the amount of sleep. That is why it is called the dependent variable. It also says that the experimenter is monitoring (i.e., measuring) aggressive behavior. The dependent variable is always what is being measured. So aggressive behavior (or aggression) must be the dependent variable. The independent variable is the amount of sleep. Remember that the independent variable is the one that the researcher manipulates, which she clearly does in this case, letting some people sleep only 3 hours, and others only 6 hours.

Example 2 ANSWER

The dependent variable is the level of relaxation. How do we know? Well, the experimenter is looking at the effect of classical music on the level of relaxation. The state of relaxation depends on classical music. Again, look at what is being measured: the researcher is measuring the level of relaxation, as measured by heart rate. You would also be correct to say that the dependent variable is the heart rate, since this is the operational definition of the level of relaxation; this is how it is being measured. The independent variable is the classical music. The experimental condition is where they get to listen to classical music (they are exposed to the treatment - the classical music). The control condition, well, it is business as usual, they are just sitting in their quiet room.

Example 3 ANSWER

Dependent variable: sense of balance (as measured by number of "stumbles")

Independent variable: alcohol

Experimental condition: 2 experimental groups: drinking one and two ounces of alcohol

Control condition: drinking soda (business as usual, no active manipulation of the independent variable, i.e., no alcohol).

|Population: |Sample Population: |

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Why is random selection important in research?

Why is it important to have both an experimental and a control group?

How can confounding variables decrease the effectiveness of your research?

Homework Question: Create a basic experiment utilizing the following terms: Independent Variable, Dependent Variable, Experimental Group, Control Group, Confounding Variables, Sample Population, Random Selection.

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Lesson Two:

Types of Experimentation:

|Types of Experimentation |Description / Examples |Positives |Negatives |

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|Naturalistic Observation | | | |

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|Surveys/Tests | | | |

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|Controlled Experiment | | | |

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|Case Study | | | |

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In Class Question: After viewing the Genie Case Study, what strengths and weaknesses do you see with using the case study approach to experimentation?

What is the difference between:

• Reliability –

• Validity -

Why are none of the above types of experimentation the same as a controlled experiment?

Lesson Three:

|Way to Eliminate Confounding Variables: |BIG IDEA |

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|Single-Blind Procedure | |

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|Double-Blind Procedure | |

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|Placebo | |

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|Counterbalancing | |

In Class Question: After viewing the video on the placebo effect, do you think you are as easily persuaded as the people in the video? Why or why not?

Homework Question: After viewing the Stanford Prison Experiment, how could Dr. Zimbardo complete the experiment with less bias and more control? Using what you’ve learned about good research design, revamp the prison experiment eliminating as much potential bias as possible.

Lesson Four:

Studies that lead to APA Ethical Guideline Practices:

|Research Study |Why it Would be Considered Unethical Today |

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|Harlow’s Attachment Study | |

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|Stanford Prison Experiment | |

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|Milgram’s Obedience Research | |

APA’s Rules for Animal Research:

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APA’s Rules for Human Research:

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Homework Assignment: Please read the following article and answer the questions that follow.

Monkey love

Harry Harlow's classic primate experiments suggest that to understand the human heart you must be willing to break it

By Lauren Slater  |  March 21, 2004

OBEDIENCE. CONFORMITY. Cognitive. Cuing. These were the words used by psychologists in the 1950s, and Harry Harlow didn't like them. He wanted to talk about love. He was at a conference one day, and every time he used the word "love" another scientist would interrupt and say, "You must mean proximity, don't you?" until at last Harlow, a brash man who could also be strangely shy, said, "It may be that proximity is all you know of love -- I thank God I have not been so deprived."

That was just like him, to make such a statement, in public no less; he was prickly, impolite, a man who is remembered by some with real distaste and by others with fondness. But in 1959, Harlow was speaking science in a way no one had dared to before, injecting statistics with hemoglobin and heart, the Nabokov of psychology.

His experiments were long meditations on love, and all the ways we ruin it. In his research with wire monkeys, he was able to show that infant monkeys cared more for a soft surrogate mother that could not feed them than for a metal, milk-bearing one. Harlow's experiments, many captured on film, are chilling and underscore the power of proximity in our lives. From his findings a whole science of touch was born.

Little is known of Harlow's own childhood. He was born Harry Israel in 1905, to Lon and Mabel Israel of Fairfield County, Iowa. His father was a failed inventor. His mother, Harlow recollected in a partly finished autobiography, was not a warm woman.

Harlow experienced bouts of depression throughout his life; maybe here is where they began, in the long Midwestern winters. At school, he did not fit in. By age 10, he had begun to draw during every free minute he had, making a strange and beautiful land called Yazoo, populated with winged animals and horned beasts. When he was done with a picture, he would bisect the beasts with sharp black lines, so they lay on the page, all bloody color but still somehow beautiful, vivid and vivisected.

Harlow did his undergraduate and graduate work at Stanford, where he studied with Lewis Terman, the famous IQ researcher who was just then probing into gifted children. At the suggestion of Terman, he changed his Jewish-sounding name to Harlow. After he married one of Terman's former gifted children, Clara Mears, with her IQ of 155, Terman wrote a letter of congratulations that makes the match sound more like animal husbandry than human bonding: "I am happy to see the joining of Clara's extraordinary hereditary material with Harry's productivity as a psychologist."

When Harlow took a job at the University of Wisconsin-Madison in 1930, he planned to study rats, but he wound up with rhesus monkeys, a small agile breed. Ever Terman's student, he began by devising a test of monkey intelligence, a sort of simian IQ profile. But soon he began to wonder about something else.

Harlow would begin his experiments by separating the infant monkeys from their mothers and peers, and he noticed that the infants, when separated, became extremely attached to the terry cloth towels covering the cage floors. They would lie on them, grip them in their tiny fists, throw tantrums if they were taken away, just like a human infant with a ratty blanket or a stuffed bear. The monkeys loved these towels. Why?

This was a huge question. Attachment had previously been understood in terms of nutritive rewards: We love our mothers because we love their milk. Researchers Clark Hull and Kenneth Spence had said human attachment was predicated on drive reduction: Hunger is a primary drive and we want to reduce it; the same goes for thirst and sex. From the 1930s through the `50s, the theory of drive reduction and its link to love went unquestioned.

Harlow, however, began to question it. He fed the baby monkeys by hand, and when he took the little plastic bottles away, the infants just smacked their lips and maybe wiped a white dribble off their hairy chins. But when Harlow tried to take their towels away, the simians screamed like a slaughterhouse, throwing their small bodies down and clutching at bunches of cloth. This fascinated Harlow. As his biographer Deborah Blum wrote, the best way to understand the heart was to break it. And so started Harlow's brutal and beautiful career.

Rhesus macaque monkeys share roughly 94 percent of their genetic heritage with humans. But Harlow felt no kinship with his test subjects. "The only thing I care about is whether a monkey will turn out a property I can publish," he said. "I don't have any love for them. I never have. I don't really like animals. I despise cats. I hate dogs. How could you love monkeys?"

Harlow's experiment required wire cutters, cardboard cones, hot coils, steel nails, and soft cloth. He used the wire cutters to fashion a wire mother, its torso patterned with small squares, a single inflexible breast "on the ventral front." Affixed to this breast, a steel nipple pierced with a tiny hole through which the monkey milk could flow.

Then Harlow fashioned a soft surrogate, a cardboard cone bunted in a terry cloth towel. He wrote, "The result was a mother, soft, warm, and tender, a mother with infinite patience, a mother available 24 hours a day . . .. It is our opinion that we engineered a very superior monkey mother, although this position is not held universally by monkey fathers."

First Harlow took a group of newborn rhesus macaque babies and put them in a cage with the two surrogate mothers: the wire mother full of food, the cloth mother with an empty breast and a sweet smile. After the initial trauma, something amazing started to happen. Within days, the baby macaques transferred their affections from the real mother, who was no longer available, to the cloth surrogate.

The cloth mother, however, had no milk, so when the youngsters were hungry, they would dart over to the chicken-wire mother and then run back to the safety of the soft towel. Harlow graphed the mean amount of time the monkeys spent nursing versus cuddling. The disparity in favor of cuddling, he wrote, was "so great as to suggest that the primary function of nursing . . . is that of insuring frequent and intimate body contact of the infant with the mother."

Harlow was establishing that love grows from touch, not taste, which is why, when the mother's milk dries up, the child continues to love her. The child then takes this love, the memory of it, and recasts it outward, so that every interaction is a replay and a revision of this early touch. "Certainly," writes Harlow, "man cannot live by milk alone."

This was a significant discovery. The `30s to the `50s had been a cold era in childrearing. The famous pediatrician Dr. Benjamin Spock advised feeding by schedule. Nestl and Ross laboratories discovered formula, white powder, plastic nipples, tepid water from the faucet. John Watson famously wrote, in his books about how to rear children, "Do not overindulge them. Do not kiss them goodnight. Rather, give a brief bow and shake their hand before turning off the light."

Well, Harlow said you should never shake a baby's hand. You should not hesitate to hold him. What's more, he said, any old palm will do: "Love for the real mother and love for the surrogate mother appear to be very similar. . .." Harlow and company had identified "contact comfort" as an essential component of love. But what else was there?

Surely, Harlow hypothesized, the face is another variable of love. The original surrogates had primitive faces with black bicycle reflectors for eyes. Now Harlow ordered his lab assistant to make a really good monkey mask, to see what sort of attachment it would produce. However, the experimental monkey was born before the face was finished, so Harlow dropped the newborn in the cage with its terry cloth mother, who had only a blank, featureless flatland for a face. The little monkey loved the faceless mother, kissing it, nibbling it. When the ornamental monkey mask was finally attached, the baby screamed in horror, rushed to a corner of its cage, rocked violently.

Many have called Harlow's experiments cruel. But there is also something powerful and affirmative about what he gave us: the sure knowledge that our needs are more complex than simple hunger, that we seek to connect at all costs, and will always find the first face the loveliest face.

Harlow was studying love even as he himself had already fallen out of love. Clara had left with her two children, but Harlow had another woman, the Iron Maiden. The Iron Maiden was a special surrogate monkey mother Harlow had designed; she shot out sharp spikes and blasted her babies with air so cold and forceful the infant monkeys were thrown back against the bars of their cages, clinging and screaming. This, claimed Harlow, was an evil mother, and he wanted to see what would happen.

Here is where Harlow began to earn his darker reputation. Here is where he stepped from science into fairy tales. He made many of the iron maidens: Some rattled their children and stabbed them. No matter what the torture, Harlow observed, the babies would not let go. There is no partial reinforcement to explain this behavior; there is only the dark side of touch, the reality of primate relationships, which is that mothers can kill us even as they hold us.

In 1958, as the newly elected president of the American Psychological Association, Harlow traveled to the group's annual meeting in Washington, D.C., to deliver a speech called "The Nature of Love." He interspersed his speech with powerful black-and-white film clips of the sci-fi-looking surrogates and the babies who depended on them. At the end, he spoke of the "practical applications" of his research.

American women, he said, were threatening to displace men in the workplace and the university. However, he declared, there was some good news. "It is cheering in view of this trend to realize that the American male is physically endowed with all the really essential equipment to compete with the American female on equal terms in one essential activity: the rearing of infants . . .."

Soon after that speech, the University of Wisconsin at Madison issued a press release announcing "Motherhood Obsolete." Harlow put out new research effectively showing that a cloth surrogate mother was more important than a nursing mother and could stand in just as well as the real mother. Harlow appeared on "To Tell the Truth," and CBS made a documentary of his work.

But something was not going well. When he took the grown-up cloth-mothered monkeys out to play and mate, they were violently antisocial. Some began to display autistic-like behavior. A New York Times reporter came out to Madison to do a follow-up and Harlow led him to his lab, where a troop of rocking, head-banging macaques sat in cages, chewing off their fingers. "I admit it," said Harlow. "I have made a mistake."

And so he set out to correct it. Mothering, he hypothesized, must have other variables, such as motion. So he made a surrogate that could rock.

According to Leonard Rosenblum, one of Harlow's students at the time and now a renowned monkey researcher in his own right, this produced babies that were almost normal, as long as they got an hour and a half of daily play with a live monkey as well. Rosenblum says, "What this means is that there are three variables to love -- touch, motion, and play -- and if you can supply all of those, you are meeting a primate's needs."

Rosenblum goes on to repeat that with a half-hour a day of play "the kids" would be perfectly fine. "It's amazing," he says, "it's amazing how little our nervous system needs in order to turn out normal."

In some respects this is encouraging. A little jiggle, a soft sweater, and only 30 minutes of actual primate interaction. Any mother can do this: lazy, working, wired, iron. But if Harlow's findings are seemingly so reassuring, so all about love, why do they lodge in the gut like one of his experimental spikes?

We shiver through Harlow's results, but still we make use of them. His published, powerful research made its way into baby-care products -- most notably the sling and the Snugli, which have added warmth to the ways we parent infants. William Sears, the famous attachment-parenting advocate, a pediatrician who preaches sleeping with your babies, keeping them close at all times, is a Harlow-made man, whether he knows it or not.

Orphanages, social service agencies, the birthing industry all had critical policies altered based in part on Harlow's findings. Thanks in part to Harlow, doctors now know to place a newborn directly on its mother's belly after birth. Also thanks in part to Harlow, workers in orphanages know it's not enough to prop a bottle; the foundling must be held and rocked, see and smile. Thanks to Harlow and his colleagues in the study of attachment, we have been humanized -- we possess an entire science of touch, and some of this came from cruelty. There's the paradox.

. . .

Thanks to Harlow, we also have the animal rights movement, which was inspired in part by his research. Until a few years ago, the Animal Liberation Front had a demonstration at the University of Wisconsin's National Primate Research Center, where they mourned in the presence of thousands of stuffed Kmart monkeys.

Some say Harlow's words caused the trouble. "The problem with Harlow," says Rosenblum, "is the way he described things. He did it to get a rise out of people . . .. He would never say `terminated.' He would say `killed.' Why couldn't he have called the `rape rack"' -- Harlow's term for the contraption he devised to force his disturbed female monkeys to mate -- "a restraining device? If he had, he wouldn't have such a mixed reputation today."

But it's not just the words that are disturbing. I am disturbed by a cuff strangling a monkey's neck. I am disturbed by the Iron Maiden, the rape rack, despite the knowledge they gave us -- and Harlow, perhaps he was disturbed as well. For all his pronouncements about how he didn't care for his monkeys and didn't like animals, some of his students suggest that the nature of his work began to really bother him.

In 1971 Harlow's second wife, Peggy, died of breast cancer. Around that time, he went off to the Mayo Clinic in Minnesota, where he submitted to a series of electroshock treatments, himself now the animal strapped down on the table.

Back in Madison, people said he was never the same again. He no longer wanted to study maternal deprivation. The 1960s saw the rise of biological psychiatry and the hope that medications might alleviate mental conditions. That interested Harlow. Once again, he turned to his rhesus macaques.

He built a black isolation chamber in which an animal was hung upside down for up to two years, unable to move or see the world, fed through a grid at the bottom of the V-shaped device. This Harlow called "the well of despair." Indeed, it was successful in creating a primate model of mental illness. The animals, once removed, after months or years, were shattered and psychotic. Nothing Harlow did could bring them back. There appeared to be no cure. No way to contact, to comfort.

In the end, Harlow died of Parkinson's disease. He could not stop shaking.

Monkey Love – Answer Sheet

Please answer each question completely.

1. After reading the article, describe what you think this quote means:

“To understand the human heart you must be willing to break it.”

2. What was the main finding of Harlow’s research with the monkeys?

3. What was Harlow’s early life like?

4. In Harlow’s early research, what did the infant monkeys become attached to when they were separated from their mother’s?

5. Explain the concept of Drive Reduction:

6. Harlow had some interesting thoughts about animals: Describe some of these thoughts:

7. Describe the difference between the wire and cloth monkeys:

8. How did the infant monkeys interact with the wire and cloth monkeys?

9. Why do you think that critics have called Harlow’s research cruel?

10. Describe the Iron Maiden monkeys: What were they like?

11. At one point in his research, Harlow said, “I have made a mistake.” What was he talking about?

12. Why does Harlow have a mixed reputation today?

13. What was Harlow’s last experiment and what was he trying to do?

In Class Question: Why do you think it is important for the APA to have rules regarding animal and human research?

What is your view on animal research?

What is the job of the Institutional Review Board?

Lesson Five:

What is the difference between:

• Quantitative Research –

• Qualitative Research –

In Class Question: Use the below information to learn more about the types of scales used in research.

|Type of Scale: |Description: |

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|Nominal | |

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|Ordinal | |

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|Interval | |

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|Ratio: | |

There are four measurement scales (or types of data): nominal, ordinal, interval and ratio.  These are simply ways to categorize different types of variables.  This topic is usually discussed in the context of academic teaching and less often in the “real world.”  If you are brushing up on this concept for a statistics test, thank a psychologist researcher named Stanley Stevens for coming up with these terms.  These four measurement scales (nominal, ordinal, interval, and ratio) are best understood with example, as you’ll see below.

Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio

November 28, 2012 - Data Analysis - Tagged: data analysis, statistics - 16 comments

There are four measurement scales (or types of data): nominal, ordinal, interval and ratio.  These are simply ways to categorize different types of variables.  This topic is usually discussed in the context of academic teaching and less often in the “real world.”  If you are brushing up on this concept for a statistics test, thank a psychologist researcher named Stanley Stevens for coming up with these terms.  These four measurement scales (nominal, ordinal, interval, and ratio) are best understood with example, as you’ll see below.

Nominal

Let’s start with the easiest one to understand.  Nominal scales are used for labeling variables, without anyquantitative value.  “Nominal” scales could simply be called “labels.”  Here are some examples, below.  Notice that all of these scales are mutually exclusive (no overlap) and none of them have any numerical significance.  A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels.

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Ordinal

With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known.  Take a look at the example below.  In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is.  For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?”  We can’t say.

Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.

“Ordinal” is easy to remember because is sounds like “order” and that’s the key to remember with “ordinal scales”–it is the order that matters, but that’s all you really get from these.

Advanced note: The best way to determine central tendency on a set of ordinal data is to use the mode or median; the mean cannot be defined from an ordinal set.

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Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio

November 28, 2012 - Data Analysis - Tagged: data analysis, statistics - 16 comments

There are four measurement scales (or types of data): nominal, ordinal, interval and ratio.  These are simply ways to categorize different types of variables.  This topic is usually discussed in the context of academic teaching and less often in the “real world.”  If you are brushing up on this concept for a statistics test, thank a psychologist researcher named Stanley Stevens for coming up with these terms.  These four measurement scales (nominal, ordinal, interval, and ratio) are best understood with example, as you’ll see below.

[pic]

Nominal

Let’s start with the easiest one to understand.  Nominal scales are used for labeling variables, without anyquantitative value.  “Nominal” scales could simply be called “labels.”  Here are some examples, below.  Notice that all of these scales are mutually exclusive (no overlap) and none of them have any numerical significance.  A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels.

[pic]

Examples of Nominal Scales

Note: a sub-type of nominal scale with only two categories (e.g. male/female) is called “dichotomous.”  If you are a student, you can use that to impress your teacher.

Continue reading about types of data and measurement scales: nominal, ordinal, interval, and ratio…

Ordinal

With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known.  Take a look at the example below.  In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is.  For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?”  We can’t say.

Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.

“Ordinal” is easy to remember because is sounds like “order” and that’s the key to remember with “ordinal scales”–it is the order that matters, but that’s all you really get from these.

Advanced note: The best way to determine central tendency on a set of ordinal data is to use the mode or median; the mean cannot be defined from an ordinal set.

[pic]

Example of Ordinal Scales

 Interval

Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values.  The classic example of an interval scale is Celsius temperature because the difference between each value is the same.  For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees.  Time is another good example of an interval scale in which the increments are known, consistent, and measurable.

Interval scales are nice because the realm of statistical analysis on these data sets opens up.  For example,central tendency can be measured by mode, median, or mean; standard deviation can also be calculated.

Like the others, you can remember the key points of an “interval scale” pretty easily.  “Interval” itself means “space in between,” which is the important thing to remember–interval scales not only tell us about order, but also about the value between each item.

Here’s the problem with interval scales: they don’t have a “true zero.”  For example, there is no such thing as “no temperature.”  Without a true zero, it is impossible to compute ratios.  With interval data, we can add and subtract, but cannot multiply or divide.  Confused?  Ok, consider this: 10 degrees + 10 degrees = 20 degrees.  No problem there.  20 degrees is not twice as hot as 10 degrees, however, because there is no such thing as “no temperature” when it comes to the Celsius scale.  I hope that makes sense.  Bottom line, interval scales are great, but we cannot calculate ratios, which brings us to our last measurement scale…

Ratio

Ratio scales are the ultimate nirvana when it comes to measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied.  At the risk of repeating myself, everything above about interval data applies to ratio scales + ratio scales have a clear definition of zero.  Good examples of ratio variables include height and weight.

Ratio scales provide a wealth of possibilities when it comes to statistical analysis.  These variables can be meaningfully added, subtracted, multiplied, divided (ratios).  Central tendency can be measured by mode, median, or mean; measures of dispersion, such as standard deviation and coefficient of variation can also be calculated from ratio scales.

Ways to View Descriptive Statistics:

|Way to View Descriptive |What it Looks Like: |

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|Frequency Distribution | |

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|Histogram | |

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|Frequency Polygon | |

Measures of Central Tendency and Variability:

|Measure of Central Tendency: |Description: |

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|Mode | |

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|Median | |

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|Mean | |

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|Range | |

In Class Question: Standard Deviation

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If the above chart was a representation of a test that was taken in class, what assumptions could you make?

Why is the phrase “correlation does not imply causation” important to follow when doing statistical research?

Which of the following shows a stronger rate of correlation: (-.34, -.44, .44, .97, -.97) – Why?

Homework Assignment: Chapter Wrap-Up

AP Psychology: Research Proposal

• You will be constructing an abbreviated version of a psychological research project (called a research proposal). The project can focus on any topic as long as it is psychologically related and of course school appropriate. Please follow the directions for each section.

Title of Research Project: _________________________________________________________

Section One: Abstract (you may want to come back to this one after you complete all other sections, as it is a basic summary of your research study. Please include:

1. An introduction of why you think your particular topic is significant

2. The type of research you’ll be conducting (experimental or naturalistic observation)

3. What your sample population will be and how you’ll assign them.

4. Your hypothesis – What do you think the outcome will be?

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Section Two: Methods – In this section you will need to include:

1. A description of your proposed sample population and how you assigned them to groups (experimental and control). (Are your participants going to be paid?)

2. Procedures and Time Frame: A very detailed overview of how the study will be conducted: (example: participants will enter the classroom and be asked to stand on one foot for 3 ½ minutes while clucking like a chicken.)

3. A detailed account of ALL supplies used in your research study. (think of everything!)

4. List both the Independent and Dependent variables of your research.

5. Explain whether or not your participants will know what you are studying in your experiment.

6. List how you will debrief your participants after the study. (tell them what you were studying)

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Section Three: Implications and Limitations:

1. Please list any foreseen problems you may encounter during your research experiment.

2. Please list any limitations your study may have. (Confounding variables, research bias, if paying the participants encourages response rate or overall mood, etc.)

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Section Four: Discussion

1. What do you already know about the topic that leads you to believe your hypothesis will be true?

2. In what other ways could this research be conducted effectively?

3. Why is this research important? – Will it encourage others to research the topic further? In what way?

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