Research Methods & Statistics

qualitative variable

(Quality) A variable that cannot be reflected with numbers and is therefore described or shown as a category or story. PROS: -more detailed and human (most learn better through storytelling-more personal and relatable) CONS: -can't be converted into numbers

Validity

(accuracy) The ability of a test to measure what it was designed to measure. (measures what you said you'd measure) EX: The results of the experiment showed validity because the sunlight directly made the plant grow faster. There was no confounding variables to affect the plant grow faster.

Independent Variable

(cause/manipulation) a condition or event that an experimenter varies in order to see its impact on another variable. EX: The researcher was interested in finding how sunlight was correlated to the growth of plants. The independent variable is the exposure to sunlight because its what can be manipulated.

Reliability

(consistency) The consistency of data resulting from psychological testing or experimental research EX: The results of the experiment showed that of 700 people, 650 felt happier after exercising. Therefore, the correlation between exercising and elevated mood is reliable because the results are consistent since most people experienced happiness.

unanalyzed correlation

(covariance) predictable relationship exists between two variables but you don't know which caused which

spurrious correlation

(covary because of a confound) A situation in which variables are associated through their common relationship with other variables, but do not have a relationship with one another. EX: As ice cream sales increase, so do drowning rates. However, they don't directly affect each other. They are simply associated because of the weather. As it gets hotter, people buy more ice cream and go more to the pool so drowning rates increase.

Dependent Variable

(effect) the variable that is thought to be affected by the manipulation of the independent variable. EX: The researcher was interested in finding how sunlight was correlated to the growth of plants. The dependent variable is the growth because its "dependent" or "affected" by the sunlight.

Standardization

(fairness) A set of uniform procedures used for treating each participant in a test, interview, or experiment. -need to be fair to compare (needed to be treated and tested the same to correctly prove cause and effect) EX: The experimenter performed standardization when he studied the effect on sunlight on plant growth by making sure that the only variable being changed was the independent variable or the exposure to sunlight. Therefore, he kept all other variables the same like the amount of water, soil, and fertilizer the plant has.

direct correlation

(ideal) -the given variable X is the direct cause of of another variable Y -it is known which happens 1st and no other confound is responsible

quantitave variable

(number) a variable that is measured numerically PROS: -easy for statistical analysis needed to create predictive models and to prove the statistical difference between two groups -better for cause and effect

Standard Deviation

-(SD) An index of the amount of variability in the set of data. (measure of spread-how different numbers are from the mean and each other) small SD: people are really close to each other & mean large SD: people are really different from each other and have a spread out mean. EX: measuring height SD is 1 cms: -all participants vary with only 1cm of height -all close to each other (similar heights) -small SD SD is 10 cms: -participants vary greatly -have tall and short people -large SD

Double-Blind Procedure

-A research strategy in which neither subjects nor experimenters know which subjects are in the experimental or control groups. EX: The subjects were divided into groups where one was given a medication and the other was given a placebo. The subjects don't know if they received the treatment (medication or placebo) and neither does the experimenter, so there is no bias and results could be more accurate.

Demand Characteristics

-A term used to denote the situation where the results of an experiment are biased because the experimenters' expectancies of the subject's performance create an implicit (stating it) demand for the participants to perform as expected. EX: The experimenter implied through a document they had to sign that the subjects would feel happy after taking the medication, so after taking it they immediately stated that they felt better.

Why does animal experiments create controversy?

-Don't have a say in what they want to be in -Animals are used in experiments where they are exposed to treatments that would be unnaceptable to humans. This creates controversy because animals shouldn't be placed in harm's way as they argue that animals have the same rights as human's and are therefore being violated of those rights. (Reason why there are many board protecting their rights)

Alfred Kinsey

-Kinsey Reports -an American biologist who started his career in zoology and then shifted his interests towards sexual orientation, beliefs and behavior. He was the first to create a survey where he gathered data from over 10,000 people on their sexual orientation, beliefs and behavior. However, because it was the 1950s, an era where people were uptight and less open with those private topics, many lied making Kinsley's research innacurate. However, although it was flawed, it was important because it influenced a new field of research and influenced experiments on human sexuality. EX: Survey

What is the purpose of having an experimental and control group?

-So the results could be compared as the two groups It's a way to make sure that the treatment you are giving is causing the experimental results, not something outside the experiment.

inferential statistics

-Statistics that are used to interpret data and draw conclusions (to be applied for the general population outside of sample). -used after researchers have summarized their data with descriptive statistics and to see whether or not their data supports their hypothesis. -used to evaluate the possibility that their results might be due to fluctuations of chance. If results can be provided by chance, then they don't support the hypotheses. EX: The researcher used inferential statistics to see if his hypotheses was correct: that studying outside would make them focus more and test better. According to the mean, the scores were higher in the experimental group (the ones who studied outside),so, the researcher analyzed the data and made sure that the reason they scored better was truly because they studied outside so he could decide whether or not his results supported his hypotheses.

Variability

-The extent to which the scores in a data set tend to vary from each other and from the mean. -as variability increases so does the standard deviation EX: The test scores in the experimental group ranged from 85-90, but the scores in the control group varied from 70-85. The variability is greater in the control group because there is a great set of scores and variety overall compared to the experimental group.

meta-analysis

-a procedure for statistically combining the results of many different research studies -allows researchers to test the generalizability of their findings and the strength of a variable's effects in a relatively precise and objective way. -see if their esults are accurate in comparison to to others. EX: The researcher found that sleep had a direct effect on a person's mood. Those that slept less seemed to be more aggravated and those that slept more were happier. Once the researcher discovered this he performed meta-analysis where he examined the results of previous studies that had to do with how sleep effected a person's mood to see if his results add up to other similar studies.

what is the importance of statistics?

-allows researchers to make sense of their data and use it in order to draw conclusions based on their observations.

Jane Goodall

-an English primatologist and anthropologist who studied chimpanzees' behavior. She set camp in a forest and observed these animals. At first the chimps changed thir behavior because they felt as if they were being watched. However, she stayed for such a long time that they eventually ignored her and acted normally. This is perfect example of naturalistic observation as she observed these chimps in their natural environment without manipulating anything about them. Her longterm observations allowed for groundbreaking discoveries about chimpanzees' behavior like how they make tools, eat and hunt for meat, and have similar social behavior to humans, expanding the knowledge of what we knew about chimpanzee's behavior. EX: naturalistic observation

Institutional Review Board (IRB)

-an administrative body that protect the rights of human research subjects chosen to participate in research activities. This is important because it decides whether something is ethical or not and protects the general welfare of people. -:Have basic guidelines written in the Geneva code fundamental rights of human participants (ex: debriefing at the end, giving consent and could leave).

Institutional Animal Care and Use Committee (IACUC)

-an administrative body that protects the rights of animals used in scientific research by making sure that the highest welfare standards for animals is maintained as they decide whether the research involving animals is ethical or unethical.

Correlation and Causation

-correlation does not mean causation as it does not mean that the correlate variables created a cause and effect relationship. It could be that they have a similar their variable causing that effect. EX: Relationship: as ice cream increases so do shark attacks. The correlation is due to the fact that as tempetkres increase ice cream sales rise and more people ar at the beach, so shark attacks increase, not that the sales affect the attacks or vice versa.

Random Assignment (Pros)

-diminishes threat to validity (more valid because their characteristics are more disperse in both the control and experimental groups-diminishes effects of confounding variables) -eliminates bias -easier to generalize results to a larger population -allows to prove cause and effect (Randomly assigning subjects helps to eliminate confounding variables)

Halo effect

-occurs when one's overall evaluation of a person, object, or institution spills over to influence more specific ratings or specific characteristics of that person or thing. EX: studying an employer's merit (pay for performance) might create specific ratings making you believe that they are dependable or take initiative.

Why do researchers conduct multiple studies for replication?

-their research could have bias. -allows for science to identify and remove erroneous findings. -If result are contradictory, the empirical approach accounts for this with meta-analysis

Why are animals used for experiments?

-they want to know the behavior of a specific animal -whether certain laws of behavior apply to both humans and animals -they can expose them to treatments that would be unacceptable to humans.

Phineas Gage

-was a railroad worker who had an accident on site where the iron rod they used to build the railroad went straight through his head and completely damaged his brain's left frontal lobe, the part of the brain that controls emotions. He survived and his accident had no physical effects, but it did emotionally as he became more agressive. He is important to psychology because he was the first patient that showed the relation between personality and the function of the front parts of the brain. EX: Case Study

How can experimenter bias be avoided?

-with a double-blind procedure because neither the subjects nor experimenters know who is in the experimental or control groups. Experimenters -can't have influence over the groups and has no preference over the experimental or control group, so the judgements are valid) Participants: -are unaware of the hypothesis etc, so they won't change their behavior -self-fulfilling prophecy: an expectation that causes you to act in ways that make that expectation come true (you get what you expect) -the hypothesis they have in mind could change their behavior (Ex: Horoscopes aren't valid, but they happen because you believe it, so it changes your behavior) =results will be accurate.

Types of Research Methods

1. Correlational Design 2. Experimental Design

Negative (Indirect) Correlation

An inverse relationship between two variables where as one variable increases, the other decreases, and vice versa EX: As people's age increases, their physical abilities decrease.

Extraneous variables

Any variable, apart from the independent variable, that can cause change in the development variable and therefore affect the results of an experiment in an unwanted way.

Surveys

Correlational Design: -A descriptive research method in which researchers use questionnaires or interviews to gather information about specific aspects of the subjects' behavior. PROS: -can obtain behavior that is difficult to observe directly. -Data collection is easy, quick, inexpensive. -ethical CONS: -Potential participant's tendency to participate in survey's has decreased due to a rise of doubts of their own privacy and telemarketing. -Not completely reliable: depends on self-report data (people could lie, have memory lapses or not understand what is being asked of them). EX: the researcher conducted a survey on how the hours of sleep they get affects their overall mood with a series of questions.

Operational Definition

Describes the operations that will be used to measure a variable. (How variable is measured in the experiment) PROS: -Improves reliability and validity by proving clear concrete descriptions of the variables. -so that anyone could replicate your experiment. EX: The researcher wanted to test if video games caused aggression. His hypotheses was that if a teen played video games for over 5 hours every day, there would be an increase in aggression. He used operational definitions by explaining that he'd measure aggression with how violently the teen reacts to situations. EX: if you're studying intelligence you should say how you are sitting it such as by measuring intelligence with GPA

Experimental Design

A design in which researchers manipulate an independent variable and measure a dependent variable to determine a cause-and-effect relationship. -impacts their life; changes something about their life -Introduces new variables to subjects and records differences PROS: -can prove cause & effect (due to the precise control over the variables (can isolate the independent variable and its effect on the dependent variable while controlling extraneous variables) -eliminates outside variables CONS: -ethical considerations (potential side effects and harm by imposing something new in their life) (EX: One may want to study whether or not a poor diet affects a pregnant woman, but one can't select some women to eat healthy and others to eat poorly because it puts them in harm's way. -expensive -artificiality- could behave differently at lab compared to in a natural setting. -could be impossible (you can't recreate an entire society or community/setting to study) EX: Lab experiments, clinical trials

negatively skewed distribution

A distribution in which most scores pile up at the high end of the scale. (results from extreme scores)

positively skewed distribution

A distribution in which scores pile up at the low end of the scale (results from extreme scores)

scatter plots

A graph with a cluster of dots to show a possible relationship between two sets of data. Direction: positive or negative Strength: if more clustered, more strong

Correlation Coefficient R

A numerical index of the degree of relationship between two variables. (How correlated they are- analyzed with strength and direction of correlation) -If the direction is positive they correlate and go in the same direction -if its negative they covary and go in the opposite direction. -The size of the coefficient determines its strength of the association between two variables where "0" means no correlation and +1.00 or -1.00 means a perfect correlation. -as correlation increases in strength, the ability to predict one variable based on knowledge of the other variable increases. EX: The experimenter studied the relationship between studying and test scores. He found that there was a strong positive correlation between studying and test scores and stated that the correlation coefficient was +0.8.

Positive (Direct) Correlation

A relationship between two variables in which both variables move in the same direction (as one variable increases, the other variable increases and vice versa). EX: The more a student studies the higher their test scores.

Experiment

A research method in which the investigator manipulates a variable under carefully controlled conditions and observes whether any changes occur in a second variable as a result.

Random Sampling (Def)

A sampling technique where every member of the target population has an equal chance of being selected. (how you get participants from an entire population) EX: The researcher chose Doral Academy as the target population, randomly choosing students from their ID number to participate in his survey on how sleep affects their performance at school.

Hypothesis

A testable prediction; A tentative statement about the relationship between two or more variables. -must adapt/change hypothesis to go with what data says)

Third-variable problem

A type of confounding in which a third variable leads to a mistaken causal relationship between two others. EX: A correlation was found between ice cream sales and drowning rates. It was found that as ice cream sales increased so did drowning rates. However, these two variables are not directly associated, so the third variable, weather, created a mistaken casual relationship between the two variables. As it gets hotter, ice cream sales go up and more people go to the pool, increasing drowning rates.

Within-subjects Design

A type of experimental design in which one group is exposed to every treatment or condition and the comparisons are made within that group. -could be to follow same ppl over time (change/time) -mostly used in cognitive psychology (how client has grown, changed, etc) EX: The researcher wants to test if studying outside affected the performance on a test. The researcher used the same group first, placing them outside to study and then taking the exam. After, they'd be inside studying and then took the exam. The results would then be compared.

Between-subjects Design

A type of experimental design in which the subjects of an experiment are assigned to different conditions, one group as the experimental and the other as the controlled. -most common EX: The researcher wants to test if studying outside affected the performance on a test. The researcher used two different groups. The experimental group was placed outside to study and then took the exam. The control group was placed inside to study and then took the exam. The results would then be compared between the two groups.

Quasi-Experimental

Research that involves the manipulation of an independent variable without the random assignment of participants to conditions. EX: The psychologist uses the quasi-experimental design to study how personality traits impact intelligence. Because she is studying personality traits the groups cannot be randomly assigned since they have to be made based on their characteristics.

Participants/Subjects

The persons or animals whose behavior is systematically observed in a study. EX: Participants were being used in an experiment because they were being tested on whether an increase in anxiety would make people want to be in the company of others by using deception as they'd be told that they'd be shocked, increasing their anxiety, where their behavior was recorded.

illusory correlation

The phenomenon of perceiving a relationship between variables even when no such relationship exists. EX: A woman went to Italy and the taxi driver was rude. Then, so was the receptionist when she arrived to the hotel. She made the assumption that people in Italy were rude, therefore, being an illusory correlation as Italy and rude people dont exist. It was only an assumption due to what she perceived being there.

Replication

The repetition of the study to see whether the earlier results are duplicated. EX: The researcher conducted an experiment where he found that a plant's exposure to sunlight made it grow faster. He repeated his experiment to make sure his results did not occur by chance and that he'd get the same results to make sure its accurate.

Median

The score that falls exactly in the center of the score distribution. (do it by placing numbers from smallest to largest and finding the middle) PROS: -resistant to outliers

Mode

The score that occurs most frequently in a distribution (not used often) PROS: -can use it with both qualitative and quantitate variables

Experimental Group

The subjects in a study who receive some special treatment in regard to the independent variable. EX: The experimental group in the experiment was the one who was going to receive the medication for anxiety.

Confirmation Bias

The tendency to seek information that supports one's decisions and beliefs while ignoring disconfirming information. EX: The client told the doctor he was experiencing a stomach ache. He confirmed what the doctor asked: that he experienced nausea and low grade fever, so the doctor told him he had a stomach virus. This shows confirmation bias because the doctor did not ask about what caused any of the symptoms, only about the symptoms that are consistent with preliminary diagnosis that would support his beliefs, without asking questions that could rule out the stomach virus which would be the disconfirming information.

Histogram

a bar graph depicting a frequency distribution

frequency polygon

a graph constructed by using lines to join the midpoints of each interval

placebo

a substance that resembles a drug but has no actual pharmacological effect.

Response set

a tendency to respond to questions in a particular way that is unrelated to the content of the questions. EX: to agree with everything in a set.

cross sectional designs

a type of research design in which one collects data from many different individuals at a single point in time. PROS: -samples different individuals from various time points -less costly and time-consuming compared to longitudinal designs CONS: -prone to cohort effects (a confounding variable) There's so many different people, there's a variety in their characteristics (like how they grew up) so they are more differences than just the variables you're studying and can affect the results. -cannot imply a trajectory

Naturalistic Observation

correlational research: -the researcher observes behavior in its natural environment without directly intervening with the subjects. PROS: Ecological validity-Allows researchers to study behavior under conditions that are less artificial than experiments, so the behavior is more accurate as it is how they'd act in the real world. 2. Good place to start first investigating a phenomenon= good for inspiration. You observe and then see what you want to focus on. 3. Can be used to study animal behavior. (Ex: Jane Goodall) CONS: -Subjects exhibit reactivity (a psychological phenomenon that happens when someone changes the way they behave because they know they're being observed) which is a disadvantage because they are not acting as they normally would. -hard to translate naturalistic observations into numerical data for analysis. EX: A group of scientists set camp in the forest and observed how animals nurtured their young.

Confounding Variables

factors other than the independent variable that may cause a result. EX: The experiment showed a correlation between the two variables: that people who were anxious asked to be with others. However, the confounding variable is the sociability of the person such as being introverted or extroverted. That may change whether they want to be with others or not.

Range

largest score to smallest score

statstcal significance

means that the probability that the observed findings are due to chance are very low.

Experimenter Bias

occurs when a researcher's expectations or preferences about the outcome of a study influence the results obtained. -could effect the researcher's observations and the subject's. -"see what they want to see" (change their perception like a referee who favors a team will scrutinize the opposing team more) -experimenters could also unintentionally influence their subjects by giving nonverbal signs such as smiling, nodding etc.

Social Desirability Bias

occurs when people give socially approved answers to a question to make themselves look better even when its not true (especially with sensitive topics)

Single-Blind Procedure

only participants don't know groups or hypothesis

random selection

process in which subjects are selected randomly from a larger group such that every group member has an equal chance of being included in the study

Confederate

research actors hired to secretly participate along with actual subjects so researchers can manipulate the social setting and study participants in complex social settings and reliably capture real reactions.

longitudinal design

research design in which one participant or group of participants is studied over a long period of time PROS: -follow the same group of people overtime (cause and effect) -observes developmental or treatment trajectory very accurate CONS: -can take a long time and ($) to conduct -issues with internal validity (how well cause and effect can be proven) Ex: a pandemic impacts the results of agoraphobia because they are staying more inside-solved with the use of a control group

Standard Operating Procedures (SOPs)

specific sets of written instructions about how to perform a certain aspect of a task

What are the problems in self-report data?

1. Social Desirability Bias 2. People may misunderstand the questions because of the way the question is worded 3. Could encounter memory lapses and answer incorrectly.

Variations in Designing Experiments

1. Using one group of subjects who serve as their own control group: EX: within-subjects design -ensures that the conditions are alike in any extraneous variables such as their personal skills and motivations, making comparisons between the experimental and control group more accurate. 2. Manipulating more than one independent variable in a single experiment to see if they interact to effect the dependent variable. EX: one can test two independent variables: whether temperature and loud music affected typing speed. If the loud music only affected the speed at a high temperature than there would be an interaction. 3. Using more than one dependent variable in a study: -can be done to get a more complete picture of how manipulations affect behavior. EX: The dependent variable of typing performance could be a)the words per minute and b) the accuracy.

Human Ethical Principles in Research

1. do no harm (physically or psychologically) 2. Confidentiality -exceptions if you indicate harm to self/others, neglect, potential victimization (abuse/neglect) 3. Self determination (have a choice to leave) 4. a risk/benefit analysis: compares the risks and benefits -more benefits allow you to have more risks (ex: if you have a potential cure for Alzheimer's you are allowed to do it even if it has bad side effects) 5. informed consent: participant must be aware of what you're doing, what risk may be involved, and their rights as a participant -in order to give consent the participant must be 18 years of age or older and be able to give consent) 6. Debriefing & protection from harm -some level of deception may be required to keep it "blind" (Ex: in the Milgram experiment they couldn't say they were testing obedience because they may react/behave differently). 7. freedom to withdraw at any time without penalty

When can a casual relationship be determined?

1. the cause/ independent variable must be before the effect 2. cause and effect covary (meaning they have a predictable relationship- if you know X, you can predict Y) -positive/direct slope (X increases, Y increases) -negative/indirect slope (X increases, Y decreases) -nonlinear if you know the value of X you can predict why 3. the relationship you're observing can't be explained by a confounding variable

correlation

when two variables are related to each other. -used to see if the two variables they are studying are strongly associated *important because the whole point is to see if the independent variable affects the dependent variable.

Pros & Cons of Deception

CONS: -puts the subject in harms way because the researcher is lying which is unethical -could cause people to not trust others -creates distress such as by thinking they will be harmed or harm others -they may feel foolish when it is explained that they were simply deceived and that it was part of the experiment. PROS: -researchers argue that without misleading them they wouldn't be able to investigate many important issues. -They say that often times they don't harm their subjects and that there is no empirical data to prove that they are being harmed -that deception is acceptable if it advances knowledge to improve human welfare -most subjects have reported that they didn't mind being misled and there is no support that after subjects have less trust in others. (Therefore, the researchers are more concerned over the negative effects of deception rather than the people themselves. This is why boards and committees exist to decide whether or not a study is ethical)

Case Studies

Correlational Design: -an in depth investigation of an individual subject. -Techniques: interviewing subjects, direct observation, examination of records, and psychological testing with one individual or small groups. -Frequently used by clinical psychologists who diagnose and treat psychological problems to study patterns that permit general conclusions. (Cases applied to victims of suicide are called psychological autopsies). PROS: -Detailed, holistic narrative of the factors that contribute to an individual's current state. -can be used to investigate certain phenomena such as the roots of psychological disorders and the efficacy of therapeutic practices. -can provide real life examples that can support a hypotheses or theory. CONS: -suffer from sampling bias (subjective because researchers could only focus on the information relevant to their expectations and theoretical slant. Thus, its easy for an investigator to see what they want to see). -Often unrepresentative of the general population because it is only one person's case of the situation. EX: Henry Molaison, (H.M) had severe epilepsy, so he went to a neurologist that burned out his hippocampus (which causes seizures and is used to store memory). when he woke up he could only remember his past and could not make any new memories. This case study helped scientists learn the hippocampus' function: to store long term memory.

Archival Research

Correlational Design: A research method that involves using evidence from archives or documents that already exist. EX: US Census ; Arrest Records CONS: -evidence can only be collected in hindsight (understand it after it happens) -constrained by record's availability & accuracy (EX: arrest records: the person behind the arrest could have been biased, so the severity or what happened could be inaccurate).

Random Assignment

where subjects are randomly assigned into the experimental or control conditions. EX: The researcher used random assignment by giving each subject on a number, randomly choosing the numbers, placing each subject in either the experimental or control group.

Empirical Data

Information acquired by observation or experimentation. EX: After conducting a survey, the researcher gathered the empirical data: that 80% of the people surveyed felt better mentally and physically after exercising, 10% felt worse because they felt fatigued, and a 10% didn't feel any difference afterwards.

Random Sampling (Pros/Cons)

PROS: -Allows for generalization of results to the entire population because there is a little bit of everyone who has different characteristics CONS: -Very difficult to do (rare) -not practical because (need time, money & resources to go around the world for a large amount of people. EX: the effect of people in New York could be different from an effect in Miami ethical considerations consent they have to volunteer time). -people who want to participate are a specific type of people volunteer bias

Pros & Cons of Descriptive/Correlational Research

PROS: -give researchers a way to explore questions that could not be examined with experimental procedures. -broadens the scope of phenomena that psychologists are able to study (ex: researchers want to learn how growing up in an urban and rural setting relates to people values-but you cant control where people grow up so you need to observe only). CONS: -Cannot be used to study cause and effect relationships because one is not manipulating the independent variable and therefore, cannot find the direct relationship between the two variables.

Matched Pairs Design

Pairs of subjects or parts of subjects are separated and given different treatments. PROS: -gets rid of confounding variables- EX: identical twins (one twin has a treatment and the other twin doesn't) you will know the cause is your independent variable and its effect because they have the same genetic makeup. EX: Mark and Scott Kelly were identical twins. One twin lived on space (experimental) and the other one on earth (controlled). As a result, the twin who live on space had a different genetic expression

correlation and regression

Statistic that describes potential relationship between strength and direction between two variables -1 = negative correlation (as x increases, y decreases) +1 = positive correlation (as x increases, y increases) 0 = no relationship

Descriptive Statistics

Statistics that are used to organize and summarize a sample of data including measures of central tendency, variability, and the coefficient of correlation. EX: The researcher used descriptive statistics when he found a positive correlation in his data: that the more students studied, the higher their test scores were.

Measures of Central Tendency

Statistics that describe the center of a data set (mean, median, and mode). EX: The researcher used measures of central tendency when he found the median, mode and mean or average of the test scores in the control and experimental group to organize the data he gathered from his experiment.

Control Group

Subjects in a study who do not receive the special treatment given to the experimental group. The control group was the one who was not manipulated, so they received no medication for anxiety.

Mean

The average of the scores in a distribution. N=sample size (total # of participants) n=# of participants per group EX: 24 total volunteers; 12 groups/12 guards N = 24 ; n = 12 PROS: -most popular (works best with normal data-with no extremes) -generally most valid CONS: -vulnerable to outliers - can make average fluctuate as to would go towards the outlier and make what's "normal" wrong

Placebo Effects

The fact that the subjects' expectations can lead them to experience some change even though they receive an empty, fake, or ineffectual treatment EX: The person thinks that the pill will help them and therefore with these expectations change their feelings, reactions, and behavior.

subject/participant

The individual taking part in an experiment

Ecological Validity

The measure of how test performance predicts behaviors in real-world settings. EX: A group of researchers set camp for 5 months in a rainforest to observe how sloths behaved. Their results have ecological validity because they were being observed in their natural habitat and didn't change their behavior since they got used to the researchers being there. Therefore, the behavior they observed is what one would observe in a real-world setting.

correlational study

a type of research design where a researcher seeks to understand what kind of relationships naturally occurring variables have with one another. -observes the potential relation between multiple variables without intervention. -Only observes: looks for trends and records. -Takes people as they are: NOT changing anything. PROS: -ethical -saves time & money -good way to begin investigating (because by observing you could see what interests you more and narrow down your test. CONS: -cant show cause & effect because theres no control over variables. Theres influence of outside variables, so you can't prove what caused it and its effect. EX: you are following two people: a nonsmoker and smoker. The smoker got cancer and the other did not. It could have been because of the persons diet (confounding variable) and not the smoking at all. You aren't studying this variable, but it could affect your result. EX: surveys, case studies, naturalistic observations, archival research -some relationships can only be investigated using correlation design. EX: tobacco & cancer You can't do experimental tests because it would be unethical. For example, you get two groups and make one group to live normally and force the other group to have 2 cigars a day. This is unethical because you are putting them in harms way. -correlation is not causation some things are correlated by coincidence

Why can the mean be misleading?

because it is sensitive to extremities or outliers, so it could make the mean misleading as the "average" will take the extreme into account and will make the average drift towards the extreme making it not be the true average.

Z scores

tells you how far away an individual score is from the mean. -If + Z then the score is above mean -if - Z then the score is below mean -if Z is 0 then its average at the mean -the greater the absolute value of Z the further you are from the mean and the more rare the score is

normal distribution (bell curve)

the pattern of scores usually observed in a variable that clusters around its average which compares your scores to everyone else's scores. Empirical Rule: The rules gives the approximate % of observations w/in 1 standard deviation (68%), 2 standard deviations (95%) and 3 standard deviations (99.7%) of the mean when the histogram is well approx. by a normal curve (such people will be at such percentage) This is called a normally skewed distribution -most things follow the even distribution -compares your individual score to the bell curve -tells you how rare your score is

Statistics

the use of mathematics to organize, summarize, and interpret numerical data.

what is the purpose of the placebo effect?

to measure the actual effect of the treatment being tested and see if its the treatment that is causing the effect. (The placebo group is compared to the treatment group).

What is the purpose of an experiment?

to see how the independent variable (the one manipulated) causes change in the dependent variable (the one affected)

what is the purpose of an experiment?

to understand relationships between variables to help society and apply it to real life.