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AP Psychology Research Methods and Stats PPT
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- 2. Research Methods How doyou do research in psychology? Psychology uses the scientific process: Question Hypothesis Data Interpretation
- 3. Research Methods Hindsight biasis the tendency of people to overestimate their ability to have predicted an outcome that could not possibly have been predicted. However, the goal of science is to be predictive; that is, determine an outcome before it happens, not after.
- 4. Research Methods Ways ofdoing research in psychology (research design): There are different ways of doing research: Experimental design (experiment) - Laboratory experiment (usually just “experiment”) - Quasi-experiments (like an experiment*) - Field experiments (like an experiment*) Naturalistic observation Correlational design (correlation) Survey Case Study
- 5. Research Methods What isthe relationship between these variables: Coffee intake and running speed? Showering and body odor? Studying and grades? Mass of an object its gravitational force?
- 6. Research Method Terms Hypothesis:Usually an ‘if, then’ statement or simply a prediction about some event. Theory: aims to explain a broad set of phenomena. Hypotheses (multiple hypothesis’) support a theory. Independent variable: The variable that influences the dependent variable. Dependent variable: A variable that depends on the independent variable.
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- 8. Operational Definitions Hypothesis: Ifpeople are given money, they experience greater happiness than if given candy. How do we measure happiness? Operational Definitions (IMPORTANT) Definitions of variables in research need to be quantifiable (countable) and observable. They need to be operationally defined. Example, happiness: how many times someone smiles.
- 9. Validity & Reliability Researchaims to be valid and reliable Validity refers to whether the research measures what the researchers set out to measure. Reliability refers to whether the same results can be produced under similar conditions.
- 10. Validity & ReliabilityExample If you have a scale, and it says you weigh 100lbs., but on every other scale you step on, the scale says 180lbs., the scale would not be a ‘valid’ measure of your weight. On the other hand, if you stepped on the scale and it said 100lbs., and then five minutes later stepped on the same scale again, and it said 130lbs., the scale would not be a ‘reliable’ measure of your weight.
- 11. Research Method Terms Participantsare the people or subjects in your study. Sample/Sampling: A sample is a set of participants or things taken from a population. Sampling refers to the selection of participants. Population is the large set of individuals from which a sample was taken. Representative sample: A sample that accurately reflects the larger population.
- 12. Research Method Terms Exampleof population and sample
- 13. Research Method Terms Exampleof population and sample
- 14. Understanding Terms Example Wearinga red shirt in a large crowd gets you more attention. What is my hypothesis? What must be operationally defined? Who is my sample? Who is my population? Is the sample representative?
- 15. Understanding Terms Example Whatis a representative sample? A representative sample better reflects the participants of a population compared to an unrepresentative sample** Therefore, whatever research result you find, the results will be able to be generalized to the population.
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- 17. Random Selection Random selectionmeans that every member of the population has an equal chance of being selected. How could we randomly select in the previous example?
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- 19. More on RandomSelection Stratified sampling: A process that allows a researcher to ensure the sample represents the population on some criteria. If I want to research whether different racial groups respond differently to a survey, I could select 10 Caucasians, 10 Asians, 10 African Americans…. Stratified sampling takes place at the selection level. If you want your sample to meet some criterion or criteria, then that is stratified sampling*
- 20. Experimental Method Psychologists preferexperiments because they can establish a cause- effect relationship. Laboratory experiments are conducted in a controlled environment and use random assignment. Field experiments are conducted in the ‘real’ world. Researchers go out and manipulate some variable and observe the effect. Example here. Quasi-experiments are experiments, but do not use random assignment.
- 21. Experimental Research Confounding variables Aresearcher must try to isolate variables or control for “confounding” variables Confounding variable: A confounding variable is any difference between the experimental controls and the control conditions, except for the independent variable, that might affect the dependent variable.
- 22. Confounding variables If Iam studying whether the amount of time spent studying is associated with better grades, what are variables that may influence this relationship other than studying?
- 23. Groups An experiment musthave a control group and an experimental group. The experimental group is that which receives the independent variable. The control group is the group that does not receive the independent variable.
- 24. Understanding Terms Hypothesis: Drinkingcoffee before running makes one run faster. After gathering a sample, who is my experimental group and who is my control group?
- 25. Terms Assignment: Assignment isthe process by which participants are put into a group, experimental or control. Random assignment: The process of randomly assigning participants to either the control or experimental group.
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- 27. Terms What if inmy coffee experiment, I allow participants to join which ever group they want? Participant-relevant confounding variables: If participants were given the opportunity to choose which group to be in, the results might become biased.
- 28. Terms Group-matching: If onewanted to match for sexual orientation, eye color, skin color, or something other variable, then group matching is the process of assigning individuals to groups based on some criteria. Situation-relevant confounding variables: When conducting an experiment, both groups (experimental and control) must be subject to the same environment.
- 29. Terms Experimenter Bias: Thetendency for researchers to treat members of the experimental and control groups differently. If the aim of my study is confirm my hypothesis, I may interpret the results differently than someone who is not involved or does not have an interest.
- 30. Controlling for Bias:Experimental Designs Single Blind: Participants do not know whether they’re in the experimental group or the control group. Double Blind: Neither the participants nor the research knows who is in which group.
- 31. Placebo & PlaceboEffect Placebo is a substance that has no therapeutic effect; it is often used in control groups for testing new drugs. Placebo effect: Participants feel a “psychological” effect, but have not been given a real substance that would cause “real” physiological differences in their body.
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- 33. Putting it alltogether
- 34. Correlational Method Correlation isa statistical measure that indicates the extent to which two or more variable fluctuate together. A correlation expresses a relationship between two variables without ascribing a cause. Correlation does not equal causation.
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- 37. Correlations 9.2.2025 A positivecorrelation between two things means that the presence of one thing predicts the presence of the other. A negative correlation means that the presence of one thing predicts the absences of the other.
- 38. Correlations Predict the correlation Drinkingwater and feeling thirsty Studying and amount of free time Colour of t-shirt worn on exam day and exam grade Number of hours spent studying and grades Amount of coffee drunk and talkativeness Staying up all night and fatigue Number of fictional books read and driving ability
- 39. Correlations Why use correlations,why not always conduct an experiment? Sometimes correlations are the best research method because you cannot manipulate the data in an experiment. Does weather affect shop lifting? Is there any way we can manipulate the weather?
- 40. Surveys Conducting a surveyinvolves asking participants to complete a questionnaire. Using the survey method means that one cannot control for certain confounding variables. Survey method is subject to the social desirability effect: the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others.
- 41. Naturalistic Observation Naturalistic observation:Researchers go out and observe participants in their natural habitat without interfering at all. Naturalistic observation is different from field experiments because in naturalistic observation, researchers do not interfere.
- 42. Case Studies The casestudy method is often used in clinical psychology. Case studies allow researchers to get a picture of a small group of people (as little as one), but this means that the findings cannot be generalized to any population. An example of a case studies may be studying a rare psychological disorder.
- 43. APA Ethical Guidelines Ethicalconsiderations are a major part of research regarding human and animal research. Guidelines are established by the American Psychological Association (APA) for human and animal research.
- 44. APA Ethical Guidelines(Animals) 1. Must have a clear scientific purpose. 2. Questions of research must be important. 3. Animals chosen must be suited for the question. 4. Animals must be cared for in a humane way. 5. Animals must be acquired legally. 6. The experimental procedures must be ones which employ the least amount of suffering possible.
- 45. APA Ethical Guidelines(Humans) 1. No coercion: (participation must be voluntary). 2. Informed consent: (participants know and agree with what you’re doing). 3. Anonymity/confidentiality: participants privacy protected. 4. Risk: as little as possible (physical and psychological. 5. Debriefing: after experiment is done, you inform participants about the nature of the experiment.
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- 47. Research Methods: Statistics DescriptiveStatistics Descriptive statistics describe a set of data. How many people have black hair in this room? How many people play a musical instrument? Good overview video Crash Course’s Methods video
- 48. Research Methods: Statistics DescriptiveStatistics Reason for using descriptive statistics: To organize quantitative information into something that is meaningful. Descriptive statistics are the measures of central tendency (mean, median, mode) and the measures of variability (range, variance, standard deviation).
- 49. Statistics Measures of CentralTendency describe the center of a data set. The measures of central tendency are the Mean, Median, and Mode. .
- 50. Statistics: Mean The meanor average is the sum of scores divided by the number of scores. Find the mean of coffee consumption per day Day 1: 1 Day 2: 4 Day 3: 5 Day 4: 2 Day 5: 3
- 51. Statistics: Mean Sum ofscores / Number of days = Mean Sum the scores: 1+4+5+2+3 = 15 Divide the score by the number of days: 15/5 = 3 Mean or average is 3 cups / day
- 52. Statistics: Median Median isat the midpoint of a frequency distribution, such that there is an equal probability of falling above or below it How to find the Median: Put all the numbers in numerical order. If there is an odd number of scores, the median is the middle number. For example: 1, 2, 3, 4, 5 3 is the median If there is an even number of scores, the median will be the mean of the two central numbers.
- 53. Statistics: Median What isthe median? 6, 2, 9, 4, 7, 3 Rearrange the numbers in ascending order: 2, 3, 4, 6, 7, 9 If there is an even number, find mean of middle two: 4 + 6 = 10. 10 / 2 = 5. Five is the median.
- 54. Statistics: Mean/Median The meanis prone to problems with outliers. Outliers are extreme scores that skew the mean. The median is sometimes a better measure of central tendency when there are outliers. Salaries in a company: •Employee 1: $35,000 •Employee 2: $45,000 •Employee 3: $43, 000 •Employee 4: $400,000 Mean: $130,750 Median: $44,000
- 55. Statistics: Mode The modeis the score that appears most frequently in a set of numbers. 4, 5, 6, 7, 7, 7, 7, 8, 5, 5, 6, 6, 8, 3, 2, 4, 4 What is the mode? (7) There may be no mode if no value appears more than any other. There may also be two modes (bimodal), three modes (trimodal), or four or more modes (multimodal).
- 56. Statistics: Mode Sometimes thereis more than one mode. 4, 5, 6, 7, 7, 7, 7, 4, 8, 5, 5, 6, 6, 8, 3, 2, 4, 4 It helps to rearrange the numbers to get a better picture: 2, 3, 4, 4, 4, 4, 5, 5, 6, 6, 7, 7, 7, 7, 8, 8 This is bimodal distribution
- 57. Statistics: Putting itall together
- 58. Statistics: Normal Distribution Anormal distribution is a function that represents the distribution of many random variables as a symmetrical bell- shaped graph. Measures of Central Tendency are a part of a normal distribution. Helpful video for general overview: https://www.youtube.com/watch?v=hFV71QPvX2I
- 59. Normal Distribution Normal Distribution,Measures of Central Tendency, and Skew
- 60. Skew Positively skewed meansthat a distribution of scores includes an extreme score(s), which give the graph a long tail trailing to the right.
- 61. Skew A negatively skeweddistribution contains more high scores than low scores, but a few really low scores result in the graph having a ‘long tail’ to the left.
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- 63. Measures of Variability Measuresof variability are statistics that describe the amount of difference and spread in a data set. Measures of variability are a part of descriptive statistics, just like measures of central tendency. Measures of variability here include range, variance, and standard deviation.
- 64. Range The range isthe difference between the highest and the lowest score in a distribution. If the highest score is 75 and the lowest score is 25, what is the range? If the highest score is 10 and the lowest score is 1, what is the range?
- 65. Variance Variance is thedispersion from the mean. In a wide variety of cases, we are trying to measure dispersion from the mean due to a multitude of chance effects. In other words, the variance measures how much things differ from the average or mean.
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- 68. Standard Deviation The standarddeviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When examples are tightly bunched together and the shape of the curve is steep, the standard deviation (and the variance) is small. When the examples are spread out and the curve is flat, then the standard deviation (and variance) is large.
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- 70. Standard Deviation example Imagineyou work for the government in the Department of Education, and your job is to allocate funding to schools that need more teaching resources. You’ve narrowed your choices to two schools: ABC School and DEF School. Students at ABC school have a mean GPA of 70% with a standard deviation of 4 Students at DEF school have a mean GPA of 70% with a standard deviation of 28 Who should get the funding?
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- 73. Properties of aNormal Distribution The ND is a theoretical bell-shaped curve. Roughly 68% of scores in a ND fall within one standard deviation of the mean, approximately 95% of scores fall within two standard deviations of the mean, and 99% of sores fall within three standard deviations of the mean.
- 74. More Properties ofa Normal Distribution
- 75. Z Scores Z-scores measurethe distance of a score away from the mean; z-score of a value tells how many standard deviations the value is from the mean. Percentile ranking indicates the distance of a score from 0. Someone who scores in the 90th percentile on a test has scored better than 90 percent of the people who took the test. Someone who scores at the 38th percentile scored better than only 38 per cent of the people who took the test.
- 76. Normal Distribution Someone whoscores at the 50th percentile has a Z score of 0, and someone who scores at the 98th percentile has an approximate Z score of +2 What is my z-score if I am in the 99.9 percentile?
- 77. Statistical Significance Statistical significance– used in hypothesis testing which this course does not cover – is a method to determine whether you can accept your hypothesis. Statistical significance is denoted by the Greek letter α (alpha) and is usually set at 0.05 or 5% in social science research (psychology). A researcher calculates what is called the p-value and compares it to α (0.05), and if the p-value is less than or equal to α then the (alternative) hypothesis is supported. If the p-value is greater than alpha, then the (alternative) hypothesis is not supported (we say the null hypothesis is supported).
- 78. Inferential Statistics Inferential statisticsare methods that enable you to compare your sample data to population data or previous samples. Inferential statistics allow you to see if the findings from your sample can be generalized to the population. In other words, if your hypothesis is supported, then you can say what you found in your sample is true of the population.
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Editor's Notes
- #2 The research methods chapter makes up 8-10% of the exam. One of the FRQs is usually a research methods’ question. This is one of the more important chapters to study. Remember to read the chapter.
- #3 The hindsight bias has also been called the I-knew-it-all along phenomenon. If you toss a coin, guess that the coin will heads, it turns out to be tails, and then say, “I had a gut feeling it was going to be tails!,” this is an example of the hindsight bias. Why didn’t you say tails to begin with then? Overconfidence is another term that may appear on the exam; overconfidence refers to overestimating our belief in an outcome, like an election or how something will turn out.
- #4 Psychologist prefer using experiments because experiments can establish cause-and-effect relationships between variables. Laboratory experiments (or just experiments) are experiments where a sample is drawn from a population and participants are randomly assigned to either the control group and experiment group(s). *Quasi-experiments are a form of experimental design without random assignment (more on this later). *Field experiments are a form of experimental design that takes places outside the laboratory or, for brevity, with ‘real people out in the real world’. Quasi and field experiments cannot yield cause-and-effect relationships because there may be confounding variables (more on this later).
- #7 Just like in math: Independent variable is on the x-axis Dependent variable is on the y-axis
- #8 The exam almost always tests your knowledge of what an operational definition is; either in the multiple-choice questions or in the FRQs. You can operationally define almost anything by making the variable quantifiable (countable) and by making it observable (able to be detected by your senses or some instrument). Sadness or depression could be operationally defined as how often someone cries (we can count that and observe that). We need to operationally define variables so that the experiment can be replicated by other researchers, so that they can provide further support or refute our results.
- #9 Validity and reliability both refer to your research design. Does your research design yield reliable results (i.e., results that are consistent). Is your research design valid (i.e., are you measuring some variable, like happiness, in a way that makes sense?). An invalid – bad measure – of happiness could be observing how often people frown and deciding that is what happiness is; frowning is not what we normally mean by the word happy.
- #13 A sample is a small portion (subset) of the population.
- #15 **For example, if I am studying how drinking coffee influences athletic performance, my sample should take from many types of athletes (football athletes, soccer, baseball, tennis, golf, etc.) rather than selecting my sample from one athletic group (football).
- #17 Random selection and random assignment are different; random selection takes place when you’re choosing participants from you population; random assignment is when you have participants and are assigning them to either the control or experimental group(s).
- #18 Random selection and random assignment are different; Random selection takes place when you’re choosing participants from you population; random assignment is when you have participants and are assigning them to either the control or experimental group(s). The exam will almost always ask about these terms, and your ability to distinguish the two is crucial.
- #19 Stratified sampling and group matching (see later slides) are similar. Group matching takes place when assignment participants to either the control or experimental group. For example, you might want an equal number of men and women in both the control and experimental group. That is an example of group matching. If you were to use purely random assignment – although group matching can still be random – there might be more women or more men in either the control or experimental group. *An example of both: Let’s assume our hypothesis is “if given two cups of coffee before a workout, athletes lift 10% more weight. In our sample, we might include 10 football players, 10 soccer, 10 swimmers, 10 golfers, 10 etc.. So our sample is stratified by type of sport. Group matching would be randomly assigning an equal number of each athletic group into either the control or experimental group. For example, 5 football players, 5 golfers, etc., in the experimental group. Five football players, etc., in the control group.
- #21 Example: Imagine you wanted to do an experiment to see if a new style of running shoe made people run faster. Imagine two groups: one set of people with the new running shoe; another group without. They run around a track and you find that the people who wore your running shoe had a quicker time around the track. But, maybe it wasn’t the running shoe that accounted for the difference; maybe the people in your running shoe group were athletes, taller, older, stronger, (any other variable that might influence speed but which is not the running shoe). Those ‘other things’ or variables that may influence your results are confounding variables. Random assignment reduces the likelihood of confounding variables.
- #25 Random selection and random assignment are different; Random selection takes place when you’re choosing participants from you population; random assignment is when you have participants (a sample) and are assigning them to either the control or experimental group(s). Random assignment must randomly assign participants to either the control or experiment group, like drawing names out of a hat.
- #26 Random selection and random assignment are different; Random selection takes place when you’re choosing participants from you population; random assignment is when you have participants (a sample) and are assigning them to either the control or experimental group(s). Random assignment must randomly assign participants to either the control or experiment group, like drawing names out of a hat. Random assignment – theoretically – eliminates confounding variables.
- #28 Stratified sampling and group matching (see later slides) are similar. Group matching takes place when assignment participants to either the control or experimental group. For example, you might want an equal number of men and women in both the control and experimental group. That is an example of group matching. If you were to use purely random assignment – although group matching can still be random – there might be more women or more men in either the control or experimental group. An example of both: Let’s assume our hypothesis is “if given two cups of coffee before a workout, athletes lift 10% more weight. In our sample, we might include 10 football players, 10 soccer, 10 swimmers, 10 golfers, 10 etc.. So our sample is stratified by type of sport. Group matching would be randomly assigning an equal number of each athletic group into either the control or experimental group. For example, 5 football players, 5 golfers, etc., in the experimental group. Five football players, etc., in the control group.
- #37 Correlation coefficient is another term you might see on the exam. It measures the relatedness of two variables. It ranges from -1 (indicating a strong negative correlation) to +1 (indicating a strong positive correlation). If a correlation coefficient is close to 0, then that indicates that there is not a correlation between the two variables; for example, color of socks worn on exam day and result of exam. Correlational data is plotted on scatterplots.
- #38 Another term you may see on the exam: Illusory correlation: the belief that there is a correlation between two variables when there is not.
- #41 Naturalistic observation is simply observing people or events in ‘real life’.
- #47 Descriptive statistics include measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation). Remember to read the chapter.
- #48 Descriptive statistics include measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation). Remember to read the chapter.
- #54 Outliers and ‘extreme scores’ are synonyms. There can be one or a few outliers that cause a graph to become skewed (more on this later).
- #59 A skewed distribution (positive/negative) is where the mean, median, and mode are not all in the middle of the distribution. Notice how the measures of central tendency vary between a negatively skewed graph and a positively skewed graph. Remember outliers or extreme scores? Outliers cause a distribution to have its skewed shape. I like to think of a negatively skewed distribution like a class of students taking a very easy test: most people do very well, but one or two people do poorly or don’t show up. Those one or two peoples’ scores create the tail of the graph on the left. Likewise, I imagine a positively skewed graph as one where a class takes a difficult math test, and most people do poorly; except for a few of the math whizzes. The people who do well on the test create the tail on the right side of the graph.
- #60 I suggest drawing out each of the graphs. The test might ask a question like: What kind of distribution has its measures of central tendency in this order: Mean, median, mode? (negative skew) Or: If a distribution has the mean, median, and mode in the center, what kind of distribution is that? (normal)
- #63 Remember to read the chapter. To really understand measures of variability (variance, standard deviation), you should first thoroughly understand how to calculate the arithmetic mean or average: Sum of all scores/number of scores. For example, imagine a set of scores {1, 2, 3, 4, 5}, then the mean is: (1 + 2 + 3 + 4 + 5) ------------------- = 3 5
- #64 The range looks at the variation of scores in a data set. It looks at the difference between the highest score and the lowest score. The range can provide meaningful information about a student’s grades, prices of products and services, or athletic performance of a sports team. Figuring out the range of a standardized test like the SAT can tell you a lot about the test and the students: If the range is a small number, then the test may be too easy or too hard, or you may just have a great set of students. In most cases, standardized tests should yield a large range. There is at least one problem here: If there is one or two outliers (extreme scores), then the range may not be the best measure of variability (good thing we have other methods!). Imagine looking at the range of incomes in a Starbucks when a billionaire just happened to be getting a cup of coffee (the range would be skewed by the high incomed). Note: sometimes the interquartile range is used (this eliminates scores at the extreme ends (not tested on in AP Psych)).
- #66 You shouldn’t have to calculate the variance on the AP Psych exam. But if you work though an example, it helps demonstrate the idea and better helps you understand standard deviation (up next). **This equation calculates sample variance. There is another kind of variance called population variance. You will not need to calculate the variance on the exam.
- #67 You shouldn’t have to calculate the variance on the AP Psych exam. But if you work though an example, it helps demonstrate the idea and better helps you understand standard deviation (up next). **This equation calculates sample variance. There is another kind of variance called population variance.
- #69 This equation is for the standard deviation of a sample. You will not need to calculate the standard deviation on the AP Psych exam.
- #70 I am purposely using the standard deviation as the deciding factor in this (while there would be other factors).
- #72 When a standard deviation is large, it means the distribution will be a flattened bell or shaped like a hill. When a standard deviation is small, it means the distribution will be peaked like Mount Everest or the Matterhorn (mountain in the Alps).
- #77 P-value ≤ α (alpha) accept your hypothesis (the alternative hypothesis) P-value ≥ α (alpha) reject your hypothesis (accept the null hypothesis) *In research, the null hypothesis is the opposite of what you set out to support. For example, if your hypothesis is “if someone drinks coffee, they run faster,” then the null hypothesis is “if someone drinks coffee, they DO NOT run faster. When doing research, you always start with the null hypothesis (the negative statement) and try to provide evidence that it’s wrong; in other words, that your hypothesis (the alternative) is supported by the data.