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IA Research Method & Design

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  1. Year 2 IB Psych Only IA Research Method & Design

  2. Methodology • The research method used. • Does more than outline the researchers’ methods • “We conducted a survey of 50 people over a two-week period and subjected the results to statistical analysis”

  3. The Scientific Method • 1. Define a research problem • 2. Propose a hypothesis and make predictions • Hypothesis: A testable prediction • Must have operational definitions (a statement of the procedures used to define the variables) • Ex: human intelligence is defined as what an intelligence test measures. (teacher without a rubric?) • Must be replicable. (repeatable) • 3. Design and conduct a research study • 4. Analyze the data • 5. Communicate the results, build new theories (modify and try again)

  4. Correlational Research • Detects relationships between 2 variables (X & Y, dogs & cats) • Does NOT say that one variable causes another. • # of books read= $$ salary

  5. Correlational Research 45 degree angle +1 • perfect positive correlation (+1.00) high scores on 1 set are associated with high scores on another set • (ex: between children’s age and height) • perfect negative correlation (-1.00) high negative correlation • (dancing accidents and amount of alcohol drunk) X (IV) horizontal line 45 degree angle -1

  6. How to Read a Correlation Coefficient

  7. Correlations • Independent variable on X axis • Dependant variable on Y axis Y axis Y axis X axis

  8. Scatterplot • A graphed cluster of dots; each represents the value of 2 variables • The slope of the points indicates the relationship • The amount of scatter suggests the strength of correlation (high or low) High Low • No Correlation (scatterplot) correlation of 0.00 shows the 2 sets are not related

  9. Illusory Correlation Mr. Pointy always gives me a better math score! • Sometime we see relationships which do not exist • We believe there is relationship and so we recall instances which confirm our belief • Ex: Length of marriage relates to male baldness?

  10. Experimental Research • The researcher manipulates 1 or more factors • Explore cause and effect relationships. • Observe the effect on some behavior or mental process • Controlled Observation • You control & manipulate the environment and the variables • Mozart causes depression • Bananas cause constipation

  11. Research Design

  12. Research Design • Skepticism: • A researcher needs to be skeptical (doubt until proven) • Let the data speak for itself

  13. Research Design • Overconfidence: confirmation bias (if you are not skeptical) “Of course it will be X” • We tend to think we know more than we do. • 82% of U.S. drivers think they are the top 30% in safety • 81% of new business owners believe their business will succeed. Their peers? Only 39%. (Now that’s overconfidence!!!)

  14. Research Design • Hindsight bias: “I Knew It All Along” • The tendency to believe, after learning the outcome, that you knew it all along. • Looking backwards • Solving a puzzle, once it’s done • “Oh, that was obvious.” • “Of course, ANY dummy could see Sept. 11, 2001 would happen.” Not Sept 10th!

  15. Research Design • Replication • You are able to repeat the experiment • You will get similar results no matter how often you repeat • Operational Definition • Procedures used in defining research variables • Narrow down the focus of the study • Study Ritalin, all kids? ADHD? Boys? WS? All schools? 1 school?

  16. Research Design • Hypothesis: what problem or situation do you want to solve, test, or discover? • Example:I want to see the results of Ritalin on young boys (age 13-16) who have ADHD, and live in Winston Salem, NC.

  17. Research Design • Population - large group • The subjects or people to be studied • Sample: draw from your Population • Age, gender, geographic location • Random from population • Random selection and assignment • Representativeness • Sample accurately reflects the population

  18. Random Sampling

  19. Research Design • Representativeness: • What percentage do you need? • Depends on the population in the study. • 100 out of 500= 20% (larger pop, lower %) • 10-15 out of a class of 25= 40-50% (smaller pop, larger %) • Netherlands studies 800,000, only need 4-5% (40,000 people in study)

  20. Research Design • Random Assignment to Groups • Do not categorize based on gender, age, size, GPA, etc. • Control Group: No change (not exposed to IV) • Experimental Group: Change 1 variable (only 1)

  21. Research Design • Independent Variable (IV) • What is being introduced, what is new, what are you changing? • What is being manipulated? • Dependent Variable (DV) • What is being measured? • The change caused (or not caused) by the indep. variable.

  22. Confounding Variables • Any extraneous variables that could cause data contamination • False consensus effect - we tend to overestimate the extent others share our beliefs and behaviors. • Reactivity: When a subject’s behavior is changed because s/he knows that s/he is being observed

  23. Confounding Variables • Demand Characteristics: When a subject behaves in the way that s/he thinks the experimenter wants, rather than in a natural fashion • Experimenter Bias: Certain behaviors from the researcher bias the subject’s behavior (*) • Experimental condition - actual setting, paint color, music, noise, time or day, what the subject ate for breakfast, the weather, the season of year

  24. Research Design • Prevention of Contamination • How to stop those Confounding Variables • Single Blind: subject is unaware (Persistent/Stubborn) • Double Blind: Assistant & Subject Unaware

  25. Research Design • Statistics & Data • T-test, CHI-square, Z-score • Psychometrics • Statistical Significance • “I want to prove that my independent variable causes my dependent variable 95% of the time” • 95% to be valid • Probability= P<.05(5%) chance, random, chaos theory

  26. T-Test, Chi-Squared, Mean, Median, Mode Statistical Methodology

  27. 2 Types of Statistics • 2 types of analysis techniques: • 1. Descriptive statistics: techniques that help summarize large amounts of info. Include measures of variability and measures of correlation (Describe the data) • Population, Bag of M&Ms • 2. Inferential statistics: techniques that help researchers make generalizations about a finding, based on a limited number of subjects • Sample, Handful of M&Ms

  28. Descriptive Statistics • Frequency distribution - organizational technique that shows the number of times each score occurs, so that the scores can be interpreted • Graph depictions • frequency polygon - curve • frequency histogram - bars

  29. Descriptive Statistics • Central Tendency - a number that represents the entire group or sample • Tend to hover towards the center • Average IQ score, around 100 • 2 genius parents tend to have average IQ child • Politicians (Dem or Rep) dance in the center for max. votes • Weight distribution

  30. Descriptive Statistics • The Bell Curve • Grades, IQ, Poverty • Link between intelligence and salary • When did a C become an F? • Is a C acceptable? C=average • Does everyone get a trophy, ribbon? • Can everyone get an A?

  31. Descriptive Statistics • mean - the arithmetic average • median - middle score when arranged lowest to highest • mode - the most frequent score in a distribution • unimodal - one high point • bimodal - two high points Set: 2, 2, 3, 5, 8 Median: 3 Mode: 2 Mean: Add up (20), divide by 5= 4

  32. Descriptive Statistics • bimodal - two high points • The more overlap in the bimodal arches, the higher the variable link between the data • The less overlap, the lower the connection

  33. Descriptive Measures • 2 ways we measure: • 1. Range: Highest score minus the lowest score--tells how far apart the scores are • simplest measures of variability to calculate. • (weakness of range: it can easily be influenced by one extreme score, Savant IQ of 220) • Set: 2, 2, 3, 5, 8 • Range: 8 - 2 = 6 Ex: Age Range 15-17, Difference 2 7-17, Difference 10 Child prodigies, Dougie Houser, Chess, sci, art, music

  34. Descriptive Measures • The other way to measure is: • 2. Standard Deviation: measure of variability that describes how scores are distributed around the mean. • (1 SD, 2 SD, -1, -2) • Central Tendency: tend to hover near the center.

  35. Standard Deviation 1% outliers Savant, 220 1 in 30 million 34% 34% 13.5% 13.5% 2% 2% 68% 95% 99%

  36. Set: 2, 2, 3, 5, 8 Standard Deviation To calculate standard deviation (SD): • 1. find the mean of the distribution 4 • 2. subtract each score from the mean 4-2, 4-2, 4-3, 4-5, 4-8 • 3. square each result – “deviations” 4-2=2 2 squared=4 • 4. add the squared deviations 4 + 4 + 1 + 1 + 16 = 26 • 5. divide by the total number (n - 1) of scores; this result is called the variance 26 / 4 (5 – 1) = 6.5 (V) • 6. find the square root of the variance; this is the standard deviation (SD) 2.55 (SD) • 7. n = biased sample – does not accurately represent population being tested (out of the norm, get rid of out-liers) 5 • 8. (n - 1) = unbiased sample 4 • 9. now you can compare distributions with different means and standard deviations (ex: 3 different class scores, 78, 80, 92)

  37. Sigma Σ • Σthe symbol for standard deviation (SD) is s. • Greek letter “sigma” (lower case form) • S upper case letter (other Greek “sigma”) • Standard meaning in mathematics, “add up a list of numbers.” • Represents Sum, i.e. add together

  38. Z-Score Z-scores: a way of expressing a score’s distance from the mean in terms of the standard deviation (SD) • to find a Z-score for a number in a distribution, subtract the mean from that number, and divide the result by the standard deviation 8 – 4 (M)= 4 / 2.55 (SD) = 1.56 • a positive Z-score shows that the number is higher than the mean (You’re OK, IQ, health average or higher) • a negative Z-score allows psychologists to compare distributions with different means and standard deviations (Below average, health, psych concerns) • Sometimes Z-scores are necessary to explain standard deviation in an experiment’s results/discussion NEG Z POS Z

  39. Skewed Results • When there are more scores at the high or low end of a distribution it is said to be skewed • tail signifies the extreme score • Single tailed = extreme score on either side • Which direction are the “outliers?” • Called Right/Left Skew • Also Pos./Neg. Skew Majority Majority Outliers: fringe, oddball, genius, bad egg

  40. A Skewed Distribution Are the results positively or negatively skewed? Positive Skew or Skewed Right

  41. Inferential Statistics Tests of Significance - used for determining whether the difference in scores between the experimental and control groups is really due to the effects of the independent variable or just due to random chance • If p < .05 (95%) the outcome (or the difference between experimental and control groups) has a probability of occurring by random chance lessthan 5 x per 100 • Researchers conclude the effect of the independent variable is significant (real).

  42. Inferential Statistics • Statistically Significant – • It is concluded that the independent variable made a real difference between the experimental group and the control group • Ritalin really DOES help ADHD • Raising serotonin levels DOES help Depression (yoga)

  43. Null Hypothesis • Null Hypothesis: any alternative hypothesis, if yours is wrong! • Significance tests are used to accept or reject the null hypothesis. • If the probability of observing your result is < .05 (95%) • Your theory is true, reject the null hypothesis • Meaning that your original hypothesis is possible (without chance, random, chaos) • If the probability of observing your result is > .05accept the null. • Meaning that your original hypothesis is not possible (too much left to chance, random events) • You need a backup, alternative hypothesis

  44. Null Hypothesis Practice • Accept or Reject the Null? • My hypothesis: Drug X will stop sleep walking 95%. • Do the testing. Do the data. • Drug X has a probability of 63%. • Is it greater than or less than 5% chance? <>.05? • Do you accept the Null or reject the Null Hypothesis? • ACCEPT the NULL! My theory was wrong! • 37% chance, error, random • Maybe it’s the patients I chose? • Maybe too much caffeine before bed? • Maybe drug was contaminated in the lab? • Start over, new test, new drug, new data

  45. Null Hypothesis Practice • Accept or Reject the Null? • My hypothesis: Stress causes mice to gain weight. • Do the testing. Do the data. • The “stressed mice” gained weight 97%. • The “control group” of mice showed no weight gain. • Is it greater than or less than 5% chance? <>.05? • Do you accept the Null or reject the Null Hypothesis? • REJECT the NULL! My theory was right! • 3% chance, error, random • Good Job! Bonus and a raise!

  46. Types of Tests • 1. T-Test • 2. Chi-Square Test • 3. Mann-Whitney U • 4. Sign Test • 5. Wilcoxon Matched-Pairs Signed-Rank Test

  47. When to Use the T-Test? • T-Test – when 1variable is used in 2 situations -- Ex: Ritalin effects in either ADHD males or ADHD females -- Ex: subject has to pick out a letter in a round list or a square list