#### Transcript Examples - Solon City Schools

Research Methods It is actually way more exciting than it sounds!!!! Why do we have to learn this stuff? . Be aware however of two hurdles that tend to skew our logic when we research • Hindsight Bias Monday Morning Quarterbacking!!! • Consider findings to be common sense Example: Overconfidence • Overconfidence – • 82% of U.S. drivers consider themselves to be in the top 30% of their group in terms of safety. • 81% of new business owners felt they had an excellent chance of their businesses succeeding. When asked about the success of their peers, the answer was only 39%. (Now that's overconfidence!!!) Overconfidence “There is no reason for anyone to have a computerin their home.” (Ken Olson, president of Digital Equipment Company, 1977) “Heavier-than-air flying machines are impossible.”(Lord Kelvin, British mathematician, physicist, and president of the British Royal Society, 1895) “Reagan doesn’t have the presidential look.”(United Artists executive when asked whether Ronald Reagan should be offered the starring role in the movie The Best Man, 1964) “A severe depression like that of 1920–1921 is outside the range of probability.” (Harvard Economic Society, Weekly Letter, November 16, 1929) “Man will never reach the Moon, regardless of all future scientific advances.” (Lee DeForest, inventor of the vacuum tube, 1957) The Scientific Attitude • Three main components – Curious eagerness – Skeptically scrutinize competing ideas – Open-minded humility before nature Critical Thinking • Critical Thinking - thinking that does not blindly accept arguments and conclusions – “Smart thinking” – Four elements • Examines assumptions • Discerns hidden values • Evaluates evidence • Assesses conclusions – Empirical Approach Scientific Method 1. Observe some aspect of the universe. 2. Invent a theory (an explanation) that is consistent with what you have observed. 3. Use the theory to make predictions, a hypothesis is a testable prediction 4. Test those predictions by experiments or further observations. 5. Modify the theory in the light of your results. 6. Go to step 3. 3Types of Research • Descriptive • Correlational • Experimental 3 Types of Descriptive Research • The Case Study • The Survey • Naturalistic Observation Case Studies • Case study – • Provides oppty to study unusual cases • Offers suggestions for further study • Results often can’t be generalized • Can’t establish cause and effect Example: Case Study Methodology – Methodology • Gather data from one person (or small group) through: Naturalistic Observation • Gather data by watch subjects in a • Do not manipulate the environment. • May be done when it is • Does not show cause and effect. Example: Survey • Gather Data – – – – – Personal facts Behaviors Attitudes Opinions Survey Method Method: •Questionnaire •Interview Can be Descriptive or Correlational Advantages: Example: 1. On ave. how many hours do you study per night? 2. What is your grade point ave? Conducting a Survey • Population - all the possible subjects in a group you want to study • Example: • Random Sampling – a portion of the population that fairly represents the population because each person has an equal chance of getting chosen • Ensures that • Helps avoid false generalizations • Example: Survey Method: The Bad • Low Response Rate • Response Bias/social desirability bias – • Wording Effects – way questions are worded may change your results How accurate would a survey be about the frequency of diarrhea? Correlational Method • Correlation - expresses a relationship between two variables. – • Can be efficient • Can make • Can use pre-existing or archival data • Make it difficult to assess the impact of a third variable As more ice cream is eaten, more people are murdered. Does ice cream cause murder, or murder cause people to eat ice cream? Results of Correlational Studies Positive Correlation • The variables go in the Example: Negative Correlation • The variables go in Example: Correlation Coefficient • Correlation coefficient - A number that measures the relationship between 2 variables. • Range is from – The relationship gets the closer you get to zero. • Scatterplot – a visual representation of the relationship between the variables – shown as a graphed cluster of dots Which is a stronger correlation? • -.13 or +.38 • -.72 or +.59 • -.91 or +.04 Which of the following would be a negative correlation, and which would be a positive correlation? Education and years in jail Weight and hours of TV watched Education and income Holding babies and crying Food and calories ingested Correlation Correlation Correlation The table below lists the scores of eight research participants on a test to measure anxiety, as well as the typical number of cigarettes each person smokes daily. Scores on the anxiety test can range anywhere from a low of 0 (indicating very low anxiety) to a high of 30 (indicating very high anxiety). Research Participant 1 2 3 4 5 6 7 8 Anxiety Cigarettes Test Score____________ 8 11 9 3 15 11 14 16 21 26 12 10 22 24 17 18 Construct a scatterplot to represent the correlation between smoking and anxiety. Describe the direction of the correlation and give two possible explanations for it. Correlation Illusory Correlations • Illusory Correlation – –A random coincidence • Speaking at a college graduation ceremony, Professor Robson compared college graduates with adults who are less educated. She correctly noted that college graduates pay more taxes, vote more frequently, engage in more volunteer activities in their communities, and are less likely to go to jail than less-educated adults. The professor concluded that colleges obviously do great things for society. How might you reasonably challenge the way the professor reached her conclusion? Experimental Method • Experiment - Manipulation of one or more variables • Advantages: • Can verify results – • Can eliminate bias • Shows • Disadvantages: Smoking causes health issues. Theory • Theory - • Example: – My hunch: • A hunch Hypothesis • Hypothesis - A testable prediction. • Example: Operational Definitions • Operational Definition - • Example: A statement that tells a person clearly the • Meaning of • measure of a • Must be clear and precise • Must be • Allow Independent Variable • Independent Variable Whatever is being manipulated in the experiment. If there is a drug in an experiment, the drug is almost always the independent variable. Example: Dependent Variable • Dependent Variable Example: • It is dependent on the independent variable. The dependent variable would be the effect of the drug. Beware of Confounding Variables • Confounding variables A factor other than the independent variable that might produce an effect on the experiment • Confounds often arise due to differences between the groups that exist before the independent variable is imposed! Examples: (We’ll talk about the last two later) . Assignment • Random Assignment • Examples: – Assigning participants in an experiment to experimental and Experimental control groups by chance Control group – – Minimizes differences between two groups – Different than Random Sample – Reduces the impact of confounding variables • Experimental Group – • Control Group – 2 other confounding variables • Experimenter Bias - expectations by the experimenter that are subtly communicated to the participants – Example: • Placebo effect – an experimental effect caused by expectations of participants or caused by a substance which the recipient assumes is the independent variable but is not – Example: Double-blind Procedure • Double-blind - • Minimizes placebo effect and experimenter bias Replication • Replication Repeating the research study to see whether the basic finding extends to other participants and circumstances. • What helps the researcher to insure the study can be replicated? Quasi-experimental • Quasi-experimental No random assignment • Used to study differences between: • Confounding variables so no cause and effect Controlled Observation • Controlled Observation - Type of Observational Research – Conditions are contrived by researcher – Independent and dependent variable – Does not show cause and effect • Early Psych Research Statistics • Recording the results from our studies. • Must use a common language so we all know what we are talking about. Descriptive Statistics • Descriptive Statistics numbers that summarize a set of data obtained from a sample – describes sets of data. • Examples: Types of Data • Nominal Data – identifies categories • Ordinal Data – identifies the order in which data falls in a set • Interval data – data that falls within a number line with a zero starting point • Ratio data – data that falls in a number line where zero is just another number • Examples: Measures of Central Tendency • Mode • Mean • Median - Central Tendency • Mean, Median and Mode. Let’s look at the salaries of the employees at Dunder Mifflen Paper in Scranton: $25,000-Pam $25,000- Kevin $25,000- Angela $100,000- Andy $100,000- Dwight $200,000- Jim $300,000- Michael The median salary looks good at $_______________________ The mean salary also looks good at about $________________ But the mode salary is only $___________________________ Watch out for extreme scores or outliers. is a better measure than the when there are extremes Normal Distribution • In a normal distribution, the are all the same. Examples: Height, Weight, IQ Scores Distributions • Outliers skew distributions. • If group has one high score (contains more low scores) • If a group has a low outlier, the curve has a (contains more high If most students scored well on a test, what would the distribution look scores) like? If most students scored poorly? What does the data tell us? Measures of variability • Range: distance from highest to lowest scores. • Standard Deviation: the variance of scores around the mean. • The higher the variance or SD, • Do scientists want a big or small SD? • Variance - The average of the differences from the mean – another measure of how the data is distributed around the mean Shaq and Kobe may both score 30 ppg (same mean). But their SDs are very different…meaning? Calculating Standard Deviation Step 1 – calculate the mean – add all of the raw scores and divide by the # of scores Step 2 – calculate the deviation from the mean by subtracting each of the raw scores from the mean Step 3 – square the deviation from the mean for each score • Step 4 – Sum the squared deviations • Step 5 – divide the sum of the squared deviation by the number of scores and find the square root Calculating the Standard Deviation Calculating the Standard Deviation your turn Scores – 10, 3, 7, 8, 7 Step 1 – calculate the mean – add the all of the raw scores and divide by the # of scores Step 2 – calculate the deviation from the mean by subtracting each of the raw scores from the mean • Step 3 – square the deviation from the mean for each score • Step 4 – Sum the squared deviations • Step 5 – divide the sum of the squared deviation by the number of scores and find the square root Variance • The average of the squared differences from the Mean. • = Standard Deviation2 • Example: Standard Deviation = 5 Variance = • *if you know the variance, how can you calculate the standard deviation? Scores • A unit that measures the distance of a score from the mean in units of standard deviations • Observation – Mean Standard Deviation • 10-15 = -1 5 • Equals 0 at the mean • A positive z score = Example: If John scored a 72 on a test • with a mean of 80 and a standard deviation of 8, John’s z score would be negative z score = Normal Distribution What is the probability an observation is less than the z score or more than the z score? Normal Distribution Calculating the % of the observed Step 1 – calculate the mean Step 2 - calculate/find the standard deviation or variance. If you only have the variance you must calculate the standard deviation Step 3 draw a normal distribution curve and find the scores for each standard deviation from the mean and place them on the graph Step 4: calculating the % of students who scored within a range of scores by finding the corresponding scores on the curve, then add the percentages from each standard deviation. Inferential Statistics • Inferential Statistics - The purpose is to discover whether the finding can be • Statistical Significance – the observed difference between the means of the experimental and control group are not due to • Measured by P-value = – 5% likely the results are due to chance or – 95% confidence level the results are due to the independent variable – You can apply the findings to the population • What does a p-value= .80 mean? Statistical Significance Key Ideas • The bigger the difference between groups the less likely it is that it's due to chance. • The larger the sample size (number of patients) the more likely it is that the observed difference is close to the actual difference. This is an example of the "law of large numbers." – Ie. The smaller the real difference is, the more patients you need to be likely to detect a statistically significant difference in a clinical trial. The larger the real difference is, the fewer patients you need to be likely to be detect a statistically significant difference in an actual clinical trial. • The larger the sample size, the smaller an observed difference has to be in order to be statistically significant. • The smaller the sample size, the larger an observed difference would have to be in order to be statistically significant. APA Ethical Guidelines for Research • IRB• Both for humans and animals. Animal Research • Clear purpose • • Acquire animals legally • Least amount of Human Research • • • • C No I D Milgram’s Experiment Milgram’s Experiment • Obedience High • Obedience Lower