1. Research Process Parts of the research study Parts of the research study Aim: purpose of the study Aim: purpose of the study Target population: group whose behaviour is investigated Target population: group whose behaviour is investigated Procedure: step-by-step process used by researcher to carry out the study Procedure: step-by-step process used by researcher to carry out the study Findings: states how the researcher interpreted the data collected Findings: states how the researcher interpreted the data collected

2. Research Measurement and Sampling How to describe what we observe is the main question. How to describe what we observe is the main question. The use of ratio variables (measurements based on a continuous scale with an obvious zero point) The use of ratio variables (measurements based on a continuous scale with an obvious zero point) Researchers want measurements to be reliable and valid Researchers want measurements to be reliable and valid

3. Measurements Sampling and population concerns Sampling and population concerns Researchers must be concerned that a sample is representative of the population Researchers must be concerned that a sample is representative of the population Researchers will use random sampling and cross- sectional sampling for the best results (the difficulty lies in making the random sample representative) Researchers will use random sampling and cross- sectional sampling for the best results (the difficulty lies in making the random sample representative) Other problems with samples are: self-selected samples; convenience samples Other problems with samples are: self-selected samples; convenience samples Cross-sectional sampling is a deliberate selection of subjects to make the sample representative Cross-sectional sampling is a deliberate selection of subjects to make the sample representative Opportunity Sampling? Okay to use? What about for you as an IB student? Opportunity Sampling? Okay to use? What about for you as an IB student?

4. Research design Hypothesis Research method ObservationsVariables All of the aforementioned are part of the research design.

5. Pitfalls in Experimental Research Internal validity: maintaining consistent conditions in the experimental situation, and ensure no unwanted factors creep in Internal validity: maintaining consistent conditions in the experimental situation, and ensure no unwanted factors creep in Confounds: a situation where two variables change simultaneously making it impossible to determine their relative influence Confounds: a situation where two variables change simultaneously making it impossible to determine their relative influence Ways to avoid confounds Ways to avoid confounds 1. Hold constant factors which are not of direct interest 2. Use multiple independent variables when the variables involved are of direct interest

6. Pitfalls in Experimental Research Bias: systematic error Bias: systematic error Subject bias Subject bias Experimenter bias Experimenter bias These situations can be avoided by using single- blind and double-blind designs (deception) These situations can be avoided by using single- blind and double-blind designs (deception)

7. Observation to Interpretation Statistics Concerned with the description and interpretation of scientific data Concerned with the description and interpretation of scientific data Used to describe and summarize results Used to describe and summarize results Descriptive stats Descriptive stats Assist in understanding what the results mean Assist in understanding what the results mean Inferential stats Inferential stats

8. Descriptive Stats Frequency distribution Frequency distribution Rearranging the scores in order of size and then see how many people got each score Rearranging the scores in order of size and then see how many people got each score Can provide a clearer picture of what data looks like Can provide a clearer picture of what data looks like

9. Descriptive Stats Central Tendencies Central Tendencies Mode: most frequently occurring Mode: most frequently occurring Median: the middle of the frequency data Median: the middle of the frequency data Mean: sum of all scores divided by the number of scores Mean: sum of all scores divided by the number of scores Normal and skewed distributions Normal and skewed distributions Bell shaped curve is a normal distribution; highest point occurs in the middle of the distribution Bell shaped curve is a normal distribution; highest point occurs in the middle of the distribution Lopsided distribution is skewed, the mean is unlikely to be representative of the majority; in most cases, the researcher will prefer the median as a way of describing the typical result Lopsided distribution is skewed, the mean is unlikely to be representative of the majority; in most cases, the researcher will prefer the median as a way of describing the typical result

10. Measures of Variability Variability tells us how the scores are distributed around the center Variability tells us how the scores are distributed around the center One indicator is the range from lowest to highest One indicator is the range from lowest to highest The next indicator is to find the deviation scores The next indicator is to find the deviation scores Squaring the deviation scores will give the variance, however, this gives inflated data Squaring the deviation scores will give the variance, however, this gives inflated data Finding the square root of the mean of the variance will give the standard deviation Finding the square root of the mean of the variance will give the standard deviation

11. Standard Deviation The standard deviation will provide a measure of variability which reflects the position of every score within the group, expressed in the same units as the original scores. The standard deviation will provide a measure of variability which reflects the position of every score within the group, expressed in the same units as the original scores. The larger the standard deviation, the greater the variability of scores The larger the standard deviation, the greater the variability of scores

12. Normal Distributions What do they tell researchers? What do they tell researchers? Knowing something is distributed “normally” tells researchers specific things, that most scores are near the mean and that very few scores are away from the center (very little standard deviation) Knowing something is distributed “normally” tells researchers specific things, that most scores are near the mean and that very few scores are away from the center (very little standard deviation) (Look at page 449 in your handout, figure A.6) Knowing these properties of normal distributions becomes very useful in making predictions about scores and in our ability to interpret the results of research Knowing these properties of normal distributions becomes very useful in making predictions about scores and in our ability to interpret the results of research

13. Correlations Any relationships between variables are correlational and do not directly identify causal factors Any relationships between variables are correlational and do not directly identify causal factors Correlation or Causation Correlation or Causation Two types of correlational patterns Two types of correlational patterns 1. Positive: occurs when increases in one variable are associated with increases in the other variable 2. Negative: occurs when increases in one variable occur as the value of the other variable decreases (turn to page 453 in the handout, figure A.8)

14. Correlations Correlational patterns are measured using a statistical measure called a correlation coefficient Correlational patterns are measured using a statistical measure called a correlation coefficient This is a number between 0.0 and +1.0 for positive correlations; -1.0 and 0.0 for negative correlations This is a number between 0.0 and +1.0 for positive correlations; -1.0 and 0.0 for negative correlations As the value moves from 0 to the maximum the degree of the relationship between the variables becomes stronger As the value moves from 0 to the maximum the degree of the relationship between the variables becomes stronger

15. Inferential Stats Inference: a logical conclusion based on what I know “In using inferential stats, we try to generalize from our sample to the population.” (Glassman and Hadad, 2004)

16. Sampling and Variability Sampling and Variability Sampling and Variability The recognition that not all samples will be alike and that any sample may differ from the population is the result of sampling variability The recognition that not all samples will be alike and that any sample may differ from the population is the result of sampling variability Therefore, nothing is set in stone. All research has sampling variability, as a consequence, more research should be done with other samples to prove theory Therefore, nothing is set in stone. All research has sampling variability, as a consequence, more research should be done with other samples to prove theory There are many questions that arise when doing research, especially when a researcher is trying to interpret data. There are many questions that arise when doing research, especially when a researcher is trying to interpret data. “Inferential statistics are concerned with providing guidelines” for evaluation “Inferential statistics are concerned with providing guidelines” for evaluation

17. Inference with Normal Distributions The simplest situation for inference The simplest situation for inference Looking at a single score in relation to a set of data Looking at a single score in relation to a set of data

18. Significance Results which are interpreted as based on a real effect are referred to as significant. Results which are interpreted as based on a real effect are referred to as significant. The statistical tests for evaluating the chance versus the real effects are called significance tests The statistical tests for evaluating the chance versus the real effects are called significance tests “The conclusion one draws, expressed as the probability that the outcome is due to chance, is called the significance level of the results.” (Glassman and Hadad, 2004) “The conclusion one draws, expressed as the probability that the outcome is due to chance, is called the significance level of the results.” (Glassman and Hadad, 2004)

19. “Inferential stats use sample data to try to make inferences about a population which cannot be known directly.” (Glassman and Hadad, 2004)

20. Null Hypothesis The null hypothesis always asserts that only chance is at work The null hypothesis always asserts that only chance is at work If a researcher can prove the null hypothesis incorrect, it becomes more likely the researcher is likely correct If a researcher can prove the null hypothesis incorrect, it becomes more likely the researcher is likely correct Significance tests lead to probability, ergo statistical inference is always a matter of probabilities, never certainty. Significance tests lead to probability, ergo statistical inference is always a matter of probabilities, never certainty.

21. Standard of Probability Commonly accepted standard is 5 in 100 Commonly accepted standard is 5 in 100 5 chances of being wrong makes 95 chances of being right 5 chances of being wrong makes 95 chances of being right Researchers print this value as p<0.05 Researchers print this value as p<0.05

22. Errors in Evaluation of Hypotheses False positives (Type I) and false negatives (Type II) Experiment works but there is inconclusive data to support=false positive (Reject null hypothesis) Experiment works but there is inconclusive data to support=false positive (Reject null hypothesis) Experiment fails, but researcher overlooked genuine effects=false negative (accept null hypothesis) Experiment fails, but researcher overlooked genuine effects=false negative (accept null hypothesis)

23. There is no certainty is inferential stats. This is why there are no absolutes in science. For most psychologists, living with uncertainty is part of the challenge of understanding behavior.