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Chapter 2 Review A) Experimental Methods : involves a control group, and an experimental group. Only considered scientific if conclusions can be verified or refuted 1. Testable Hypothesis - to be testable, a hypothesis must be formulate precisely. - The variable under study must be clearly defined. Researchers achieve these clear formulations by providing operational definitions of the relevant variables. 2. Independent variable - Variable that experimenter manipulates 3. Dependent variable - Variable that is measured by the experimenter 4. Control group - Group that does not receive the treatment 5. Experimental group - group that receives the treatment 6. Operational definitions - Description of your variables. Example: dependent variable may be academic performance. You would need to define how you will measure academic performance 7. Participants - Subjects who are participating in your study 8. Steps in designing an experiment (See slide in power point) 9. Extraneous - Undesired variables that affect the results of an experiment 10. Confounding variable - Inconsistencies between the experimental & control group that skew the results. - Because of confounding variable you are now unsure on what affected the results…was in the confounding or independent variable? 11. Random sample - You must insure that your group of participants represents your larger population 12. Random assignment - Randomly assign participants to control group and experimental group 13. Random selection - Ensures everyone an equal opportunity in being selected for an experiment 14. Strengths and weaknesses - Strengths: See all strengths for each research method in power point - B) 1. 2. 3. Weakness: See all weaknesses for each research method in power point Description/Correlational Methods Naturalistic observation - Observing subjects in their natural habitats/no interaction b/w researcher and subjects Case study - An in depth study of one person, thing, event or topic - Strength: can provide valuable insight on an issue/condition - Weakness: findings can rarely be generalized to a population Surveys - Used to research the selfported attitudes of behavior of people - Questionnaire/survey/inte rview] - Strength: can be generalized to a larger population (if sample is random) - Weaknesses: Social desirability bias - Individuals tend to answer questions in a way that is socially acceptable. - Example: How many hours of TV do you watch? You answer 5 per week when - you really watch 30 hrs worth but you don’t want to seem like a couch potato Another weakness: Response set: the tendency to exhibit a particular pattern of response independent of the question being asked. Example: Christmas tree the answers - - 4. 5. 6. 7. When you answer all the questions without actually reading the questions Example: answer all of the questions with a 5 if you were asked to rank from 11 to 5. Or Christmas treeing the answers Statistics: descriptive & inferential - Descriptive: organizing and summarizing data - Inferential: interpreting data and drawing conclusions – use of probability Measures of central tendency a. Mean: average b. Median: number in the middle c. Mode: number that is seen the most Standard deviation - measure that is most commonly used to describe variability in data distributions Positive correlation - Variables both go in the same direction. - The closer to +1 the stronger the correlation - Example: attendance increases…GPA increases 8. Negative correlation - Variables go in opposite direction - The closer to -1 the stronger the correlation Example: exercise increases…weight decrease 9. Statistical significance - If the results from the experiment are minimal but are NOT by chance! They are STATISTICALLY SIGNIFICANT C) Miscellaneous 1. Sampling bias - Must have a sample that represents the population that you are conducting research on 2. Placebo effects 3. Scatter plots - Used to graph the correlation between two measures obtained on a group of individuals - The more the data forms a line, the stronger the correlation - The slope of the line suggests a positive correlation 4. Experimenter bias - Can be avoided with double-blind 5. frequency polygon a line figure used to present data from a frequency distribution 6. histogram - a bar graph that represents data from a frequency distribution - used to summarize statistical data 7. Double blind - Both the experimenter and the participants are unaware of the testable hypothesis or what is being tested 8. Quasi-experiment - An experiment without one of the factors involved in an actual experiment 9. Single blind experiment - When the participants are unaware of what is being tested 10. Ethics - Must let the participants know what they are signing up for - Must debrief participants - Must have participants provide written consent to share results if you want to share them - suggested rules for acting responsibly and morally when conducting research or in clinical practice - guidelines by the APA prevent unnecessary deception to humans and animals, and to protect confidentiality 11. Cross sectional study - Investigate participants from various ages and compare them 12. Longitudinal study - Investigate participants over a long period of time - Cohort-effect: occur when differences b/w ages occur b/c they group up @ different times -