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This research analysis delves into the concept of self-fulfilling prophecies, where researchers' expectations can inadvertently shape their behavior and influence participant outcomes. It highlights the importance of utilizing double-blind and single-blind experimental designs to mitigate biases. The discussion includes fundamental statistical concepts such as descriptive statistics, frequency distributions, and measures of central tendency like mean, median, and mode, as well as the understanding of standard deviation and correlation coefficients to describe relationships between variables.
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Self- fulfilling prophecy • When a researcher’s expectations influence that person’s own behavior, thus influencing the participant’s behavior • Non-verbal cues, body language
How to avoid self-fulfilling prophecy • Double blind experiment • Neither participants or experimenter know who is receiving the independent variable • Single blind • The participants do not know who is receiving the independent variable • Placebo effect- a change in a participant’s behavior that results from a belief that he/she is receiving treatment (independent variable
Explaining the research…statistics • Descriptive statistics- organizing data in a practical and efficient way • Frequency distribution- organizing data so we know how OFTEN something occurs • Frequency polygon- shows the shape of data distribution • Normal Curve-symmetrical, bell shaped curve
Central Tendency • Measure of Central tendency- a number that describes something about the average score of a distribution • Mean- average • Median-middle number • Mode-most often • Standard deviation- measure of variability, how far away from “normal” is it?
Correlation Coefficient • Shows direction and strength of a relationship between two sets of variables • Positive correlation + as one variable increases, the second variable increases • Negative correlation – as one variable increases, the second variable decreases
