Unit 1 Flashcards
Scientific Foundation of Psychology
Confidentiality
No data can be traced back to a single experiment
Debriefing
Inform participants of true nature of the study when it is over
Protection From Risks
Must be informed if there are known risks
Right to Withdraw
Can leave right away, no questions asked
Justification
Deception (telling the participant they are measuring one thing, when really, they are measuring another) must be justified
Informed Consent
Participants decide to participate after study is explained
Humanitarian
People come first; well-being outweighs science
APA
American Psychological Association- founded in 1892. Contains the IRB (Institutional Review Board) and is made of 53 divisions representing specific areas. It works to advance the science and profession of psychology concerning both humans and animals
Animals Research- the 3 R’s and the IACUC
IACUC: Institutional Animal Care and Use Committee.
Replacement: Animals should be replaced with invertebrates when possible.
Refinement: Regulations should minimize harm; appropriate anesthesia used.
Reduction: Number of animals minimized.
Type I and Type II Error
Type I: A false positive. When an investigator rejects a null that is true- researcher says that their hypothesis is true when it’s not.
Type II: A false negative. When an investigator fails to reject a null that is actually false- researcher says there is no link when there is.
Null Hypothesis- Reject and Fail to Reject
A general statement that there is no relationship between 2+ variables. The commonly accepted hypothesis.
Fail to reject the null: assumes that the null is true.
Reject the null: assumes that the alternative research hypothesis is true through testing and retesting
Meta-analysis
The statistical combination of the results of multiple studies addressing a similar research question
Statistical Significance
The purpose is to discover whether the finding can be applied to the larger population from which the sample was collected
T-Test - ANOVA
Examines 2 groups and decides if the data is significant.
ANOVA: a specific T-test that can look at 2+ groups
P-Value
0.5% statistical significance. 5% likely that the results are just due to chance. 95% likely that the results are accurate. Measuring the height of 500 students. Majority of students would not be extremely short or tall. If the probability that results are due to chance is less than 5% (0.5) they are confident their results were not due to chance
Z-Score
A unit that measures the distance of one score from the mean.
Positive: a number above the mean
Negative: a number below the mean
Calculation: your score minus the mean score divided by the SD
Percentile Score
How your score compares to the rest of the population- how far it is from 0. The median is the 50th percentile- where 50% lie below and 50% lie above. You want to be in a higher percentile.
Range
The gap between the lowest and highest score- subtract the lowest score in the data from the highest score
Variance
How spread out the scores are from one another
Skewed Distributions- Positive and Negative
If one of its tails is longer than the other it contains outliers.
Positive: long tail in the positive direction- contains more low scores
Negative: long tail in the negative deirection- contains more high scores
Standard Deviation
A measure of viability that indicates the average distance between the scores and the mean.
Low: data points are very close to the mean
High: data points are spread out over a large range of values.
Scores above mean: positive deviation
Scores below mean: negative deviation
Larger deviation = spread out scores
Normal Distribution
Means there is no skew. A frequency distribution shaped like a symmetrical bell-shaped curve- normal distribution. Can measure variables such as height, weight, and IQ. Can divide the curve into sections and predict how much of the curve falls within each section
Measures of Central Tendency- mean, median, and mode (and bimodal)
Measures of central tendency: a number that describes something about the “average” score of a distribution.
Mean: the average score- add together, divide by number of total scores
Median: the middle score- midpoint of a set of values
Mode: the most frequent score- graphed in a frequency distribution (more than 1 = bimodal)
Inferential Statistics
What can you infer or assume about the data?
Descriptive Statistics
Statistics: a branch of mathematics, helps categorize information, makes inferences.
Descriptive: what is the data showing?
Bar graphs with no spaces between bars. Height of bars indicates frequency of a group of scores.
Falsifiability
Must be present in all theories- the possiblitiy that an assertion can be shown false. Not meaning the results are false, but that the experiments can be shown as false. This eliminates other factors and means the experiment is testable.
Demand Characterstics
Participants go into an experiment and “figure out” what the researcher is trying to study. May subconsciously change their behavior to fit that interpretation.
Hindsight Bias
Tendency to believe, after learning the outcome, you knew all along
Hawthorne Effect
Some people may work harder and perform better when they know they are in an experiment. Some people may change their behavior due to attention from the researcher rather than the manipulation of the IV
Generalizability of Results
When analyzing results, results from sample population are applied to the greater population
Replication
When analyzing results, are the same results obtained? If so, retest more, could eventually be considered a theory
Steps to the Scientific Method
Scientific Method: an approach to gathering info and answering questions. Errors and biases are minimized. 1. Make an observation. 2. Ask a question. 3. Form a hypothesis or testable explanation. 4. Make a prediction based on the hypothesis. 5. Test the prediction
Applied Research
Scientific inquiry that focuses on developing practical solutions to real-world problems
Basic Research
Scientific research that aims to increase knowledge and understanding about the natural world without having any practical or immediate applications
Illusory Correlation
Myths and legends in statistics. Seeing what you want to see based on your own interpretations. For example, the more your knee hurts, the more likely it is to rain.
Cross-Sectional Study
Method in which data is collected from groups of participants of different ages and compared so that conclusions can be drawn about differences due to age-less expensive and time consuming than longitudinal studies. For example, a 5 year old, a 4 year old, and a 3 year old. Data only collected once.
Longitudinal Study
Method in which data is collected about a group of participants over a number of years to assess how certain characterstics change or remain the same during development. Very time consuming. Participants may disappear mid-study.
Order Effects, Social Desirability Bias, Non-Response Bias
Order Effects: positioning of questions in a survey may influence the outcome. Participants may start to guess what the experiment is studying.
Social Desirability Bias: want to appear in a good light to researcher
Non-Response Bias: does not respond to certain questions in the survey if they are uncomfortable or don’t know
Survey
Information is obtained by asking many individuals a fixed set of questions. Most practical way to gather data on large numbers of people. May include interviews and questionnaires.
Naturalistic Observation
Psychologists observe the subject in a natural setting without interfering- scientist conceals himself
Ex Post Facto
Research based on pre-existing condition. A non-experiment design
Correlation Coefficient
Describes the direction (positive or negative) and the strength (+1 or -1) of the relationship between 2 sets of variables.
Pearson Correlation (r): the extent to which the correlation is in a straight line on a scatterplot. r near +1 or -1 is a strong correlation. The closer r is to 0, the weaker the relationship.
