Psychology by Gray and Bjorklund (7th edition) - a summary
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Psychology
Chapter 2
Methods of psychology
In psychology, the data are usually measures or descriptions of some form of behaviour produces by humans or other animals.
A fact (or observation) is an objective statement, usually based on direct observation, that reasonable observers agree is true. In psychology, facts are usually particular behaviours, or reliable patterns of behaviours, for persons or animals.
A theory is an idea, or conceptual model, that is designed to explain existing facts and make predictions about new facts that might be discovered.
Any prediction about new facts that is made from a theory is called a hypothesis.
Facts lead to theories, which leads to hypothesis, which are tested by experiments, which leads to new fact. It is a cycle of science.
Each of this dimensions can vary form the others, resulting in any possible combination.
Research design
Researches design a study to test a hypothesis, choosing the design that best fits the conditions the researcher wants to control.
Also in three basic types.
Setting
In two basic types
3 Data-collection method
Two basic types
Caution! Sometimes subjects know that they’re being watched. Does this knowledge affect how they behave?
Hawthorne effect:
The subjects’ knowledge that they’re being watched and their belief that they are receiving special treatment includes their behaviour.
One technique to minimalize the Hawthorne effect is habituation. A decline in response when a stimulus is repeatedly or continuously present. Over time, subjects may habituate to the presence of the researcher and go about their daily activities more naturally than they would have if suddenly placed under observation.
Those are divided in two categories.
Descriptive Statistics
Describing a set of scores
The mean: The arithmetic average. Determined by adding the scores and dividing the sum by the number of scores.
The median: the centre score. Determined by ranking the scores from highest to lowest and finding the score that had the same number of scores above it as below it.
The variability: the degree to which the numbers in the set differ from one another and from the mean. Close to the mean is low variability and widely differ is a high variability. A common measure of variability is the standard deviation.
Describing a correlation
The strength and direction of the relationship of a correlation can be assessed by a statistic called the correlation coefficient. This is a ranking form -1.00 to +1.00. The – and + indicates the direction of the correlation. Positive or negative.
Positive: an increase of one variable coincides with a tendency for the other variable to increase.
Negative: An increase in one variable coincides with a tendency for the other variable to decrease.
The absolute value of the correlation coefficient indicates the strength of the correlation.
A correlation close to 0 means that the two variables are statistically unrelated.
To visualise the relationship between two variables, researchers might produce a scatter plot.
Inferential statistics
Inferential statistics are necessary because any set of data collected in a research study contains some degree of variability that can be attributed to chance. There are random effects caused by uncontrollable variables.
Given that results can vary as a result of chance, how confident can a researcher be in inferring a general conclusion for the study’s data?
Inferential statistics are ways of answering that question using the laws of probability.
Statistical Significance
P is for probability (or level of significance).
Then two means are being compared, p is the probability that a difference as great as or greater than that observed would occur by chance, in the larger population, there were no difference between the two means.
P is the probability that a difference would occur if the independent variable had no real effect on the scores.
By convention the results are usually labelled as statistically significant if the value of p is less than 0,05 (5 percent).
Components of a test of statistical significance
The elements that go in calculations are:
In short:
A large observed effect, a large number of observations, and a small degree of variability in scores within groups all reduce the likelihood of the effect is due chance (and increase the likelihood that a difference between two means, or a correlation between two variables, will be statistical significant).
Statistical significance tells us that a result probably did not come about by chance, but does not, by itself, tell us that the result has practical value.
Good scientist strive to minimize bias in their research.
Bias is non-random effects causes by some factor (or factors) extraneous to the research hypothesis.
Bias is a serious problem in research because statistical techniques cannot identify it or correct for it. Whereas error only reduces the chance that researchers will find statistically significant results.
Bias can lead to false conclusion.
Three types of bias are:
Avoiding biased samples
It has to do which the way individuals are studied or selected or assigned in groups.
If the members of a particular group are initially different from those of another group in some systematic way (or different from the larger population the researcher is interested in) the group is a biased sample. The group is no longer representative.
Reliability and validity of measurements
Reliability
Reliability has to do whit measurement of error, not bias.
Validity
A lack of validity can be a source of bias. It can lead to false conclusions.
Avoiding biases from observers’ and subjects’ expectancies
Research with humans
In research with humans, ethical considerations revolve around three interrelated issues:
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This is a summary of Psychology by Gray and Bjorklund. This book is an introduction to psychology and is used in the course 'Introduction to psychology' in the first year of the study Psychology at the UvA.
The first four chapters of this summary are for free, but to
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