 There are times when an experiment cannot be carried out, but researchers would like to understand possible relationships in the data. Data is collected and if the data indicates a relationship this is known as correlational data.

Positive Correlation Negative Correlation  A positive correlation is when both variables are affected the came way. As x increases, y increases. For example, the more hours you spend studying, the better you do on a test or the less time you spend studying, the less well you do on a test. Also called a direct correlation.  A negative correlation means that as one variable increases the other one decreases. For example, as the number of hours spent watching TV increases, your scores on the test will decrease. Also called an inverse correlation.

 You cannot determine a cause and effect relationship because no independent variable was manipulated. For instance, if a researcher wanted to study the impact of TV on aggression, the researcher could study the number of hours a child watched TV and the child’s level of aggression. This would be difficult as an experiment, because of ethical concerns.

 If the researcher finds that as TV viewing increases so does aggressive behavior, this would be a positive correlation.  However, it would not be possible to determine definitely if the TV viewing caused the aggressive behavior or if aggressive behavior led to more TV viewing. This is called bidirectional ambiguity.

 Although, correlation does not show cause and effect, it can help predict how behaviors are connected. The correlation coefficient is a statistical index of the relationship between two things.  The correlation coefficient can help a researcher to predict how connected two behaviors actually are.

 Scatterplots are graphed clusters of dots, each of which represents the values of two variables. The slope of the points suggests the direction of the relationship between the variables. The amount of scatter suggests the strength of the correlation.

 Remember: Correlations indicates the possibility of a cause and effect relationship but it does not prove causation.  Illusory correlation is the perception of a relationship where one does not exist. We are more likely to perceive a relationship if we already believe that the two variables are connected. We are prone to perceiving patterns even if there is no pattern.

 Central tendency: a single score that represents the whole set of scores  Mode: the most frequently occurring score(s) in a distribution  Mean: the arithmetic average of a distribution, add all scores together and divide by the number of scores  Median: the middle score in a distribution, half the scores are above it and half are below it

 These measures are ways to neatly summarize data but can be misleading or skewed. The mean is easily skewed by a few scores to either extreme end of the scale. You should always be aware of which measure was used and remember that the mean could be distorted by a few atypical scores.

 Although a single measure of central tendency can tell us something about the data, that single figure leaves out other valuable information. It is helpful to know about the variation in the data.  The range is the difference between the highest and lowest number in the distribution. Range however is only a crude estimate of variation, because a few extreme scores can distort the range.

 Standard Deviation is a computed measure of how much scores vary around the mean score  The standard deviation is a more useful tool to determine how much scores deviate from each other and whether they are packed in closely or spread apart.  Standard deviation can be shown in a curve. A normal SD will present in a bell curve or a normal curve.

 When is a difference significant?  If averages from two samples are reliable measures of their population, the difference is said to also be reliable.  If the difference between two samples is large and the difference is reliable, it is said to that the difference has statistical significance.

 Interview: The research interview is a common way to collect data.  Issues: interviewer effects—actions of the interviewer that interfere with the process  Participant bias—participants change their answers to what the believe the interviewer desires  Social desirability bias—participants change answers to present the most positive picture of themselves

 Structured: interview schedule dictates which questions should be asked and in what order. Very controlled  Unstructured: topic and time are specified. May reveal more data but the data is more difficult to analyze  Semi-structured: looks and feels more like an informal conversation but follows a schedule. Allows respondents to more freely answer but is still a set of questions that maintain the focus