Variability in research proposition two
- Research questions in all behavioral sciences are questions about behavioral Variability.
Ex: what is the divorce rate? Vs. Is the divorce rate higher among people younger than 30 compared to those older than 30? Or did the divorce rate significantly changed from 1980 to 2010?
Variability and research proposition one
- Psychology and other behavioral sciences involve the study of behavior variability.
- behavior varies across situations, among individuals, and overtime.
- behavioral scientists try to understand how and why behavior varies
Variability and research proposition three
- Research should be designed in a manner that best allows the researcher to answer questions about behavioral variability.
Ex: is there a relationship between eating dinner together as a family and children’s academic achievement? What varies?
Variability in research proposition four
- The measurement of behavior involves the assessment of behavioral variability.
- we measure behavior by assigning numbers to behaviors such that the variability in the numbers reflects the variability in the behavior.
Variability and research proposition five
- Statistical analysis are used to describe an account for the observed variability in the behavioral data.
- Will you statistics to answer questions about the variability in the data. How much variability is there? What is it related to? What caused it?
What are two types of statistics?
- Descriptive statistics-used to summarize and describe the behavior of participants in a study.
- inferential statistics-used to draw conclusions about the reliability and generalizability of one’s findings
What is the variance?
- Statistic used to indicate the amount of variability and participants responses
- you would calculate The variance to see how much the six participants responses vary
What is the range?
• The difference between the highest and lowest scores
Disadvantage of the range
- Two sets of data could be very different but still have the same range
- these two data sets have the same range, yet the variability in the scores is very different.
Statistical explanation of variance: step 1
- Calculate the mean(some the scores and divide by the number of scores you have)
- 4+1 +2+2+4+3 = 16
- 16/6=2.67
What is the mean?
The sum of a set of scores divided by the number of scores
- we assess the variability in a set of data by seeing how much the scores vary around the mean
- if the scores are tightly clustered around the mean, then the variance of the data will be small.
- if the scores are more spread out from the main, then the variance will be longer.
Variance
• We express the variability of the data using all the scores using a statistic called…
•Variance (square of the standard deviation).
s2=£(Yi-Ymean)2/(n-1)
Statistical explanation of variance step 2
- Calculate a deviation score-how much each score differs from the mean
- A positive deviation score indicates that the participants response fell above the mean.
- A negative deviation score indicates that the participants response fell below the mean
Statistical explanation of variance step 3
- Square each deviation score. This will eliminate any negative values.
Statistical explanation of variance step 4
- Calculate the total sum of squares-The sum of the squared deviations of the scores from the mean
- 77+2.79+0.45+0.45+1.77+0.11=7.34
Statistical explanation of variance step 5
- Divide the total sum of squares by n-1.
•n= The number of participants in the sample
7.34/6-1=1.47
-The variance for this data set is 1.47
(Systematic variance)
Total variance=systematic variance+error variance
The portion of the total variability in participants scores that is related in an orderly, predictable fashion to the variables the researcher is investigating.
(Error variance) Total variance = systematic variance + error variance
• The portion of the total variance in participants scores that is unrelated to the variables under investigation in the study; variants that remains unaccounted for (not necessarily an error and measurement)
Distinguishing systematic from error variance
• Researchers use statistical analysis to partition the total variance of their data into the systematic and error components.
• The more error variance in the data, the more difficult it is to determine whether the variables of interest are
related to variability in behavior.
• researchers try to minimize error variance as much as possible in order to detect the systematic variance in the data.
What is effect size?
- is a measure of the strengths of the relationship between two variables.
- effect size indicates the proportion of the variance.
- because the effect size is a proportion, it is easy to compare effect sizes.
Effect size example
- we could calculate the proportion of the the total variance in peoples ratings of how upset they are about losing their job that is systematic for finding a new one
- when the systematic variance is .00- there is no relationship between being upset and expectations for suture work
- when the systematic variance is 1.00- there is a perfect relationship between the two
