AP PSYCH 1.5 Statistical Analysis in Psychology

Statistical Analysis

Once researchers have conducted their studies, they must summarize, organize, interpret, and analyze their data
This comes before drawing any conclusions
This is the premise behind statistics

Researchers often start with quantitative data
- This deals with raw numbers
Qualitative data is descriptive
- The data is investigative and often open-ended

Organizing and describing data is descriptive statistics
- There are several steps and tools used such as frequency charts and graphs
When making predictions about data, researches use inferential statistics
- Researchers can predict or generalize how their data and the independent variable relates to a larger population

Using a frequency distribution table, researchers can see how often a certain data point occurs

Discrete Data: Data that can be counted
- Nominal Scale: Data without structure or order
- Ordinal Scale: Count and order but not measurable
Continuous Data: Data that can be measured
- Interval Scale: Data with degrees of difference but no ratio between them
- Ratio Scale: Data that processes a meaningful measurement with a zero value
Dichotomy Scale: Having two categories when organizing data
Trichotomy Scale: Having three or more categories

When using central tendency, researchers are identifying an estimated “center” of the data distribution
Mean is the average of the data set
Mode is the most frequent data point
Median is found at the exact middle of the data set
- This is found by counting inwards from the top and bottom
- If there are two ‘middle numbers,’ the two are averaged

Central tendency provides a snapshot of the data but does not reveal how the data is dispersed
Range and standard deviation allow researchers to understand the variation between data points
Range is the difference between the highest and lowest value point
- This only identifies the distance between the extremes
- It does not reveal distance from the mean, nor any other value point
Standard deviation allows researchers to indicate the average difference from the mean for a set of scores
- The higher the standard deviation, the less similar the scores

A symmetrical distribution is produced when a large group of people’s variables are tested such as intelligence, shoe size, height, etc.
- This symmetrical distribution is called a normal distribution or bell curve
- In order to achieve the perfect bell curve, the mode, median, and mean are at the 0-point value
Most data will not have a perfect distribution curve
- A positive skew occurs when scores pull the mean toward the higher end of the scores
- A negative skew occurs when scores pull the mean toward the lower end of the scores

Correlational studies do not imply causation
Correlational studies do, however, offer researchers the opportunity to determine the relationship between two variables

Scatter plots are used by researchers to understand the relationship between two variables
This strength of those two variables is called the correlation coefficient
- The closer the value is to +1.0 or -1.0, the stronger the relationship
A positive relationship between two variables can be 0 through +1.0
- This means the variables increase or decrease together
A negative relationship can range from 0 to -1.0
- This means one of the variables increases as the other decreases
No correlation means there is not a relationship between the variables

When drawing conclusions about data, researchers use inferential statistics
The likelihood that data collection was a result of intentional manipulation and not chance is called statistical significance
- Researchers are looking to establish a p-value
- The closer it is to 0, the more they can be sure that data supports their hypothesis and outside factors have not influenced their results