exam 1

Hypothesis

• Simply, put an educated guess about a relationship
• Should be based on theory
• Includes independent and dependant variables
• Directionality

Variables

• Discrete Variable
• Variables with a fixed number of values
• Continuous Variable
• Variables with an infinite range of values
• Independent Variable
• Variable that influences an outcome
• Dependant Variable
• Variable that is influenced by an independent variable

Levels of Measurement

• Nominal
• Ordinal
• Interval
• Ratio

Nominal

• 
• Caption: What's your gender?
• Uses numbers to represent names
• Numbers are not representative of value
• Exhaustive and mutually exclusive

Ordinal

• 
• Uses numbers to convey rank
• Most common level of measurement

Rato vs Interval

• Ratio data has an absolute zero point
• Ratio examples: age, net worth
• Distance (interval) between numbers is meaningful and consistent
• Interval examples: temperature, IQ

Experiment Quasi- experiment

• Two groups to be studied: control and experimental
• Experimental group receives independent variable
• No control group - comparison group

Experiment vs Quasi-Experiment example

• In a study of prison safety, researchers want to know if providing mandatory inmate counseling will reduce inmate violence
• Experiment - 100 inmates are randomly selected to participate, 50 are randomly assigned to receive counseling (experimental), 50 do (not control)
• Quasi-experiment - 50 inmates sign up to receive mandatory counseling (experimental), and 50 similar inmates do not (comparison)

Descriptive Stats Inferential Stats

• Summarize raw data
• Describe how people responded
• Represent building blocks for subsequent analyses
• Test hypotheses
• Compare variables
• Determine cause and effect
• Aid decision making

Proportions and Percentages

• Proportion = frequency/total number or p = f/n
• Percentage = (100) frequency/total number or % = (100) f/n

Calculating Missing Data

• 
• Valid percentage uses valid N as the denominator
• (N) total number minus (M) missing observations
• Valid % = (100) frequency/total number - missing number
• Caption: what's your fav color freq chart ex

Pie Chart

• Good for nominal and ordinal data
• Quickly determine 100% is shown
• Intuitively display
• Easy to interpret
• 
• Caption: pie chart in spss

Bar chart

• 
• How to Create a Bar Chart in SPSS - Bar Graph (Embed)

Histograms

• Ordinal and interval data
• Extension of the bar chart
• non-cumulative/cumulative
• 

Skewness

• Symmetry
• 

Kurtosis (peakness)

What is the difference?

• Skewness differentiates extreme values in one versus the other tail, kurtosis measures extreme values in either tail.

line chart

• X-axis usually depicts time
• Single or multiple variables
• Comparison of variables
• 

Mode

• The most frequent value in a distribution
• unimodal/bimodal
• If there is one highest value, the distribution is unimodal; if two, the distribution is bimodal

Median

• The median is the score that divides the frequency distribution in half.

The mean

• Most common measure of central tendency
• Ordinal and interval
• Used in most other statistical procedures
• *Add up all the scores and divide by the number of scores*

Data vs. Variable View in SPSS

• Both windows in the Data Editor display aspects of the contents of a data file.
• In Data View, the rows represent cases (e.g., the scores for each participant in a research study) and the columns represent variables (i.e., the things we want to measure)
• In Variable View, the rows represent variables and the columns represent the attributes of each variable.
• Data View contains the data that will be analyzed and Variable View contains information about the variables themselves.

Variable View vs. Data View in SPSS