Understand the basic concepts, theories, and techniques in the use of statistics in psychology;
Explore the historical roots of the use of statistical concepts in psychology;
Understand the basic concepts, theories, and techniques in using statistics in psychology.
Determine the major statistical techniques used in understanding human behavior;
Why Learn Statistics in Psychology?
It is crucial to be able to read psychology research articles.
It is crucial to conduct research yourself.
It develops your analytic and critical thinking.
Five Basic Concepts of Statistics:
Population
Sample
Parameter
Statistic (singular)
Variable
Statistics - a set of mathematical procedures for organizing, summarizing, and interpreting information.
Organize and Summarize the information so that the researcher can see what happened in the research study and can communicate the results to others;
Answer the questions that initiated the research by determining exactly what general conclusions are justified based on the specific results that were obtained.
OR
Statistics - refers to a set of mathematical procedures for organizing, summarizing, and interpreting information.
- Consists of facts and figures such as average income, crime rate, birth rate, average snowfall, and so on. (Collection, presentation, analysis, interpretation)
Populations and Samples
population: an entire group of people of interest.
sample: a subset (proportion) of the population.
Example: A bookstore wants to estimate the proportion of its customers who buy murder mysteries. A group of 76 customers is observed at the checkout counter and the number purchasing murder mysteries is recorded.
Population: all customers from that bookstore
Sample: 76 customers from that bookstore
Variables and Data
variable: characteristic or condition that changes or has different values for different individuals
data: measurements or observations
data set: a collection of measurements or observations
datum: single measurement or observation and is commonly called a score or raw score
Parameters and Statistics
parameter: value, usually numerical, that describes a population derived from measurements of the individuals in the population.
statistic: value, usually numerical, that describes a usually derived from measurements of the individuals in the sample.
Example: The average score on the R-score for a random sample of Quebec residents; ask yourself if this information comes from the whole population or a sample.
Descriptive vs. Inferential Statistics
Descriptive: describing and summarizing information from the sample or population.
Example: Twenty-seven percent of the rats in an experiment learned how to complete the maze in five trials
Inferential: using information from the sample to conclude.
Example: Due to a drought this summer, it is expected that the production of apples in Quebec will be reduced by 35% this year
Sampling Error: naturally occurring discrepancy, or error, between a sample statistic and the corresponding population parameter
Psychological Statistics Review Notes:
Statistics is a branch of mathematics that deals with the organization, analysis, and interpretation of groups of numbers. A set of mathematical procedures for organizing, summarizing, and interpreting information.
KEY TERMS
Population – set of all the individuals of interest in a particular study.
Parameter – a value - usually a numerical value that describes a population.
Sample – a set of individuals selected from a population, usually intended to represent the population in the research study.
Statistic – a value— usually a numerical value that describes a sample.
Variable – a characteristic or condition that changes or has different values for different individuals.
Values – possible number or category that a score can have.
Ex. 0-20 on a stress scale, male or female
Score – or raw score, is a particular person’s value on a variable.
Data (plural) – measurements observations.
Datum (singular) – a single measurement or observation, commonly called a score or raw score.
Data Set – a collection of measurements or observations.
Constructs and Operational Definitions
Constructs: internal attributes or characteristics that cannot be directly observed but are useful for describing and explaining behavior.
Example: intelligence, anxiety, and hunger
Operational Definition: identifies a measurement procedure for measuring an external behavior and uses the resulting measurements as a definition and a measurement of a hypothetical construct
Discrete and Continuous Variables
Discrete: countable in a finite amount (A discrete variable consists of separate, indivisible categories. No values can exist between two neighboring categories. Can assume a finite or countable finite number of values) -
Example: Gender, Nationality, Occupation
Continuous: would take forever to count, can take any value of an interval (Continuous variable, there are an infinite number of possible values that fall between any two observed values. A continuous variable is divisible into an infinite number of fractional parts. Cannot be counted because of their distinct division; - Considered abstract variables)
Example: Age, Weight, Height
Scales of Measurements
Nominal: categorical (words, letters, and alpha-numeric symbols)
Example: brand of laptop purchased; Female = F Male = M Transgender = T
Ordinal: order of possible answers (ranking)
Example: listing your five favorite NFL players in order; Student A with a grade of 98 = 1st Rank; Student B with a grade of 94 = 2nd Rank
Ratio: scale with a true zero
Example: ratio 10:1
Interval: meaningful difference between values- does not have a true 0 point - has negatives (distances between each interval on the scale).
Example: the daily temperature of a lake in the winter; Measurement level of stage fright of student A is 20 and 22, which is the same as student B’s 40 and 42.
Example: 6 people are randomly selected and asked how much money they have with them. The results are: 120, 240, 360, 480, and 600
Descriptive Research: measuring one or more separate variables for each individual with the intent of simply describing the individual variables
Example: A company studies the behavior of its customers to identify its target market before it launches a new product
Correlational Method: Two Different Variables are observed to determine whether there is a relationship between them demonstrating the existence of a relationship between two variables but does not explain the relationship.
Example: A study may examine the relationship between self-esteem and social anxiety.
Experimental Method
Experimental Method: one variable is manipulated while another variable is observed and participant (age, gender) and environmental (lighting, time of day) variables must be considered
Independent Variable: manipulated by the researcher
Dependent Variable: observed to assess the effect of the treatment
Control Condition: individuals do not receive experimental treatment
Experimental Condition: individuals do receive the experimental treatment
Non-Experimental Method
The Independent Variable that is used to create the different groups of scores is often called the quasi-independent variable.
Scores
X scores for a particular variable
X and Y when observations are made for two variables
N number of scores in a population•n: number of scores in a sample
Summation Notation
ΣX add all the scores for variable X