Introduction to Psychological Statistics

PSYCHOLOGY

Scientific study of human behavior and mental

processes

GOALS: Describe, Predict, Explain, and Control

STATISTICS

A branch of mathematics that deals with the

organization, analysis, and interpretation of group

of numbers

A set of mathematical procedures for organizing,

summarizing, and interpreting information

POPULATION

Set of all the individuals of interest in a particular

study

PARAMETER

A value– usually a numerical value– that describes

a population

SAMPLE

Set of individuals selected from a population,

usually intended to represent the population in a

research study

STATISTIC

A value– usually a numerical value– that describes

a sample

VARIABLE

Characteristic or condition that changes or has

different values for different individuals

VALUE

Possible number or a category that a score can

have

-

E.g 0–20 in a Stress Scale, Male or Female

SCORE

Or Raw Score, is a particular person’s value on a

variable

DATA

Are measurements of observation

Datum- single measurement or observation and is

commonly called a score or raw score

DATA SET

Collection of measurements or observations

DESCRIPTIVE STATISTICS

-Are statistical measures used to

summarize, organize, and simplify data

-Respondents: Quantitative

-Participant: Qualitative

INFERENTIAL STATISTICS

-Consist of techniques that allows us to study samples

and then make generalizations about populations from

which they were selected

-“The sample represents the population”

CONTINUOUS VARIABLE

Infinite number of possible values that fall

between any two observed values

SAMPLING ERROR

Naturally occurring discrepancy or error that exists

between a sample statistic and the corresponding

population parameter

DISCRETE VARIABLE

Separate, indivisible categories

E.g Number of Students, Number of Correct

Answers

DICHOTOMOUS VARIABLE

-

One that takes on only of two possible

values when observed or measured

-

Artificial Dichotomous

True dichotomous

LEVELS OF MEASUREMENT

Also called SCALES OF MEASUREMENT, is a

classification that describes the nature of

information within the values assigned to variables

NOMINAL

-Also known as Categorical Values

-Variable with values that are categories

-E.g Sex, Nationality, Religion, Civil Status

ORDINAL

-Also known as Rank-Order Value

-E.g Highest Educational Attainment, S,M,L,

Likert Scale

INTERVAL

Level of measurement

Variable that contains equal-interval

between numbers and contain no

absolute zero point

-

Distance between value

-

E.g Temperature, IQ, Stress

RATIO

An interval scale with the additional

feature of an absolute zero point

Variables that have a natural order, a

quantifiable difference between values

and a ‘true zero’ value.

E.g Time to Complete a task, number of

correct answers, weight gain in the past 6

months.

Artificial Dichotomous -

derived from

scores (passed or failed)

True Dichotomous-

naturally occurring

(Male/Female, Yes/No, Heads/Tails)

QUALITATIVE

- Nominal, Dichotomy, Ordinal, Discrete

● QUANTITATIVE

- Interval, Continuous, Ratio

DATA STRUCTURE 1: One Group with One or More

Separate Variables Measured for Each Individual

- One (or more) variables measured per individual

- Variable(s) is/are described by descriptive

statistics

- May use category and/or numerical variables

DESCRIPTIVE RESEARCH

- Involves measuring one or more separate variables

for each individual with the intent of simply

describing the individual variables

SURVEY RESEARCH

- A useful way of obtaining data about people’s

opinions, attitudes, preferences, and experiences

that are hard to observe directly

- Data may be obtained using questionnaires and

interviews

RELATIONSHIP BETWEEN VARIABLES

- Two (or more) variables observed and measured

- One of two possible data structures used to

determine what type of relationship exists

DATA STRUCTURE 2: One Group with Two Variable

Measured for Each Individual

- One group of respondents

- Measurement of two variables for each respondent

- Goal is to describe the type and magnitude of the

relationship

- Patterns in the data reveal relationships

- Non-experimental method of study

CORRELATIONAL METHOD

- Two different variables are observed to determine

whether there is a relationship between them

- Two is observed, one is measured

CORRELATIONAL METHOD LIMITATIONS

- Can demonstrate the existence of a relationship

- Does not provide an explanation for the relationship

- Does not demonstrate a cause-and-effect

relationship between two variables

DATA STRUCTURE 3: Comparing Two (or more) Groups of

Scores

- One variable defines the group

- Scores are measured on second variable

- Both experimental and non-experimental studies

use this structure

EXPERIMENTAL METHOD

- One (or more) variable is manipulated while

another variable is observed or measured

- Aims to establish a cause-and-effect relationship

between two variables and attempts to control all

other variables to prevent them from influencing

the results

INDEPENDENT VARIABLE

- The variable that is manipulated by the researcher

- Should consist of at least two (or more) levels

(treatment conditions) to which subjects are

exposed

DEPENDENT VARIABLE

- The variable that is being observed or measured to

assess the effect of the treatment

EXPERIMENTAL CONDITION

- A condition in an experiment wherein the subjects

will receive the experimental treatment

- Experimental random assignment: manipulation of

independent variables, control of extraneous

variables and measurement of dependent variable

CONTROL CONDITION

- A condition in an experiment wherein the subjects

do not receive the experimental treatment

QUASI-EXPERIMENTAL DESIGN

- Often seem like (as the prefix quasi implies) real

experiments, but they lack one or more of its

essential elements, such as manipulation of

antecedents (independent variable) and random

assignment to treatment conditions

NONEQUIVALENT GROUP DESIGN

A design in which the researcher compares the

effect of different treatment conditions on

pre-existing groups of respondents/subjects

EX-POST FACTO STUDY

- A study which a researcher systematically

examines the effects of pre-existing subject

characteristics (or subject variables) by forming

group based on theses naturally occurring

differences between subjects

LONGITUDINAL DESIGN

- A method in which the same group of subjects is

followed and measured at different points in time; a

method that looks for change across time

APA-STYLE RESEARCH WRITING

- Style of documentation of sources used by the

American Psychological Association

- This form of writing research papers is used mainly

in the social sciences like psychology,

anthropology, sociology, as well as education fields

Sections of an APA-Style Research Paper

- Title

- Abstract

- Introduction

- Method

- Results

- Discussion

- References

- Appendices