Alternative explanation

• The idea that it is possible that some other, uncontrolled, extraneous variable may be responsible for the observed relationship

Census

• A census is a study of every unit, everyone or everything, in a population.

Control

• Manipulating the independent variable in an experiment or any other extraneous variables that could affect the results of a study

Control group

• The group of participants that does not receive any level of the independent variable and serves as the baseline in a study

Constant

• A fixed value; values that do not change

Independent variable

• The variable that is manipulated or used to predict an outcome

Dependent variable

• The outcome variable or what is expected to be impacted by the independent variable

Description

• Carefully observing behaviour in order to describe it

Experimental group

• The group of participants that receives some level of the independent variable

Experimental method/essential characteristics of experiments

• An experiment requires manipulation, control, and random assignment.

Random assignment

• Assigning participants to conditions in such a way that every participant has an equal probability of being placed in any condition

Positive Relationship

• A relationship between two variables in which an increase in one variable is accompanied by an increase in the other variable

Negative Relationship

• A relationship between two variables in which an increase in one variable is accompanied by a decrease in the other variable

Population

• The entire set/group of people from which you got your data from

Sample - The group of people who participate in study

Explanation

• Identifying the cause that determines when and why a behavior occurs

Hypothesis

• Prediction regarding the outcome of a single study

Prediction

• The process of using correlations between variables to hypothesize about future events and outcomes

Manipulation

• changing an experiment systematically so that different groups are exposed to different levels of that variable

• Changing a variable/how variable operates in conditions

Convenience sampling

• Convenience sampling is a sampling method where units are selected for inclusion in the sample because they are the easiest for the researcher to access.

Random sampling

• A random sample is achieved when, through random selection, each member of the population is equally likely to be chosen as part of the sample.

Sample

• A sample is a smaller, manageable version of a larger group

Theory

• An organized system of assumptions and principles that attempts to explain certain phenomena and how they are related

Variable

• Anything that can differ amongst people, contexts, or time (age, weight, mood, etc)

• An event or behaviour that has at least two values

Scales of Measurement

Nominal scale

• A scale in which objects or individuals are broken into categories that have no numerical properties; Name-type data

• Categorical data; ethnicity, gender, political affiliation

Interval scale

• A scale in which the units of measurement (intervals) between the numbers on the scale are all equal in size

• Number based with interpretable and consistent distance between values

• For example, the Fahrenheit temperature scale is an interval scale of measurement

Ordinal scale

• A scale in which objects or individuals are categorized and the categories form a rank order along a continuum; Rankings

• Ordinal data are often referred to as ranked data because the data are ordered from highest to lowest, or biggest to smallest. For example, reporting how students did on an exam based simply on their rank (highest score, second highest, and so on) would be an ordinal scale

Ratio scale

• A scale in which, in addition to order and equal units of measurement, there is an absolute zero that indicates an absence of the variable being measured

• Like interval, but zero has a true meaning

• Examples of ratio scales of measurement include weight, time, and height

Operational definition

• A definition of a variable in terms of the operations (activities) a researcher uses to measure or manipulate it

• Specifies the activities of the researcher in measuring and/or manipulating a variable

What are the four ways of obtaining knowledge?

• Intuition; guts, emotions, and instinct

• Authority; accept ideas from authority figures

• Rationalism; using logic and reasoning

• Empiricism; observation and experience

What makes science scientific?

• Systematic Empiricism; an observation of relationships that is carefully structured, so you can learn about cause and effect relations between variables.

• Empiricism is the process of making an observation about experiences.

1. Design project that answers a research question

2. Collect data (numbers or information)

3. Anaylze the data

4. Interpret the data (in relation to question)

5. Share the results

What are the 3 goals of science?

• To describe; making careful observations to describe it

• To predict; use information to anticipate outcomes/identifying factors that indicate when an event will occur

• To explain; identifying the causes that determine when and why something occurs/understand the cause-and-effect relationships

The purpose of experimental research is to; explain

The purpose of correlational research is to; predict

The purpose of observational research is to; describe

Identify and compare descriptive methods

• Naturalistic Observation; Observing humans or other animals in their natural habitat

• Observational/Laboratory Method; Making observations of humans/animals behavior

• Case Study Method; An in depth study of one or more individuals

• Survey Method; Questioning individuals on a topic and then describing their responses

Identify and compare predictive (relational) methods

• Correlational method; A method that assesses the degree of relationship between two variables

• If two variables are correlated with each other, then we can predict from one variable to the other with a certain degree of accuracy

• Quasi-experimental method; Research that compares naturally occurring groups of individuals; the variable of interest cannot be manipulated

Describe the explanatory method

• A research method that allows a researcher to establish a cause and-effect relationship through manipulation of a variable and control of the situation.

• Researchers pay a great deal of attention to eliminating alternative explanations by using the proper controls

• This method enables researchers to know when and why a behavior occurs

Explain how we “do” science and how proof and disproof relate to doing science

• Scientists do not prove theories true; they are supported based on data collected but that does not mean it is true in all instances; proof of a theory is logically impossible

• We test a hypothesis by attempting to falsify or disconfirm it; if it cannot be falsified, then we say we have support for it

• Falsifying a hypothesis does not always mean that the hypothesis is false; we need to be cautious with our interpretation

Explain and give examples of an operational definition

• Operational Definition; A definition of a variable in terms of the operations (activities) a researcher uses to measure or manipulate it

• Specifies the activities of the researcher in measuring and/or manipulating a variable

For example, many people study abstract concepts such as aggression, attraction, depression,

hunger, or anxiety. How would we either manipulate or measure any of these variables? My definition of what it means to be hungry may be quite different from yours. If I decided to measure hunger by simply asking participants in an experiment if they were hungry, the measure would not be accurate because each individual may define hunger in a different way. What we need. is an operational definition of hunger—a definition of the variable in terms of the operations (activities) the researcher uses to measure or manipulate it.

Explain the four properties of measurement and how they are related to the four scales of measurement

• Identify; Objects that are different receive different scores

• E.g, If participants in a study had different political affiliations, they would receive different scores

• Magnitude (ordinality; When the ordering of the numbers reflects the ordering of the  variables; numbers are assigned in order so that some numbers represent more or less of the variable being measured than others

• Equal Unit Size ; When a difference of 1 is the same amount throughout the entire scale

• E.g, The difference between people who are 64 inches tall and 65 inches tall is the same as the difference between people who are 72 inches tall and 73 inches tall

• Absolute Zero; A property of measurement in which assigning a score of 0 indicates an absence of the variable being measured

• E.g, Time spent studying would have the property of absolute zero because a score of 0 on this measure would mean an individual spent no time studying. However, a score of 0 is not always equal to the property of absolute zero

Types of variables

• Discrete variables; Whole-number units or categories. They are made up of chunks or units that are detached and distinct from one another.

• A change in value occurs a whole unit at a time, and decimals do not make sense with discrete scales.

• Most nominal and ordinal data are discrete. For example, gender, political party, and ethnicity are discrete scales.

• Continuous variables; Fall along a continuum and allow for fractional amounts.

• The term continuous means that it “continues” between the whole-number units.

• Examples of continuous variables are age (22.7 years), height (64.5 inches), and weight (113.25 pounds). Most interval and ratio data are continuous in nature.

Explain why correlation does not mean causation

• A correlation simply means that the two variables are related in some way, not that they necessarily had any direct effect on eachother.

• Instead of A causing B, it could be B causing A

• There could be a third variable, C, that is the cause