A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.

A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.

A variable thought to predict an outcome variable. This is basically another term for independent variable.

A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with 'dependent variable' for the sake of an easy life.

A variable thought change as a function of changes in a predictor variable. This term could be synonymous with 'dependent variable' for the sake of an easy life.

A variable thought to predict an outcome variable. This is basically another term for independent variable.

A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.

A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.

A variable thought change as a function of changes in a predictor variable. This term could be synonymous with 'dependent variable' for the sake of an easy life.

A variable thought to predict an outcome variable. This is basically another term for independent variable.

A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.

A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.

A variable thought to predict an outcome variable. This is basically another term for independent variable.

A variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated.

A variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome.

A variable thought to change as a function of changes in a predictor variable. This term could be synonymous with 'dependent variable'.

The same as nominal variable but the categories have a logical order (eg. whether people got a fail, a pass, a merit or a distinction in their exam).

There are more than two categories (eg. whether someone is an omnivore, vegetarian, vegan, or fruitarian).

There are only two categories (eg. dead or alive)

There are only two categories (eg. dead or alive)

There are more than two categories (eg. whether someone is an omnivore, vegetarian, vegan or fruitarian)

The same as nominal variable but the categories have a logical order (eg. whether people got a fail, a pass, a merit or a distinction their exam)

The same as a nominal variable but the categories have a logical order (eg. whether people got a fail, a pass, a merit or a distinction in their exam)

There are only two categories (eg. dead or alive)

There are more than two categories (eg. whether someone is an omnivore, vegetarian, vegan or fruitarian).

Analyse the data: look at the data visually and by fitting a statistical model to see if it
supports your predictions (and therefore your theory). At this point you should return to
your theory and revise it if necessary.

Generating a research question: through an initial observation (hopefully backed up by some
data).

Collect data to test the theory: decide on what variables you need to measure to test your
predictions and how best to measure or manipulate those variables

Generate hypotheses: break your theory down into a set of testable predictions.

Generate a theory to explain your initial observation

Analyse the data: look at the data visually and by fitting a statistical model to see if it
supports your predictions (and therefore your theory). At this point you should return to
your theory and revise it if necessary.

Generating a research question: through an initial observation (hopefully backed up by some
data).

Collect data to test the theory: decide on what variables you need to measure to test your
predictions and how best to measure or manipulate those variables.

Generate a theory to explain your initial observation.

Generate hypotheses: break your theory down into a set of testable predictions

Generate hypotheses: break your theory down into a set of testable predictions

Generating a research question: through an initial observation (hopefully backed up by some
data).

Generate a theory to explain your initial observation.

Analyse the data: look at the data visually and by fitting a statistical model to see if it
supports your predictions (and therefore your theory). At this point you should return to
your theory and revise it if necessary.

Collect data to test the theory: decide on what variables you need to measure to test your
predictions and how best to measure or manipulate those variables

Generating a research question: through an initial observation (hopefully backed up by some
data).

Generate hypotheses: break your theory down into a set of testable predictions.

Analyse the data: look at the data visually and by fitting a statistical model to see if it
supports your predictions (and therefore your theory). At this point you should return to
your theory and revise it if necessary.

Generate a theory to explain your initial observation.

Collect data to test the theory: decide on what variables you need to measure to test your
predictions and how best to measure or manipulate those variables.

Analyse the data: look at the data visually and by fitting a statistical model to see if it
supports your predictions (and therefore your theory). At this point you should return to
your theory and revise it if necessary.

Generating a research question: through an initial observation (hopefully backed up by some
data).

Generate a theory to explain your initial observation.

Generate hypotheses: break your theory down into a set of testable predictions.

Collect data to test the theory: decide on what variables you need to measure to test your
predictions and how best to measure or manipulate those variables.

In a word, causality. In experimental research we manipulate a variable (predictor, independent variable) to see what effect it has on another variable (outcome, dependent variable).

This manipulation, if done properly, allows us to compare situations where the
causal factor is present to situations where it is absent.

Therefore, if there are differences between these situations, we can attribute cause to the variable that we manipulated. In
correlational research, we measure things that naturally occur and so we cannot attribute cause but instead look at natural covariation between variables.

All of the above.

This is a discrete ratio measure. It is discrete because you can download only whole songs, and it is ratio because it has a true and meaningful zero (no downloads at all).

This is a nominal variable. Bands can be identified by their name, but the names have no meaningful order. The fact that Norwegian black metal band 1349 called DISCOVERING STATISTICS USING SPSS PROFESSOR ANDY P FIELD 2 themselves 1349 does not make them better than British boy-band has-beens 911; the fact that 911 were a bunch of talentless idiots does, though.

This is an ordinal variable. We know that the band at number 1 sold more than the band at number 2 or 3 (and so on) but we don’t know how many more downloads they had. So, this variable tells us the order of magnitude of downloads, but doesn’t tell us how many downloads there actually were.

This is an ordinal variable. We know that the band at number 1 sold more than the band at number 2 or 3 (and so on) but we don’t know how many more downloads they had. So, this variable tells us the order of magnitude of downloads, but doesn’t tell us how many downloads there actually were.

This is a discrete ratio measure. It is discrete because you can download only whole songs, and it is ratio because it has a true and meaningful zero (no downloads at all).

This is a nominal variable. Bands can be identified by their name, but the names have no meaningful order. The fact that Norwegian black metal band 1349 called DISCOVERING STATISTICS USING SPSS PROFESSOR ANDY P FIELD 2 themselves 1349 does not make them better than British boy-band has-beens 911; the fact that 911 were a bunch of talentless idiots does, though.

This is a discrete ratio measure. It is discrete because you can download only whole songs, and it is ratio because it has a true and meaningful zero (no downloads at all).

This is an ordinal variable. We know that the band at number 1 sold more than the band at number 2 or 3 (and so on) but we don’t know how many more downloads they had. So, this variable tells us the order of magnitude of downloads, but doesn’t tell us how many downloads there actually were.

This is a nominal variable. Bands can be identified by their name, but the names have no meaningful order. The fact that Norwegian black metal band 1349 called DISCOVERING STATISTICS USING SPSS PROFESSOR ANDY P FIELD 2 themselves 1349 does not make them better than British boy-band has-beens 911; the fact that 911 were a bunch of talentless idiots does, though.

This variable is continuous and ratio. If the drummer buys 100 g of cocaine and the singer buys 1 kg, then the singer has 10 times as much.

This variable is continuous and ratio. It is continuous because money (pounds, dollars, euros or whatever) can be broken down into very small amounts (you can earn fractions of euros even though there may not be an actual coin to represent these fractions).

This variable is categorical and nominal: the name of the drug tells us something meaningful (crack, cannabis, amphetamine, etc.) but has no meaningful order.

This variable is categorical and nominal: the name of the drug tells us something meaningful (crack, cannabis, amphetamine, etc.) but has no meaningful order.

This variable is continuous and ratio. If the drummer buys 100 g of cocaine and the singer buys 1 kg, then the singer has 10 times as much.

This variable is continuous and ratio. It is continuous because money (pounds, dollars, euros or whatever) can be broken down into very small amounts (you can earn fractions of euros even though there may not be an actual coin to represent these fractions).

This variable is categorical and nominal: the name of the drug tells us something meaningful (crack, cannabis, amphetamine, etc.) but has no meaningful order.

This variable is continuous and ratio. If the drummer buys 100 g of cocaine and the singer buys 1 kg, then the singer has 10 times as much.

This variable is continuous and ratio. It is continuous because money (pounds, dollars, euros or whatever) can be broken down into very small amounts (you can earn fractions of euros even though there may not be an actual coin to represent these fractions).

This variable is categorical and nominal too: the instruments have no meaningful order but their names tell us something useful (guitar, bass, drums, etc.).

This variable is categorical and nominal too: the phone numbers have no meaningful order; they might as well be letters. A bigger phone number did not mean that it was given by a better person.

This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).

This variable is categorical and nominal too: the instruments have no meaningful order but their names tell us something useful (guitar, bass, drums, etc.).

This variable is categorical and nominal too: the phone numbers have no meaningful order; they might as well be letters. A bigger phone number did not mean that it was given by a better person.

This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).

This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).

This variable is categorical and nominal too: the phone numbers have no meaningful order; they might as well be letters. A bigger phone number did not mean that it was given by a better person.

All of the above.

This is a continuous and ratio variable. The amount of time could be split into infinitely small divisions (nanoseconds even) and there is a meaningful true zero (no time spent learning your instrument means that, like 911, you can’t play at all).

This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).

This variable is categorical and nominal too: the instruments have no meaningful order but their names tell us something useful (guitar, bass, drums, etc.).

This variable is categorical and nominal too: the instruments have no meaningful order but their names tell us something useful (guitar, bass, drums, etc.).

This variable is categorical and binary: the people dishing out their phone numbers could fall into one of only two categories (male or female).

This is a continuous and ratio variable. The amount of time could be split into infinitely small divisions (nanoseconds even) and there is a meaningful true zero (no time spent learning your instrument means that, like 911, you can’t play at all).