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Planning Psychological Research

Deciding on a research question: Co-variables, Operationalisation of variables and Extraneous variables

  • 3 things to know
  • Component 2

Overview

Recall questions on Co-variables, Operationalisation of variables and Extraneous variables.

What you need to know

Answer each one before you reveal it.

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Key term

Why do we speak of co-variables rather than an independent and dependent variable in a correlation?

Key term

One memory condition is tested at nine in the morning and the other at four in the afternoon. What is wrong with this?

Key term

Why does poor operationalisation damage a study?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    What are co-variables?

    Model answer

    They are the two variables measured in a correlational study, neither of which is manipulated by the researcher.

  2. 2

    Why do we speak of co-variables rather than an independent and dependent variable in a correlation?

    Model answer

    Nothing has been manipulated, so no cause-and-effect direction can be claimed, and the terms independent and dependent would wrongly imply that one variable produces the other.

  3. 3

    What does it mean to operationalise a variable?

    Model answer

    It means defining the variable precisely in terms of exactly how it will be manipulated or measured, so that another researcher could repeat the study.

  4. 4

    Operationalise the variable “aggression” for a study of children in a playground.

    Model answer

    Aggression could be operationalised as the number of physical acts such as hitting, kicking or pushing recorded during a ten-minute observation of the playground.

  5. 5

    Why does poor operationalisation damage a study?

    Model answer

    A vaguely defined variable cannot be measured consistently, which lowers validity and makes it impossible for anyone else to replicate the work.

  6. 6

    What is an extraneous variable?

    Model answer

    It is any variable other than the independent variable that could affect the dependent variable and add unwanted noise to the results if it is left uncontrolled.

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