Participant Relevant Confounding Variables Assignment

Design of Experiments > Confounding Variable

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What is a Confounding Variable?

A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. They can suggest there is correlation when in fact there isn’t. They can even introduce bias. That’s why it’s important to know what one is, and how to avoid getting them into your experiment in the first place.

A confounding variable can have a hidden effect on your experiment’s outcome.

In an experiment, the independent variable typically has an effect on your dependent variable. For example, if you are researching whether lack of exercise leads to weight gain, lack of exercise is your independent variable and weight gain is your dependent variable. Confounding variables are any other variable that also has an effect on your dependent variable. They are like extra independent variables that are having a hidden effect on your dependent variables. Confounding variables can cause two major problems:

Let’s say you test 200 volunteers (100 men and 100 women). You find that lack of exercise leads to weight gain. One problem with your experiment is that is lacks any control variables. For example, the use of placebos, or random assignment to groups. So you really can’t say for sure whether lack of exercise leads to weight gain. One confounding variable is how much people eat. It’s also possible that men eat more than women; this could also make sex a confounding variable. Nothing was mentioned about starting weight, occupation or age either. A poor study design like this could lead to bias. For example, if all of the women in the study were middle-aged, and all of the men were aged 16, age would have a direct effect on weight gain. That makes age a confounding variable.

Confounding Bias

Technically, confounding isn’t a true bias, because bias is usually a result of errors in data collection or measurement. However, one definition of bias is “…the tendency of a statistic to overestimate or underestimate a parameter”, so in this sense, confounding is a type of bias.

Confounding bias is the result of having confounding variables in your model. It has a direction, depending on if it over- or underestimates the effects of your model:

  • Positive confounding is when the observed association is biased away from the null. In other words, it overestimates the effect.
  • Negative confounding is when the observed association is biased toward the null. In other words, it underestimates the effect.

How to Reduce Confounding Variables

Make sure you identify all of the possible confounding variables in your study. Make a list of everything you can think of and one by one, consider whether those listed items might influence the outcome of your study. Usually, someone has done a similar study before you. So check the academic databases for ideas about what to include on your list. Once you have figured out the variables, use one of the following techniques to reduce the effect of those confounding variables:

  1. Bias can be eliminated with random samples.
  2. Introduce control variables to control for confounding variables. For example, you could control for age by only measuring 30 year olds.
  3. Within subjects designs test the same subjects each time. Anything could happen to the test subject in the “between” period so this doesn’t make for perfect immunity from confounding variables.
  4. Counterbalancing can be used if you have paired designs. In counterbalancing, half of the group is measured under condition 1 and half is measured under condition 2.

Related Articles:

Age Graded Influences
History Graded Influences
Nonnormative Influences


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Chapter 2: Methods

  1. Overview
    1. Hindsight Bias
      1. Upon hearing research findings, the tendency to believe that you knew it all along
    2. Applied Research
      1. Has clear, practical applications
    3. Basic Research
      1. Explores questions that are of interest to psychologists
      2. Not intended to have immediate real world applications
  2. Terminology
    1. Hypothesis
      1. Expresses a relationship between two variables
    2. Variables
      1. The dependent variable depends on the independent variable>
      2. Things that can vary among the participants in the research
    3. Theory
      1. Aims to explain some phenomenon
      2. Allows researchers to generate testable hypotheses with the hope of collecting data that support the theory
    4. Operational Definitions
      1. Explanations of how variables will be measured
    5. Validity and Reliability
      1. Research is valid when:
        1. it measures what the researcher set out to measure
        2. it is accurate
      2. Research is reliable when:
        1. it can be replicated
        2. it is consistent
    6. Participants (Subjects)
      1. The individuals on which the research will be conducted
    7. Sampling
      1. The process by which participants are selected
    8. ​Sample
      1. ​The group of participants
    9. ​Population
      1. ​Includes anyone or anything that could possibly be selected in the sample
    10. ​Random Selection
      1. ​Every member of the population has an equal chance of being selected
      2. ​​Increases the likelihood of a representative sample
      3. Allows researchers to generalize about their results
    11. ​Stratified Sampling
      1. ​Allows a researcher to ensure that the sample represents the population on some criteria (ex. race)
      2. Sample size uses proportions equal to that of the population
  3. ​​Experimental Method
    1. ​Laboratory Experiments
      1. ​​​Conducted in a lab
      2. Advantage- highly controlled
    2. ​Field Experiments
      1. ​Conducted out in the world
      2. Advantage- more realistic
    3. ​Experiment
      1. ​Only way to show a cause-effect relationship
      2. Preferred research method
    4. ​Confounding Variables
      1. ​Any difference between the experimental and control conditions that could affect the dependent variable
        1. ​(other than the independent variable)
    5. ​​Assignment
      1. ​The process by which participants are put into the experimental or control group
    6. ​Random Assignment
      1. ​Each participant has an equal chance of being placed into any group
      2. ​​Limits the effect of participant-relevant confounding principles
    7. ​Group Matching
      1. ​Divide the sample into groups based on some criterion and assign half of each group to each condition
      2. ​​ex: gender
    8. ​Situation-Relevant Confounding Variable
      1. ​Ex: time of day, weather, presence of others
      2. Each condition has to be equivalent with the exception of the independent variable
    9. ​Experimenter Bias
      1. ​​​A situation-relevant confounding variable
      2. The unconscious tendency for research members to treat members of the experimental and control groups differently to increase the chance of confirming the hypothesis
    10. ​Double-Blind Procedure
      1. ​Neither the participants nor the researcher are able to affect the outcome of the research
      2. ​​Eliminates experimenter and subject bias
    11. ​Single Blind
      1. ​Only the subjects don’t know to which group they’ve been assigned
      2. Minimizes demand characteristics and participant bias
      3. Demand characteristics
        1. ​cues about the purpose of a study that affect the participants’ responses
      4. ​Response/participant bias
        1. ​the tendency for subjects to behave in certain ways
        2. ​​social desirability
          1. ​the tendency to try to give politically correct answers
    12. ​​​Experimental Group
      1. ​Gets the treatment operationalized in the independent variable
    13. ​Control Group
      1. ​Gets none of the independent variable
      2. ​​Without it, knowing the effects of the experimental treatment is impossible
    14. ​Hawthorne Effect
      1. ​Selecting a group of people on whom to experiment affects the performance of that group, regardless of what is done to them
    15. ​Placebo Effect
      1. ​Controlled by the placebo method
        1. ​giving the control group an inert drug
    16. ​​Counterbalancing
      1. ​Using participants as their own control group
      2. ​To eliminate order effects, have half do one order, the other half the other, then switch
  4. ​​Correlational Method
    1. ​Correlations
      1. ​Express a relationship between two variables
      2. Positive
        1. ​the presence of one predicts the presence of the other
      3. ​Negative
        1. ​the presence of one predicts the absence of the other
      4. ​Do not imply causation
    2. ​Ex-Post Facto Study
      1. ​Cause and effect cannot be determined
      2. ​​The assignment of the independent variable is predetermined
      3. Controls all other aspects of the research process
    3. ​Survey Method
      1. ​Asking people to fill out surveys
      2. Investigates relationships, but not causation
      3. No independent or dependent variables
      4. Participant-relevant confounding variables can’t be controlled for
      5. Controlling for situation-relevant confounding variables
        1. ​bring all participants to one place at one time to complete the survey
      6. ​Response rate
        1. ​people who send the survey back
  5. ​​​Naturalistic Observation
    1. ​Naturalistic Observation
      1. ​Observe participants in their natural habitats without interacting with them
      2. Control is sacrificed
      3. Goal
        1. ​to get a realistic and rich picture of the participants’ behavior
    2. ​​Disparity with Field Experiments
      1. ​In field experiments:
        1. ​manipulate independent variable
        2. ​​attempt to eliminate all confounding variables
  6. ​​​Case Studies
    1. ​Case Study
      1. Used to get a full, detailed picture of one participant or a small group of participants
      2. Findings can’t be generalized to a larger population
      3. Often used to research clinical disorders
  7. ​​Descriptive Statistics
    1. ​Frequency Distributions
      1. ​Can easily be turned into:
        1. ​frequency polygons
        2. histograms
      2. ​Y-axis represents frequency
      3. X-axis represents what you’re graphing
    2. ​Central Tendency
      1. ​Mean, median, mode
      2. ​​Mean most common, but most affected by outliers/extreme scores
    3. ​Outliers Skew Distributions
      1. ​Positively skewed
        1. ​has high outliers
        2. contains more low scores
        3. ​the mean is higher than the median
      2. ​Negatively skewed
        1. ​low outliers
        2. the mean is less than the median
    4. ​​Measures of Variability
      1. ​Depict the diversity of a distribution
      2. Range
        1. highest score minus lowest score
      3. ​Variance and standard deviation
        1. ​relate the average distance of any score in the distribution from the mean
        2. the higher they are, the more spread out the distribution
        3. the square root of the variance is the standard deviation
      4. ​Z-scores
        1. ​measure the distance of a score from the mean in units of standard deviation
        2. scores above the mean have a positive z-score
        3. 600 on SAT: z-score of +1
      5. ​Normal curve
        1. ​one standard deviation from the mean- 68% of scores
        2. two standard deviations- 95%
        3. three standard deviations- 99.7%
      6. ​Percentiles
        1. indicate the distance of a score from zero
        2. 50th percentile = z-score of 0
  8. ​​​Correlations
    1. ​Correlation Coefficient
      1. ​Range from -1 to +1
      2. -1 = perfect negative correlation
      3. +1 = perfect positive correlation
      4. 0 = weakest possible correlation
    2. ​Scatter Plot
      1. ​Correlations can be graphed using a scatter plot
      2. Line of best fit (regression line)
        1. ​drawn through it
  9. ​​​Inferential Statistics
    1. ​Purpose
      1. ​To determine whether findings can be applied to the larger population from which the sample was selected
    2. ​Sampling Error
      1. ​The extent to which the sample differs from the population
    3. ​Tests
      1. ​ANOVAs, MANOVAs, t-tests
      2. Consider the magnitude of difference and size of sample
      3. Yield a p-value
        1. ​the smaller, the more significant the results
        2. p = .05 is the cut off for statistically significant results
          1. ​5% chance that results occurred by chance
  10. ​​​​APA Ethical Guidelines
    1. ​Institutional Review Board (IRB)
      1. ​Any type of academic research must first propose the study to this ethics board
    2. ​Animal Research: Requirements for Psychological Studies
      1. ​They must have a clear scientific purpose
        1. ​research must answer a specific and important scientific question
          1. ​animals chosen must be best suited to answer it
      2. ​​Must care for and house animals in a humane way
      3. Must acquire animal subjects legally
        1. ​purchased from accredited companies
        2. trapped in a humane way
      4. ​Must design experimental procedures that employ the least amount of suffering feasible
    3. ​Human Research
      1. ​Coercion
        1. ​participation must be voluntary
      2. ​Informed consent
        1. ​participants must know that they are involved in research and give consent
        2. ​no extreme deception about the nature of the study
      3. ​Anonymity/confidentiality
        1. ​identity and actions of participants can’t be revealed
        2. can’t identify participants as the source of any of the data
      4. ​Risk
        1. ​participants can’t be placed at significant mental/physical risk
      5. ​Debriefing procedures
        1. ​participants must be told the purpose of the study and provided with ways to contact the researchers about study results

You just finished Chapter 2: Methods. Nice work!

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How to cite this note (MLA)

Aboukhadijeh, Feross. "Chapter 2: Methods" Study Notes, LLC., 12 Oct. 2013. Web. 10 Mar. 2018. <>.

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