Internal Validity and Confounding Variables

Internal Validity & Confounding Variables

• A confounding variable is a variable that systematically varies with the independent variable.
• Internal validity refers to the confidence that the independent variable caused the change in the dependent variable.
• A confounding variable threatens internal validity because it provides an alternative explanation for the observed results other than the independent variable.

Ensuring Internal Validity in Experiments

• Internal validity is achieved through a carefully controlled experiment that eliminates alternative explanations for the observed results.
• A posttest-only design is a type of experimental design in which participants are randomly assigned to groups, exposed to the independent variable, and then measured on the dependent variable.
• This design provides good control over extraneous variables by ensuring that all groups except the independent variable are kept constant.

The Pretest-Posttest Design

• A potential problem with pretest-posttest designs is mortality, the loss of participants from a study.
• Mortality can threaten internal validity if the participants who drop out of the study are different from those who remain.

Solomon Four-Group Design

• The Solomon Four-Group Design is used to assess the effects of the pretest on the dependent variable.
• It includes four groups, two that receive a pretest and two that do not, and only two groups exposed to the independent variable.
• This design helps determine if the pretest itself influenced the outcomes or if the changes in the dependent variable are truly due to the independent variable.

Between-Subjects vs. Within-Subjects Designs

• An independent groups design (between-subjects design) assigns different participants to each level of the independent variable.
• This ensures that there is no overlap between participants in different groups. Each participant is exposed to only one level of the independent variable.
• Examples include comparing the effectiveness of two different types of therapy, each with a separate group of participants.
• A repeated measures design (within-subjects design) exposes participants to all levels of the independent variable.
• This design is efficient as it uses fewer participants but requires more careful planning because of potential threats to internal validity like order effects.
• Examples include assessing the effectiveness of a new drug across various doses using the same participants for all doses.

Order Effects

• Practice effects result from participants improving their performance on a task due to repeated exposure.
• Fatigue effects occur when participants perform worse as they become tired or bored with repeated tasks.
• Counterbalancing helps control for order effects by presenting the different levels of the independent variable in different orders to different participants.

Matched Pairs Design

• A matched pairs design creates pairs of participants with similar characteristics and then randomly assigns one member from each pair to each experimental condition.
• It reduces the risk of confounding variables related to participant characteristics.

Manipulation of Independent Variables

• A straightforward manipulation is directly manipulating the independent variable, often involves varying the levels of the independent variable.
• Examples include manipulating the amount of sleep participants get before a task or giving different doses of a medication
• A staged manipulation involves creating a specific situation to manipulate the independent variable.
• Examples include creating a situation where participants feel stressed or anxious, often involving a scenario or a confederate.

Measuring Dependent Variables

• Self-report measures ask participants to provide information about themselves, often with questionnaires or interviews.
• Behavioral measures focus on observable behaviors, often recorded by observers or using tracking devices.
• Ceiling effect occurs when a task is too easy, and most participants score high on the dependent variable.
• Floor effect occurs when a task is too difficult, and most participants score low on the dependent variable.

Threats to Internal Validity

• Demand characteristics are cues that give participants hints about the purpose of the study and how they are expected to behave.
• Placebo effect occurs participants' expectations of treatment can influence their responses.
• Experimenter expectancy effect occurs when researchers' expectations about the results of the study unconsciously influence how they treat participants.

Manipulation Check

• Manipulation check is a measure used to assess the effectiveness of the manipulation of the independent variable.
• It verifies that the manipulation successfully changed the independent variable in the intended way.
• It helps determine whether the independent variable truly influenced the dependent variable.

Quantitative & Qualitative Approaches

• Quantitative approaches use numerical data and statistical analysis.
• Examples include experiments, surveys, and correlations.
• Qualitative approaches involve non-numerical data, such as interviews, observations, and text analysis.
• Examples include studies that focus on understanding meanings, interpretations, and experiences.

Naturalistic Observation

• Naturalistic observation is a type of research where researchers observe and record behavior in a natural setting, without actively influencing the situation.
• This method allows for the examination of behavior in its real-world context.
• It allows for examining behavior in its natural setting, but there are difficulties in controlling variables and potential biases in observation.

Reactance

• Reactance is a phenomenon where individuals feel their freedom is threatened and resist the attempt to control their behavior.
• It can influence the behavior of participants in a study, making it difficult to determine the true effects of the independent variable.

Reliability

• Reliability is the extent to which a measure is consistent and stable over time.
• High reliability means a measure produces consistent results across different occasions or with different observers.

Case Studies

• Case studies are in-depth investigations of a single individual, group, or event.
• They are often used in clinical psychology and can provide valuable insights into rare phenomena or complex cases.
• They are informative for unique cases but can be subjective and difficult to generalize to other individuals or situations.

Content Analysis

• Content analysis is a method used to systematically analyze written, spoken, or visual materials to identify patterns, themes, or meanings.
• It involves coding and quantifying the data using a set of predetermined rules.
• It can be used to study media content, social media interactions, or historical documents to uncover trends and insights.

Advantages of the Survey Method

• Surveys offer a cost-effective and efficient way to collect data from many individuals.
• They are easy to administer and can be used to gather a large amount of data on topics like opinions, beliefs, attitudes, and behaviors.

Response Set

• Response set occurs when participants respond to survey questions with a consistent pattern of answers, regardless of the content of the questions.
• It can bias the results of the survey, making it difficult to interpret the findings accurately.

Social Desirability

• Social desirability is a bias that results from participants' tendency to respond to survey questions in a way that will present them in a favorable light.
• It can lead to inaccurate responses, especially on sensitive topics.

Sampling Techniques in Surveys

• Sampling techniques are crucial in surveys to ensure that the results can be generalized to the broader population.
• This involves selecting a representative sample from the population of interest.

Confidence Intervals

• A 95% confidence interval indicates that we are 95% certain that the true population mean falls within that range.
• It provides an estimate of the margin of error associated with the sample mean.

Sample Size & Confidence Interval

• The larger the sample size, the narrower the confidence interval.
• In other words, as the sample size increases, we become more confident that the sample mean is a good estimate of the true population mean.

Non-Probability Sampling

• Non-probability sampling involves techniques where every member of the population does not have an equal chance of being selected for the sample.
• It should be used cautiously, as it might not be representative of the population.
• Examples include convenience sampling, snowball sampling, and quota sampling.

Types of Probability Sampling

• Simple random sampling gives each member of the population an equal chance of being selected.
• Stratified random sampling divides the population into subgroups (strata) and then randomly selects participants from each stratum.
• Cluster sampling involves randomly selecting clusters of participants, often geographic clusters, and then including all members of those clusters in the sample.
• Haphazard sampling involves selecting participants in a haphazard way, without any systematic method.

Factorial Designs

• A factorial design involves two or more independent variables that are manipulated simultaneously.
• This allows researchers to study the main effects of each independent variables and the interaction effect between them.

Main Effect

• A main effect refers to the effect of one independent variable on the dependent variable, averaging across the levels of other independent variables.
• It refers to the overall effect of one independent variable, ignoring the other independent variable.

Interaction Effect

• An interaction effect occurs when the effect of one independent variable depends on the level of the other independent variable.
• It occurs when the independent variables interact with each other.

3 x 3 Design

• A 3 x 3 factorial design involves two independent variables, each with three levels.
• It results in nine different experimental conditions.

IV x PV Factorial Design

• An IV x PV factorial design involves one independent variable (IV) and one participant variable (PV), which is a characteristic of the participants that is measured instead of being manipulated.
• It can help examine how the independent variable affects the dependent variable in different groups based on the participant variable.

2 x 2 & 3 x 2 x 2 Mixed Factorial Designs

• A 2 x 2 mixed factorial design involves two independent variables, one between-subjects (independent groups) and one within-subjects (repeated measures).
• This design involves measuring the dependent variable in different groups for one factor, and repeatedly measuring the other factor in the same group.
• A 3 x 2 x 2 factorial design involves three independent variables, with one variable having three levels and the other two having two levels.
• It allows for the study of complex interactions among three factors and how each variable's main effect and combined effects influence the dependent variable.

Explore the concepts of internal validity and confounding variables in experimental design. Understand how these elements impact the reliability of research results and learn about design strategies to enhance internal validity. This quiz will test your knowledge on critical experimental designs such as posttest-only and pretest-posttest methods.