Variables in science: Types, analysis, examples

Variables in science represent factors or conditions that researchers manipulate, measure or control in experiments. They serve as key elements in scientific studies and allow researchers to observe changes, identify patterns and establish cause-and-effect relationships. Common types of variables in science include:

• independent variables, which are manipulated
• dependent variables, which respond to changes in the independent variable
• controlled variables, which are kept constant to prevent interference
• extraneous variables, which are outside factors that could influence results
• confounding variables, which unintentionally affect both the independent and dependent variables and may potentially skew results.

Variables in science are essential to understanding and conducting effective research. This blog post discusses how different types of scientific variables — independent, dependent, controlled, extraneous and confounding — contribute to designing, analysing and interpreting scientific studies. It also explains methods for handling variables to improve the reliability and clarity of scientific findings and how to explain variables in scientific writing.

• Independent variables
• Dependent variables
• Controlled variables
• Extraneous variables
• Confounding variables
• Examples of variables in scientific experiments
• Analysing variables in science
• Scientific variables in academic writing
• Editing services for academic writing
• Resources

Independent variables

The independent variable is the factor that researchers manipulate to observe its effect on other variables. In experimental studies, it is part of the hypothesis, as it represents the presumed cause in cause-and-effect relationships. Researchers choose independent variables so they align with the study’s objectives.

• Usage: Researchers adjust the independent variable to observe changes in the dependent variable.
• Benefits: By isolating this variable, researchers can establish causation, allowing clearer interpretations of results.
• Limitations: Manipulating the independent variable may sometimes be ethically or practically challenging, especially in studies involving human behaviour or environmental factors.
• Examples
  • Dose of medication in a study on pain relief (e.g. 0 mg, 50 mg, 100 mg)
  • Type of fertiliser in a plant growth study (e.g. organic, synthetic, no fertiliser)
  • Temperature in a reaction rate experiment (e.g. 20°C, 40°C, 60°C)
  • Study technique in an education experiment (e.g. flashcards, summarising, rereading)

Dependent variables

Another type of variables in science is a dependent variable, which represents the observed effect or outcome in response to changes in the independent variable. Researchers measure the dependent variable to analyse how it responds to manipulations and make it a key indicator of the study’s findings.

• Usage: Researchers measure and record changes in the dependent variable to test their hypothesis.
• Benefits: It provides quantifiable evidence of the relationship between variables and offers insight into the study’s central question.
• Limitations: Many factors can affect the dependent variable, so isolating its responses solely to the independent variable is critical yet challenging. Fluctuations in measurement techniques can also introduce inconsistencies.
• Examples
  • Pain relief level measured on a scale in a medication study
  • Plant height in a fertiliser experiment
  • Reaction time in a cognitive study on caffeine
  • Exam score after different study techniques

Controlled variables

Controlled variables, also called constants, are factors that researchers keep the same across all conditions of the experiment. Controlling these variables is essential to reduce external influences on the dependent variable and ensure that any observed effect comes from the independent variable.

• Usage: Researchers identify and standardise controlled variables at the experiment’s outset to ensure reliability.
• Benefits: Controlled variables strengthen the study’s validity by reducing extraneous influences, which helps establish causation.
• Limitations: Complete control over all possible variables is often impractical. Uncontrolled factors could still introduce noise and potentially affect results.
• Examples
  • Soil type and amount of sunlight in a plant growth experiment
  • Room temperature in a reaction rate experiment
  • Participant age range and diet in a study on exercise and memory
  • Water quality and pH in a solubility experiment

Extraneous variables

Extraneous variables are additional factors not of primary interest in the study but could influence the dependent variable. These are not systematically controlled but need to be acknowledged as they may introduce variability.

• Usage: Researchers identify potential extraneous variables during study design and attempt to minimise their impact through careful study conditions.
• Benefits: Recognising extraneous variables improves study design by highlighting sources of potential bias or error.
• Limitations: It is nearly impossible to identify and control all extraneous variables, especially in complex environments, which may reduce study precision.
• Examples
  • Stress level in a study on the effects of caffeine on concentration
  • Ambient noise in a study measuring reaction time
  • Dietary intake in a weight-loss programme trial
  • Time of day for testing in a memory study

Confounding variables

Confounding variables are a subset of extraneous variables that have a direct influence on both the independent and dependent variables and may create an unintended association between them. They introduce bias and may falsely suggest or obscure relationships.

• Usage: Researchers strive to identify and account for confounding variables in the experimental design and often use statistical controls or matched sampling.
• Benefits: Recognising confounders enhances the study’s internal validity by helping clarify relationships.
• Limitations: Confounders can be difficult to detect, and even minor confounding effects can significantly skew results, potentially leading to erroneous conclusions. Complex studies may require advanced statistical techniques to control for confounding.
• Examples
  • Baseline health status in a study on exercise effects on weight, as it influences both activity levels and weight loss potential
  • Socioeconomic status in educational outcomes research, as it can influence both access to resources and academic performance
  • Stirring speed in a solubility experiment, affecting both temperature distribution and solubility
  • Sleep quality in a study on screen time and focus, as it could affect both screen usage patterns and cognitive performance

Examples of variables in scientific experiments

Analysing variables in science

Analysing variables involves several steps to determine relationships, assess trends and ensure data reliability. Here is an approach to analysing different types of variables:

1. Define variables clearly

First, define each variable (independent, dependent, controlled, extraneous, and confounding) clearly, as their precise identification ensures focused analysis. For instance, specify the unit of measurement for the dependent variable and the levels or categories for independent variables.

2. Choose appropriate statistical tests

The nature of each variable (e.g. categorical, continuous) determines which statistical tests to use:

• Independent and dependent variables: To assess the relationship between these, choose tests based on the variable types:
  • For continuous dependent variables and categorical independent variables, use t-tests or ANOVA.
  • For continuous variables, consider correlation or regression analysis to explore linear relationships.
  • For categorical variables, use chi-square tests to examine associations.
• Controlled variables: Verify that these variables remained constant by comparing their values across groups or conditions. Descriptive statistics (mean, standard deviation) can indicate if they were indeed controlled effectively.

3. Check for confounding variables

Identify and control for potential confounding variables by using statistical techniques:

• Stratification divides data into subgroups based on confounders to observe differences within each subgroup.
• Multivariable regression analysis allows researchers to adjust for confounders and observe the independent impact of the primary variable.
• Matching helps balance confounders by pairing participants or cases with similar confounding characteristics across groups.

4. Control extraneous variables

Extraneous variables can add noise to data, so minimise their impact by standardising conditions across tests. Measure the effect size (e.g. Cohen’s d) to understand the independent variable’s impact compared to the variation caused by extraneous influences.

5. Use descriptive statistics

Calculate means, medians, and standard deviations to summarise data for each variable. Visual tools like histograms, box plots, and scatter plots provide insights into data distribution, trends and outliers.

6. Validate results

Verify the reliability of your findings by performing sensitivity analyses or testing alternative variable classifications. Cross-validation (using subsets of data to check consistency) and replicating experiments in different conditions can confirm results.

7. Report limitations

After analysis, report any uncontrolled confounders or extraneous factors and potential impacts. Discussing limitations ensures transparency and guides future research.

Scientific variables in academic writing

Academic texts that commonly include scientific variables focus on empirical research, experiments and data analysis. Here are the main types:

• Research articles present original research with clear definitions of independent, dependent and controlled variables. They include methodology sections describing variable manipulation, measurement and control, along with statistical analysis of results.
• Systematic reviews and meta-analyses synthesise findings from multiple studies and often include discussions of scientific variables used across studies to compare outcomes. They may highlight common variables in a field (e.g. common predictors of academic performance in educational psychology) and analyse how different studies handle them.
• Experimental reports are often part of laboratory courses in academic settings. These reports focus on hands-on research where variables are manipulated, controlled and measured. They are typically structured with sections on methodology, results and discussion of variables.
• Methodology textbooks explain how to design and conduct research, which may include defining, controlling and analysing variables. They provide foundational guidance on identifying and operationalising variables across various research contexts.
• Theses and dissertations identify and justify the scientific variables that students examine in their studies. Methodology chapters in these works explain variable choices, control measures and statistical analysis methods used.
• Statistical analysis books cover techniques for analysing different types of variables (categorical, continuous, ordinal). They often include examples of how to handle variables in fields like biology, psychology and social sciences.

Editing services for academic writing

Professional editing services enhance academic texts by improving structure, clarity, coherence and adherence to academic standards. Here is how different editing stages — developmental editing, line editing, copyediting and proofreading — contribute to preparing texts involving scientific variables for publication or submission:

Clarity of research design

Developmental editing enhances the clarity of research design by ensuring variables are well-integrated into the research questions, hypotheses and methods. Developmental editors help researchers articulate the role and relevance of each variable—independent, dependent, controlled, extraneous and confounding. They may suggest additional context or recommend removing unnecessary elements that detract from the focus. This process makes the research design clear, logical and aligned with the study’s purpose.

Sentence-level flow and readability

Line editing focuses on sentence structure and wording, improving the text’s readability and clarity. For texts involving scientific variables, line editors rephrase sentences for clarity, smooth transitions and improve overall flow, ensuring complex scientific concepts are presented in accessible language. They may adjust sentence length, remove jargon and clarify definitions, making the discussion of variables more concise and understandable to readers. This stage strengthens readability and engagement, especially for complex research studies.

Technical and stylistic consistency

Copyediting ensures technical accuracy, style consistency and adherence to academic standards. For scientific texts, copyeditors standardise terminology, ensure proper variable notation and check for consistency in variable definitions and usage throughout. They verify adherence to citation and formatting standards, ensuring compliance with journal or academic guidelines. Copyediting corrects minor grammatical issues, which enhances the text’s professionalism and reliability.

Final polish

Proofreading is the final quality check, focusing on eliminating typographical errors, grammatical mistakes and formatting inconsistencies. Proofreaders review all aspects of the text, ensuring that tables, figures and variable names are accurate and consistent with the main content. This last step ensures precision in how variables are presented and prepares the manuscript for submission without distractions from minor errors.

Resources

Here is a list of resources helpful when working with variables in science.

• Designing and Conducting Research by Clifford J. Drew, Michael L. Hardman and John L. Hosp
• Experimental and Quasi-Experimental Designs for Generalized Causal Inference by William R. Shadish, Thomas D. Cook and Donald T. Campbell
• Research Design: Qualitative, Quantitative, and Mixed Methods Approaches by John W. Creswell and J. David Creswell
• Research in Action podcast by Oregon State University
• SAGE Research Methods
• Stats+Stories podcast
• The Craft of Research by Wayne C. Booth, Gregory G. Colomb, and Joseph M. Williams

Key takeaways

Variables in science enable researchers to explore cause-and-effect relationships by manipulating independent variables, observing changes in dependent variables, and controlling constant factors. Understanding controlled, extraneous and confounding variables helps minimise biases and external influences, ensuring more accurate results. Therefore, understanding variables in science is key to producing robust, reliable research.

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