10.4 Scientific Investigations: Types, Variables, Controls & Data

Scientific Investigations: Types, Variables, Controls & Data

Subtest 603 expects you to differentiate between the characteristics of experiments (multiple trials, control groups, variables) and other types of scientific investigations (such as observations and surveys), and to apply science and engineering practices through the integrated process skills. This section covers investigation types, hypotheses, variables, controls, repetition and replication, data collection, and data displays.

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Types of Scientific Investigations

Not every scientific question can be answered with a controlled experiment. Scientists choose the investigation type that fits the question:

Only a controlled experiment can show that one factor causes a change. Observational and survey studies can reveal patterns and correlations, which often lead to new testable questions.

The Integrated Process Skills

Elementary science builds from basic process skills (observing, classifying, measuring, predicting, inferring, communicating) to integrated skills (hypothesizing, identifying and controlling variables, designing and carrying out investigations, interpreting data, developing and using models, and constructing explanations).

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Rigorous Experimental Design: Variables, Controls & Trials

Authentic science investigations in elementary classrooms require deliberate structural design. Students must progress from informal observations to structured, controlled experiments.

Formulating Testable Hypotheses

A scientific hypothesis is a tentative, testable, and falsifiable proposition explaining an observed phenomenon. In elementary education, teachers teach students to formulate hypotheses using the structured If-Then-Because template: $\text{"If [independent variable is changed], then [dependent variable will respond], because [scientific principle]."}$ Example: "If the water temperature is increased from 20°C to 50°C, then the time required for a sugar cube to dissolve will decrease, because higher thermal energy causes water molecules to move faster and collide with the sugar crystal lattice more frequently."

Identifying and Managing Variables

A variable is any factor, trait, or condition that can exist in differing amounts or types in an experiment. To maintain internal validity, an experiment must rigorously isolate variables:

1. Independent Variable (Manipulated Variable): The single factor deliberately altered or manipulated by the investigator to observe its effect. An experiment must contain only ONE independent variable at a time. If two or more conditions are altered simultaneously, it is impossible to determine which factor caused the observed result, invalidating the experiment.
2. Dependent Variable (Responding Variable): The factor being measured, observed, and recorded in response to changes in the independent variable. The dependent variable depends on the independent variable.
3. Controlled Variables (Constants): All extraneous conditions, environmental factors, and materials that must be maintained completely identical across all groups and trials. Constants guarantee that any observed change in the dependent variable was caused solely by the independent variable.

Experimental Group vs. Control Group

• Experimental Group: The treatment group that receives the specific level or alteration of the independent variable (e.g., plants watered with a 5% fertilizer solution).
• Control Group: The untreated baseline group kept in standard, normal conditions, serving as the benchmark for comparison (e.g., identical plants watered with plain water under identical sunlight, soil, and temperature conditions). Without a control group, researchers cannot establish whether changes would have occurred naturally.

Repetition vs. Replication: Clarifying the Distinction

A critical distinction on educator licensing exams is the difference between repetition and replication:

• Repetition (Multiple Trials within an Investigation): Occurs when the SAME investigator conducts multiple successive trials within the exact same experimental setup. The researcher averages the numerical data across trials (e.g., dropping a parachute 5 times from a height of 2 meters). Repetition minimizes random measurement errors, identifies outlier data, and ensures precision.
• Replication (Verification by Independent Scientists): Occurs when an INDEPENDENT, external researcher reads the published methods of an investigation, recreates the experimental setup with fresh materials, and executes the procedure to determine if the original findings are reproducible. Replication verifies scientific validity, eliminates personal researcher bias, and builds confidence in scientific consensus.

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Qualitative vs. Quantitative Data Collection

Scientific investigations yield two complementary types of empirical data:

• Qualitative Data: Non-numerical, descriptive observations gathered via the human senses (sight, touch, smell, hearing). Examples include noting that a chemical reaction produced vigorous bubbling, an effervescent odor, a color transition from pale yellow to royal blue, or a crystalline texture. Qualitative data is recorded using descriptive narrative logs, field sketches, and observational matrices.
• Quantitative Data: Numerical measurements obtained using calibrated scientific instruments. Examples include recording a mass of 42.6 g, a volume of 18.5 mL, a temperature drop of 3.2°C, or an elapsed time of 14.2 seconds. Quantitative data allows for mathematical modeling, calculation of statistical means, and precise graphical analysis.

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Worked Experimental Design Walkthrough: Elementary Classroom Case Study

To anchor experimental design principles in practice, consider the following comprehensive fifth-grade inquiry scenario:

The Investigation: "The Effect of Surface Incline on Toy Car Velocity"

• Driving Question: How does altering the angle of a wooden ramp affect the velocity of a toy car traveling down the ramp?
• Hypothesis: If the angle of the wooden ramp is increased from 10 degrees to 30 degrees, then the velocity of the toy car will increase, because gravitational acceleration exerts a greater net forward force along the incline plane.
• Independent Variable: The angle of the ramp incline (tested at 10°, 20°, and 30°).
• Dependent Variable: The velocity of the toy car, calculated by dividing the ramp length (1.0 meter) by the elapsed travel time in seconds recorded with a digital stopwatch.
• Controlled Variables (Constants):
  1. The identical wooden ramp (constant surface friction, 1.0-meter track length).
  2. The identical toy car (constant mass of 45.0 grams, identical wheels and axles).
  3. Release method (car held at the top line and released without pushing or downward force).
  4. Environmental conditions (indoor classroom floor, zero wind or ambient air drafts).
• Control Group / Baseline: The minimum functional incline (10°).
• Repetition Protocol: Student teams conduct 5 consecutive trials at each of the three angles (15 total runs), calculating the mean velocity for each angle to minimize reaction-time stopwatch error.
• Replication Protocol: A neighboring student group executes the exact written protocol using an identical car and ramp setup to verify whether their velocity calculations match Team One's published findings.

Interpreting Authentic Data Displays

Elementary educators must guide students to represent quantitative findings through appropriate visual formats:

• Data Tables: Organized with the independent variable in the far-left column and the dependent variable trials, subtotals, and calculated averages in adjacent columns. Headers must clearly specify the variable name and metric units (e.g., Elapsed Time (seconds)).
• Line Graphs: Used when both the independent and dependent variables are continuous quantitative data, or when tracking changes continuously over time. The independent variable is plotted on the horizontal X-axis, while the dependent variable is plotted on the vertical Y-axis.
• Bar Graphs: Used when the independent variable consists of discrete categories (e.g., comparing plant growth across distinct soil types: potting soil, sand, clay, loam).
• Scatter Plots: Used to identify correlational patterns and trend lines between two continuous numerical variables in upper elementary data analysis.

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Comparison Table: Types of Variables in Experimental Design