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Drop it straight into the free Physics IA frame. Research Design is free; unlock the full step-by-step IA — data tables with uncertainty, linearised graphs with error bars and max/min gradients, conclusion and evaluation — to take it to the top band.
Start this IA in the Physics frame →MECHANICS & MOTION
Motion and force experiments give clean linear or linearisable relationships and a gradient you can read a physical quantity straight off.
1 · How does the length of a simple pendulum affect its period of oscillation?
The classic top-band IA: T = 2π√(L/g) squares to T² = (4π²/g)L, so a graph of T² against L is linear and the gradient yields g to compare with 9.81 m s⁻². Rich uncertainty work and a zero-intercept check.
2 · How does the drop height affect the rebound height of a bouncing ball?
A graph of rebound against drop height is linear through the origin; its gradient is the coefficient of restitution squared, linking the result to energy lost per bounce — a clean, quantitative story with error bars.
3 · How does the launch angle affect the range of a projectile?
Tests range ∝ sin(2θ) with a predicted maximum at 45° — a non-linear model you fit and explain, with air resistance giving a genuine systematic shift to evaluate.
4 · How does the mass on a trolley affect its acceleration down a ramp (or under a constant force)?
Plotting a against 1/m linearises Newton's second law F = ma, so the gradient is the applied force — a direct test of F = ma with friction surfacing in the evaluation.
OSCILLATIONS & WAVES
Springs, strings and pendulums linearise neatly, and wave studies give a gradient equal to a speed, frequency or wavelength.
5 · How does the spring constant affect the period of a mass oscillating on a vertical spring?
T = 2π√(m/k) squares to T² = 4π²m·(1/k), so plotting T² against 1/k is linear with gradient 4π²m — a determination of the oscillating mass that demonstrates command of SHM.
6 · How does the length of a vibrating string affect its fundamental frequency?
For a fixed tension f = v/(2L), so a graph of f against 1/L is linear and the gradient gives the wave speed v — and hence the tension via v = √(T/μ) for the evaluation.
7 · How does the tension in a string affect the wave speed along it?
v = √(T/μ) linearises as v² = (1/μ)T, so the gradient gives the mass per unit length μ — a quantity you can independently weigh and measure to check accuracy.
8 · How does the air-column length affect the resonant frequency in a tube (speed of sound)?
Resonant length against 1/f is linear with gradient proportional to the speed of sound, which you compare to the accepted ~343 m s⁻²; the end-correction is a tidy systematic-error discussion.
ELECTRICITY & CIRCUITS
Circuits give precise, repeatable readings and direct linear laws — ideal for tight error bars and a gradient with clear physical meaning.
9 · How does the length of a wire affect its resistance, and what is the resistivity of the metal?
R = ρL/A is already linear, so the gradient ρ/A combined with a measured cross-section gives the resistivity ρ to compare with the data-book value — a model data-rich IA.
10 · How does the temperature of a metal wire (or thermistor) affect its resistance?
A linear R–T trend for a metal lets you extract the temperature coefficient of resistance; a thermistor's exponential fall linearises on a log plot — choose the level of stretch you want.
11 · How does the load resistance affect the terminal voltage of a cell (internal resistance)?
V = ε − Ir is linear in I, so the intercept gives the EMF and the gradient gives the internal resistance r — two quantities from one graph, with neat uncertainty propagation.
12 · How does light intensity (distance from a lamp) affect the output of a solar cell or LDR?
Tests the inverse-square law: plotting output against 1/d² should be linear, turning a familiar idea into a quantitative, graphable investigation with a clear gradient.
Ready to write it up properly?
The Physics IA frame walks you through every criterion — and the paid unlock builds your data tables, linearised graphs and evaluation into one export-ready document.
Open the Physics IA frame →THERMAL PHYSICS
Heating and cooling experiments are cheap, data-rich and link straight to specific heat capacity, latent heat or an exponential law.
13 · How does the temperature difference affect the rate of cooling of water (Newton's law of cooling)?
Cooling is exponential, so a graph of ln(ΔT) against time linearises to a straight line whose gradient is the cooling constant — sophisticated log-linear processing the examiner rewards.
14 · How does the mass of water affect the time to heat it through a fixed temperature rise (specific heat capacity)?
E = mcΔT is linear in m, so the gradient cΔT gives the specific heat capacity c to compare with 4180 J kg⁻¹ K⁻¹; heat loss to the surroundings drives a strong evaluation.
15 · How does the insulation thickness affect the rate of heat loss from a hot container?
Rate of heat loss falls with thickness in a predictable way (rate ∝ 1/thickness for conduction), giving a linearisable trend and a real-world energy-efficiency hook.
16 · How does the pressure of a fixed mass of gas vary with temperature (absolute zero)?
P ∝ T is linear, and extrapolating the P–T line back to zero pressure estimates absolute zero in °C — a striking result to compare with −273 °C with full uncertainty.
FIELDS & MISC — OPTICS, MAGNETISM, FLUIDS
Optics, magnetism and fluid experiments round out the syllabus with relationships that linearise and gradients with clear meaning.
17 · How does the angle of incidence affect the angle of refraction in a glass block?
Snell's law n = sinθ₁/sinθ₂ linearises as sinθ₁ = n·sinθ₂, so the gradient is the refractive index — comparing to the accepted ~1.5 for glass gives a built-in accuracy check.
18 · How does the object distance affect the image distance for a converging lens (focal length)?
The thin-lens equation 1/v = −1/u + 1/f is linear in 1/u, so the intercept gives 1/f and the focal length f directly — a tidy two-quantity-from-one-graph IA.
19 · How does the distance between two magnets affect the force between them?
The force falls steeply with distance; a log–log plot linearises F ∝ d⁻ⁿ and the gradient gives the power n, which you compare to the expected dipole value — high-level processing.
20 · How does the number of coils in an electromagnet affect its magnetic strength?
Field strength rises linearly with turns (B ∝ N for fixed current and length), so a B–N graph is linear with a meaningful gradient and an accessible, controllable setup.
21 · How does the terminal velocity of a sphere depend on its radius in a viscous liquid?
Stokes' law gives terminal velocity ∝ r², so plotting v against r² is linear and the gradient yields the liquid's viscosity — a rich determination with a clear systematic error to weigh.
22 · How does the depth of liquid affect the time for it to drain through a small hole (Torricelli)?
Torricelli's law v = √(2gh) linearises as v² = 2g·h, so plotting v² against h gives a straight line of gradient 2g — another independent route to determining g.
23 · How does the surface area of a parachute affect the terminal velocity of a falling mass?
At terminal velocity drag balances weight, giving v² ∝ 1/A; the linearised plot tests the drag model and its gradient packages air density and the drag coefficient.
24 · How does the activity of a radioactive source (or count rate) vary with absorber thickness?
Attenuation is exponential, so a graph of ln(count rate) against thickness linearises to a straight line whose gradient is the absorption coefficient — sophisticated log-linear analysis where apparatus allows.
From a topic to a top-band IA
An idea is the easy part — the marks are in how you build it. The Physics IA is scored out of 24 across four equal criteria: Research Design, Data Analysis, Conclusion and Evaluation. Whichever topic you pick, the same moves win: a focused research question with named variables and the governing equation, a method developed through trials, data processed with absolute and percentage uncertainty, a linearised graph with error bars and max/min gradients, a conclusion that extracts a constant and is justified against the accepted value, and an evaluation that weighs your random and systematic errors and proposes realistic improvements and extensions.
Build your chosen idea into a full IA
The examiner-written Physics IA writing frame takes you through every section with the rubric, worked examples and the traps that cost marks. Research Design is free — unlock Data Analysis, Conclusion & Evaluation to finish the whole investigation and export it to Word or PDF.
Open the Physics IA frame →Physics IA ideas — FAQ
What makes a good IB Physics IA topic?
A clear physical relationship you can write as an equation, a clearly named independent variable with range and units that you can manipulate, a dependent variable with how it's measured, feasibility with school apparatus, and enough quantitative data to graph with uncertainties — ideally a relationship that linearises so a gradient yields a physical quantity such as g, the resistivity or a wave speed. Phrase it as "How does … affect …?".
How many data points, and do I need error bars?
At least five values of the independent variable across a wide range, each repeated at least three times so you can take a mean and judge reliability. Give every measured quantity an absolute uncertainty, propagate it through your calculations, and plot it as error bars so you can draw max and min gradient lines and quote the gradient as m ± Δm.
Can I just copy one of these ideas?
Use them as a launchpad, but make the investigation your own: narrow the research question, choose your own variable ranges, and develop the method through your own trials. That ownership is exactly what the Research Design and Evaluation criteria reward.
How do I turn the idea into a top-band IA?
Build it section by section in the free Physics IA writing frame — research question and governing equation, variables, method, data with propagated uncertainty, a linearised graph with error bars and max/min gradients, a conclusion that extracts a constant against the accepted value, and an evaluation with realistic improvements and extensions.
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