Do you have students who struggle with CER (Claim, Evidence, Reasoning) statements? I give a lot of CER examples on this site (Refer to #4 and #46), but, sometimes, examples are not enough. Some students still need a step-by-step process to writing CER statements. This is especially true when writing CER statements are based on lab data (where the answers may not come from a textbook). So, how do we write CER statements? What step-by-step process can we follow?

In this post, we give our process of writing CER statements, which actually doesn’t start with the claim. It starts with the evidence. The evidence is the glue that holds everything together. Therefore, analyzing evidence is our focus for this post (although there will be a quick blurb about reasoning at the end too). Handouts are available for download at the end of this post.

Writing CER statements Step-by-Step

To be honest, when writing CER statements, it starts with the question. Evidence (ie experimental data and observations) can provide researchers with a lot. But, only the evidence that gets us an answer to the question at hand is important. Hence, what students focus on must always relate to the question.

Now, with regards to analyzing evidence to come up with a CER statement, students can look for the following:

I. Similarities or Differences in results (between control and trial variables)

Some research questions ask whether a trial variable (ie. Drug, chemical, scientific process) has an effect on an outcome. To answer such a research question, students need to look at similarities (or differences) in the data.

In a nutshell, if results between a trial variable and control are the same, then we claim the trial variable does not have an effect on the outcome. If the results differ, then we claim the trial variable does have an effect (whether positive or negative) on the outcome.

Consider the following example, where researchers tracked the amount of milk that was wasted when chocolate milk was sold in the school cafeteria and when chocolate milk wasn’t sold.

The graph above shows two conditions with different results. When chocolate milk is not available, the amount of milk waste goes up. Thus, researchers can claim that students waste more milk when only white milk is offered.

A similar research question may compare a whole bunch of different trial variables (ex. Different drugs) to see which has a better effect on the experimental outcome.

If the results between trial variables are the same, then we claim the trial variables with similar results have a similar effect on the outcome. If the results differ, then we claim the trial variables differ on their effect on the outcome (and we can explain how they differ too).

II. Trends

Some research questions ask for the effect of increasing or decreasing a single variable has on an experimental outcome. In these cases, we look for trends in the data.

For example, if the dependent variable increases in response to increases to the independent variable, then we claim there is a positive relationship or trend between the independent and dependent variables.

If the dependent variable decreases in response to increases to the independent variable (or vice versa), then we claim there is a negative relationship or trend between independent and dependent variables.

Consider the following example, where researchers tracked how like individuals were to smoke cigarettes f they were exposed to scenes of cigarette smoking in movies.

According to the graph above, as the exposure to smoking scenes increased (ie. MSE Quintile), the likelihood of individuals smoking cigarettes afterwards (ie. smoking prevalence) increased too.

III. Maximums or Minimums

Some research questions ask for the minimum or maximum effect of an independent (ie. trial) variable. Or, alternatively, the question asks for the conditions where the minimum or maximum effect is observed. In these cases, we look for maximums or minimums in the data.

When looking at a graph, finding maximums or minimums is done through interpolation. For a quick guide to interpolation, check out post #23. The claim we write (ie. where the minimum or maximum effect occurs, or what minimum or maximum conditions produce a certain effect) depends on what we interpolate.

What about Reasoning when writing CER statements?

The Reasoning part of a CER statement is meant to explain the evidence and claim. For example, if we claim “Red Jellybeans are the best type of jellybean” – and our evidence shows that red jellybeans are purchased by more people than any other colour – then our reasoning needs to explain why jellybeans are purchased by more people. Some reasons could be scientific (perhaps our eyes are conditioned to see red because it represents danger) or social (red is the colour of love, and of course, there’s Valentine’s Day). However, some reasons may lead to new hypotheses or questions. Thus, a reason may not be a certain thing (although some reasons may already be proven fact). Both are valid.

Wrap Up

When it comes to writing CER statements, there are many ways one can learn. Sure, it helps to look at examples. It helps to practice (if you’re looking for practice, refer to post #24 and #28). But, sometimes students need or prefer a step-by-step solution. And that solution needs to start with looking at evidence (and seeing how it connects to the question). Click the link below to download a summary of this post. Also, please share our resources with your colleagues and sign up for our newsletter if you want to receive weekly factoids and updates.

Until next time, keep it REAL!

Resources

Handout(s): 48 – Writing CER Statements Cheat Sheet

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