We investigated the scientific reasoning competencies of pre-service science teachers (PSTs) using a multiple-choice assessment. This assessment targeted seven reasoning skills commonly associated with scientific investigation and scientific modeling. The sample consisted of 112 PSTs enrolled in a secondary teacher education program. A latent class (LC) analysis was conducted to evaluate if there are subgroups with distinct patterns of reasoning skills. The analysis revealed two subgroups, where LC1 (73% of the PSTs) had a statistically higher probability of solving reasoning tasks than LC2. Specific patterns of reasoning emerged within each subgroup. Within LC1, tasks involving analyzing data and drawing conclusions were answered correctly more often than tasks involving formulating research questions and generating hypotheses. Related to modeling, tasks on testing models were solved more often than those requiring judgment on the purpose of models. This study illustrates the benefits of applying person-centered statistical analyses, such as LC analysis, to identify subgroups with distinct patterns of scientific reasoning skills in a larger sample. The findings also suggest that highlighting specific skills in teacher education, such as: formulating research questions, generating hypotheses, and judging the purposes of models, would better enhance the full complement of PSTs’ scientific reasoning competencies.
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