Teacher perceptions of children with cancer. Liang, Chiang,...

Teacher perceptions of children with cancer. Liang, Chiang, Chien, and Yeh (2007) tested whether teachers for children with cancer (case) perceived these children similarly to a group of healthy children (control). They measured a variety of social factors, including social withdrawal, somatic complaints, and other social problems. The following table summarizes many of their results using the related-samples $t$ test. $$ \begin{array}{|l|l|l|l|l|} \hline \multirow{2}{*}{\text { Measures }} & \multicolumn{3}{|c|}{} & \multicolumn{2}{|l|}{} \\ \cline { 2 - 3 } & \text { Mean (SD) } & \multirow{2}{*}{\mathrm{t}} & p \text { Value } \\ \cline { 2 - 5 } \text { Withdrawn } & \text { Case } & \text { Control } & \multicolumn{2}{|l|}{} \\ \hline \text { Somatic complaints } & 0.19(2.22) & 0.06(1.24) & 3.85 & <.001 \\ \hline \text { Social problems } & 0.26(3.34) & 0.08(1.32) & 4.31 & <.001 \\ \hline \end{array} $$ a. Is this a repeated-measures design or a matched-pairs design? b. Interpret the results displayed in the table. Do teachers perceive children with cancer as having more social problems than their peers?

Teacher perceptions of children with cancer. Liang, Chiang, Chien, and Yeh (2007) tested whether teachers for children with cancer (case) perceived these children similarly to a group of healthy children (control). They measured a variety of social factors, including social withdrawal, somatic complaints, and other social problems. The following table summarizes many of their results using the related-samples $t$ test.
$$
\begin{array}{|l|l|l|l|l|}
\hline \multirow{2}{*}{\text { Measures }} & \multicolumn{3}{|c|}{} & \multicolumn{2}{|l|}{} \\
\cline { 2 - 3 } & \text { Mean (SD) } & \multirow{2}{*}{\mathrm{t}} & p \text { Value } \\
\cline { 2 - 5 } \text { Withdrawn } & \text { Case } & \text { Control } & \multicolumn{2}{|l|}{} \\
\hline \text { Somatic complaints } & 0.19(2.22) & 0.06(1.24) & 3.85 & <.001 \\
\hline \text { Social problems } & 0.26(3.34) & 0.08(1.32) & 4.31 & <.001 \\
\hline
\end{array}
$$
a. Is this a repeated-measures design or a matched-pairs design?
b. Interpret the results displayed in the table. Do teachers perceive children with cancer as having more social problems than their peers?

Key Concepts

Repeated-Measures Design

A repeated-measures design involves assessing the same participants under different conditions or over time. This design is useful in controlling for inter-individual variability since each participant acts as their own control. It is often used when the same measurement is taken at multiple times from the same subjects to evaluate change or effect due to an intervention.

Matched-Pairs Design

A matched-pairs design is a method of pairing participants in one group with similar participants in another group based on specific characteristics. This design helps control for potential confounding variables by ensuring that the paired groups are comparable, which is critical when examining differences between conditions where random assignment may not be possible.

Related-Samples t Test

A related-samples t test (or paired samples t test) is a statistical method used to compare the means of two related groups or paired observations. This test is appropriate when the measurements are taken from the same subjects under different conditions or when subjects are paired in a controlled manner. It accounts for the dependency between the paired observations, providing more accurate estimates of differences between the groups.

Interpretation of Statistical Results

Interpreting statistical results involves analyzing the t values and p-values to determine if the differences between groups are statistically significant. A low p-value (typically less than .05) indicates that the observed differences are unlikely to be due to chance. In applied settings, such as teacher perceptions, significant results would suggest a meaningful difference in how groups are perceived, which could have practical implications for understanding biases or effects of specific conditions.

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