Quantitative Techniques with Small Sample Sizes: An Educational Summer Camp Example
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| Title: | Quantitative Techniques with Small Sample Sizes: An Educational Summer Camp Example |
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| Language: | English |
| Authors: | Trina Johnson Kilty (ORCID |
| Source: | Problems of Education in the 21st Century. 2024 82(4):507-520. |
| Availability: | Scientia Socialis Ltd. 29 K. Donelaicio Street, LT-78115 Siauliai, Republic of Lithuania. e-mail: scientia@scientiasocialis.lt; e-mail: problemsofeducation@gmail.com; Web site: http://www.scientiasocialis.lt/pec/ |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | High Schools Secondary Education |
| Descriptors: | Summer Programs, Camps, Computer Science Education, 21st Century Skills, Sample Size, Evaluation Methods, Probability, College Bound Students |
| ISSN: | 1822-7864 2538-7111 |
| Abstract: | A computer science camp for pre-collegiate students was operated during the summers of 2022 and 2023. The effect the camp had on attitudes was quantitatively assessed using a survey instrument. However, enrollment at the summer camp was small, which meant the well-known Pearson's Chi-Squared to measure the significance of results was not applied. Thus, a quantitative analysis method using a multinomial probability distribution as a model of a multilevel Likert scale survey was used. Exact calculations of a multinomial probability model with likelihood ratio were performed to quantitatively analyze the results of questionnaires administered to participants in two cohort groups (combined N=17). Probabilities per Likert categories were determined from the data itself using Bayes theorem with a Dirichlet prior. Each cohort functioned as part of a homogenous sample, thus allowing cohorts to be pooled. Post-test revealed significant changes in participants' attitudes after camp completion. Using this technique has implications for studies with small sample sizes. Using exact calculation of the multinomial probability model with the use of likelihood ratio as a statistical test of evidence has advantages: a) it is an exact value that can be used on any size sample, although it offers a quantitative analysis option for small sample size studies; b) depends only on what was observed during a study; c) does not require advanced calculation; d) modern spreadsheet and statistical package programs can calculate the analysis; and e) likelihood ratio employed in Bayes theorem can update prior beliefs according to evidence. Utilizing small sample size quantitative analysis can strengthen insights into data trends and showcase the importance of this quantitative technique. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1435736 |
| Database: | ERIC |
| Abstract: | A computer science camp for pre-collegiate students was operated during the summers of 2022 and 2023. The effect the camp had on attitudes was quantitatively assessed using a survey instrument. However, enrollment at the summer camp was small, which meant the well-known Pearson's Chi-Squared to measure the significance of results was not applied. Thus, a quantitative analysis method using a multinomial probability distribution as a model of a multilevel Likert scale survey was used. Exact calculations of a multinomial probability model with likelihood ratio were performed to quantitatively analyze the results of questionnaires administered to participants in two cohort groups (combined N=17). Probabilities per Likert categories were determined from the data itself using Bayes theorem with a Dirichlet prior. Each cohort functioned as part of a homogenous sample, thus allowing cohorts to be pooled. Post-test revealed significant changes in participants' attitudes after camp completion. Using this technique has implications for studies with small sample sizes. Using exact calculation of the multinomial probability model with the use of likelihood ratio as a statistical test of evidence has advantages: a) it is an exact value that can be used on any size sample, although it offers a quantitative analysis option for small sample size studies; b) depends only on what was observed during a study; c) does not require advanced calculation; d) modern spreadsheet and statistical package programs can calculate the analysis; and e) likelihood ratio employed in Bayes theorem can update prior beliefs according to evidence. Utilizing small sample size quantitative analysis can strengthen insights into data trends and showcase the importance of this quantitative technique. |
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| ISSN: | 1822-7864 2538-7111 |
