After develop a 'ODK' <https://opendatakit.org/> frame, we can link the frame to 'Google Sheets' <https://www.google.com/sheets/about/> and collect data through 'Android' <https://www.android.com/>. This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'.

OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. 
    In the first layer of the hierarchy, the clusters are separated automatically based on the variation 
    of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy 
    corresponds to the sub clustering process of small clusters based on the distance between the origin 
    and destination of each cluster.

Calculate the optimal sample size allocation that 
    uses the minimum resources to achieve targeted statistical 
    power in experiments.
    Perform power analyses with and without accommodating 
    costs and budget. The designs cover single-level 
    and multilevel experiments detecting main, mediation, 
    and moderation effects (and some combinations). 
    The references for the proposed methods include: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>.
    (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
    three-level multisite cluster-randomized trials. 
    Journal of Research on Educational Effectiveness, 15 (1), 130-150.
    <doi:10.1080/19345747.2021.1953200>.
    (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
    multisite randomized trials. The Journal of Experimental Education,
    90(3), 693-711. <doi:10.1080/00220973.2020.1830361>.
    (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). 
    Using ant colony optimization to identify optimal sample 
    allocations in cluster-randomized trials. 
    The Journal of Experimental Education, 93(1), 167-185.
    <doi:10.1080/00220973.2024.2306392>.
    (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and 
    optimal design for randomized controlled trials investigating 
    mediation effects. Psychological Methods.
     <doi:10.1037/met0000698>.
    (6) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.