Journal of K-12 Educational Research 57 The distinguishing features of Q methodology include: • Use of a forced-choice sorting activity in which participants rank-order statements along a defined continuum (e.g., most to least important) • Use of by-person factor analysis to identify clusters of participants who sort the Q-set similarly • Incorporation of post-sort interviews to deepen interpretation and capture explanatory context behind each participant’s ranking decisions. Q methodology is particularly valuable for studies in educational leadership because it recognizes that administrators’ judgments are shaped by professional experience, personal values, and role expectations. It allows researchers to elevate subjective expertise to a level of formal analysis while preserving the depth and nuance of each participant’s voice. Participants and Setting The current study took place in a large urban school district in North Texas, hereafter referred to as “the District.” The District serves a highly diverse student population across more than 40 campuses and includes a range of specialized programming, including self-contained and inclusion-based special education services. Two groups of participants were engaged: • Campus Administrator Participants (P-set): 21 campus-based leaders (14 principals and seven assistant principals) participated in the Q-sort and post-sort interviews. These individuals represented elementary, middle, and high schools. Participants had varying years of leadership experience and included both graduates and non-graduates of the District’s internal leadership development program. • District Leader Focus Group: A separate focus group included five central office leaders with expertise in leadership development and special education. This group consisted of one assistant superintendent, three area superintendents, and one director of special education. This sampling structure aligned with Q methodology’s emphasis on perspectival diversity, not generalizability. In Q studies, participant counts are typically smaller than traditional surveys because the focus is on patterns of meaning, not statistical prevalence. Q-Sort Procedure Participants sorted the 21 competencies using a 7-point forced-distribution grid ranging from -3 (least important) to +3 (most important). The grid was designed to approximate a quasi-normal distribution, encouraging participants to make difficult choices about which competencies they considered most and least critical in relation to supervising special education. The Q-sort process was completed one-on-one with the researcher, either in person or via a secure digital platform. After completing the sort, participants engaged in a semistructured post-sort interview to explain their rationale for high- and low-ranked competencies, reflect on challenges they faced in supervising special education, and identify any perceived gaps in their training. Quantitative Analysis The completed Q-sorts were analyzed using QMethod software, which applies by-person factor analysis to identify clusters of participants who ranked the statements in similar ways. Each factor represents a distinct viewpoint or pattern of shared thinking among participants. Statistical outputs included: • Factor arrays: Idealized Q-sorts for each group • Z-scores: Representing the weighted average rank for each statement within each factor • Factor loadings: Measuring how strongly each participant aligned with a given factor Two factors were retained using eigenvalue thresholds and Humphrey’s rule: • Factor 1: Supportive and Effective Learning Environment • Factor 2: Ethical Leadership and Collaborative School Culture
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