قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| العينة القصدية التناسبية (Proportional Purposive Sampling)× | أخذ العينات الحصصي× | |
|---|---|---|
| المجال | منهجية المسح | منهجية المسح |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1980s–2000s | 1930s |
| صاحب الطريقة≠ | Derived from purposive sampling tradition (Patton); formalized in mixed-methods literature | Developed in market research and opinion polling, notably applied by George Gallup in the 1930s |
| النوع≠ | Non-probability sampling with proportional allocation | Non-probability sampling design |
| المصدر التأسيسي≠ | Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications. ISBN: 978-0761919711 | Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann. ISBN: 978-0435827496 |
| الأسماء البديلة | proportional criterion sampling, quota-proportional purposive sampling, representational purposive sampling | quota-controlled sampling, quota selection, non-probability quota sampling |
| ذات صلة≠ | 4 | 5 |
| الملخص≠ | Proportional purposive sampling combines the intentional case selection of purposive sampling with proportional allocation across subgroups. Researchers first determine how each meaningful subgroup (e.g., gender, school type, professional role) is represented in the population, then deliberately select participants from each subgroup in those same proportions — using purposive judgment to ensure each selected case is information-rich and relevant to the research question. | Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference. |
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