Quantitative ResearchAt the other e

Quantitative Research
At the other end of the research design spectrum lies quantitative research, which typically tests theories by examining the relationships between variables. Therefore, quantitative is generally deductive; in other words, it is typically used to test a theory statistically rather than to build theory (as is commonly the case with qualitative research). variables in quantitative are usually measured by means of instruments that produce numerical data for statistical analysis. Quantitative research is often aimed at using data from a sample (often collected via closed-ended questions) to generalize to a larger population, and it often stresses objectivity and replicability. Other strengths of quantitative research include precise measurement of variables, the ability to ststistically test research questions and hypotheses, and opportunity to longitudinally measure subjects’ subsequent performance. Quantitative designs can be experimental, quasi- experimental, or non experimental. True experimental designs randomly assign subjects to either an experimental group or a control group. Examples include the posttest / post-test control group design, the post-test-only control group design, and the Solomon four-group design. In the pretest / post-test control group design, both the experimental and control groups are given a pretest, but only the experimental group receives the treatment. After the treatment has treatment has been administered, a post-test is given to both groups, and resulting differences between the experimental and control groups are attributed to the treatment. For example, two groups of randomly assigned subjects are given a strength test (e.g., the bench press). The experimental group is taught a new conditioning technique, whereas members of the control group work out as they normally do. If the experimental group performs better on the post-test after 6 weeks of the new workout regimen, the researcher could conclude that there is evidence to support the effectiveness of new conditioning technique. The post-test-only control group design works in similar fashion except that no pretest is administered. Another group of designs, termed pre experimental designs, do not assign subjects randomly to groups and are therefore considered weaker than group pretest / post-test, and static group comparison designs. In one-shot case studies, subjects in a single group are introduced to a treatment or condition, than observed for changes that are attributed to the treatment. In a one-group pretest / posttest design, subjects are given a pretest, then exposed to a treatment, and finally given a post-test. In a static group comparison, experimental and control groups undergo the testing procedure, but only the control group is exposed to the treatment. Due to practical constraints, quasi-experimental designs do not randomly select select subjects or assign subjects to groups. These approaches include the time series design, multiple-time-series design, and non equivalent control group design. The time series design expands the one-group pretest / post-test design by adding multiple pretests and post-tests to provide greater control over extraneous variables by minimizing the history effect (i.e., the potential impact of major events that occur between testing periods distorting the relationship between independent and dependent variables). The multiple-time-series design merely adds a control group to the time-series design. The nonequivalent control group design mimics the pretest / posttest control group design except that subjects are not randomly assigned to the two groups. This and other quasi-experimental designs are commonly used in sport management and related disciplines sport management research often involves intact groups of subjects, thus eliminating the opportunity to randomly assign subjects.

ผลลัพธ์ (ไทย) 1:

การวิจัยเชิงปริมาณAt the other end of the research design spectrum lies quantitative research, which typically tests theories by examining the relationships between variables. Therefore, quantitative is generally deductive; in other words, it is typically used to test a theory statistically rather than to build theory (as is commonly the case with qualitative research). variables in quantitative are usually measured by means of instruments that produce numerical data for statistical analysis. Quantitative research is often aimed at using data from a sample (often collected via closed-ended questions) to generalize to a larger population, and it often stresses objectivity and replicability. Other strengths of quantitative research include precise measurement of variables, the ability to ststistically test research questions and hypotheses, and opportunity to longitudinally measure subjects’ subsequent performance. Quantitative designs can be experimental, quasi- experimental, or non experimental. True experimental designs randomly assign subjects to either an experimental group or a control group. Examples include the posttest / post-test control group design, the post-test-only control group design, and the Solomon four-group design. In the pretest / post-test control group design, both the experimental and control groups are given a pretest, but only the experimental group receives the treatment. After the treatment has treatment has been administered, a post-test is given to both groups, and resulting differences between the experimental and control groups are attributed to the treatment. For example, two groups of randomly assigned subjects are given a strength test (e.g., the bench press). The experimental group is taught a new conditioning technique, whereas members of the control group work out as they normally do. If the experimental group performs better on the post-test after 6 weeks of the new workout regimen, the researcher could conclude that there is evidence to support the effectiveness of new conditioning technique. The post-test-only control group design works in similar fashion except that no pretest is administered. Another group of designs, termed pre experimental designs, do not assign subjects randomly to groups and are therefore considered weaker than group pretest / post-test, and static group comparison designs. In one-shot case studies, subjects in a single group are introduced to a treatment or condition, than observed for changes that are attributed to the treatment. In a one-group pretest / posttest design, subjects are given a pretest, then exposed to a treatment, and finally given a post-test. In a static group comparison, experimental and control groups undergo the testing procedure, but only the control group is exposed to the treatment. Due to practical constraints, quasi-experimental designs do not randomly select select subjects or assign subjects to groups. These approaches include the time series design, multiple-time-series design, and non equivalent control group design. The time series design expands the one-group pretest / post-test design by adding multiple pretests and post-tests to provide greater control over extraneous variables by minimizing the history effect (i.e., the potential impact of major events that occur between testing periods distorting the relationship between independent and dependent variables). The multiple-time-series design merely adds a control group to the time-series design. The nonequivalent control group design mimics the pretest / posttest control group design except that subjects are not randomly assigned to the two groups. This and other quasi-experimental designs are commonly used in sport management and related disciplines sport management research often involves intact groups of subjects, thus eliminating the opportunity to randomly assign subjects.

ผลลัพธ์ (ไทย) 3:

การวิจัยเชิงปริมาณในส่วนอื่น ๆของการวิจัยการออกแบบการวิจัยเชิงปริมาณคลื่นความถี่อยู่ ซึ่งโดยปกติจะทดสอบทฤษฎีโดยการตรวจสอบความสัมพันธ์ระหว่างตัวแปร ดังนั้นโดยทั่วไปเป็นแบบนิรนัยเชิงปริมาณ ; ในคำอื่น ๆมันมักจะใช้ในการทดสอบทฤษฎีทางสถิติมากกว่าที่จะสร้างทฤษฎี ( เป็นกรณีปกติกับการวิจัยเชิงคุณภาพตัวแปรเชิงปริมาณ มักจะถูกวัดโดยวิธีการของเครื่องดนตรีที่ผลิตข้อมูลตัวเลขเชิงสถิติ การวิจัยเชิงปริมาณมักมุ่งการใช้ข้อมูลจากตัวอย่างที่ ( มักจะเป็นแบบคำถามปลายปิด ) เพื่อพูดคุยกับประชากรขนาดใหญ่ และมักจะเน้นความเป็นกลาง และ replicability .จุดแข็งของการวิจัยเชิงปริมาณ ได้แก่ การวัดของตัวแปรที่แม่นยำ ความสามารถในการ ststistically ทดสอบคำถามการวิจัยและสมมติฐาน และโอกาสที่จะวัดตามยาววิชาภายหลังการแสดง การออกแบบเชิงปริมาณสามารถทดลอง กึ่งทดลอง หรือ ไม่ทดลองจริงการออกแบบการทดลองสุ่มให้คนทั้งกลุ่มทดลอง และกลุ่มควบคุม ตัวอย่างรวมแบบทดสอบ / โพสต์กลุ่มควบคุมการออกแบบ ทดสอบโพสต์เฉพาะกลุ่มควบคุมการออกแบบ และซาโลมอน สี่กลุ่มออกแบบ ในกลุ่มทดสอบก่อน / หลัง ควบคุมการออกแบบ ทั้งกลุ่มทดลองและกลุ่มควบคุมจะได้รับชุดแต่กลุ่มทดลองได้รับการรักษา หลังการรักษามีการรักษาได้รับบริหารการโพสต์ทดสอบให้กับทั้งสองกลุ่ม และส่งผลให้ความแตกต่างระหว่างกลุ่มทดลองและกลุ่มควบคุมมีการบันทึกการรักษา ตัวอย่าง 2 กลุ่มแบบสุ่มกลุ่มตัวอย่างจะได้รับการทดสอบความแข็งแรง เช่น ม้านั่งกด )