A null hypothesis states that there is no significant relationship or difference between two or more variables, serving as a baseline for statistical testing.
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Applied research often tests and refines existing theories in practical settings, thereby contributing to their development and validation by addressing real-world problems.
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Explanation: Random sampling is a technique in which every member of the population has an equal chance of being selected. It helps reduce sampling bias and improves the representativeness of the sample, making research findings more reliable and generalizable.
Why Experimental Research?
Note: Although random sampling can be used in many types of research, it is considered especially important in experimental research. In contrast, action research usually involves a specific classroom or local setting and commonly uses purposive or convenience sampling rather than random sampling.
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The pattern method, when viewed as a top-down approach, involves confirming expected patterns or relationships derived from a general theory or hypothesis.
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A Type-I error occurs when a true null hypothesis is incorrectly rejected, meaning a researcher concludes there is an effect when none actually exists.
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The inductive method is a bottom-up approach where researchers observe specific instances to develop broader generalizations, theories, or patterns.
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A variable is a condition or characteristic that can take on different values or categories, allowing for measurement and analysis in research.
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The variable that is presumed to cause a change in another variable is called an independent variable.
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A variable that is presumed to cause a change in another variable is called a(n):Read More »
Experimental research is fundamentally defined by the researcher's manipulation of the independent variable to observe its effect on the dependent variable.
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A positive correlation exists when two variables move in the same direction, meaning as one increases, the other also increases, or both decrease together.
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Mixed method research integrates both qualitative and quantitative paradigms in different phases to provide a more comprehensive understanding of the research problem.
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Historical research systematically investigates past events to understand their causes, effects, and significance, offering context for present-day issues.
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Inductive reasoning begins with specific observations and data, identifies patterns, and then progresses towards forming broader generalizations or theories.
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Qualitative research prioritizes in-depth understanding of specific contexts and experiences, focusing on rich detail rather than generalizability to larger populations.
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The nominal scale is the simplest measurement level, categorizing data without any inherent order or numerical value, merely distinguishing groups.
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Content validity assesses whether a test adequately covers all relevant aspects of the construct being measured, unlike reliability which focuses on consistency.
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Stevens' four levels of measurement, from least to most complex, are nominal, ordinal, interval, and ratio, each building upon the properties of the previous level.
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