Random sampling is a crucial research technique that provides every individual in a population an equal chance of being selected for a study. It's key for creating representative samples, minimizing bias, and accurately reflecting population characteristics. The text explores different methods like simple, systematic, stratified, and cluster sampling, and discusses variables, sample size, and sampling variability in the context of research methodology.
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1
To accurately reflect the traits of all college women in a study on depression, researchers may ______ select a sample of ______ from the total population.
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2
Effect of non-random sampling on study results
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3
Consequence of selection bias in sampling
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4
Random sampling's role in population representation
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5
In ______ sampling, individuals are chosen at regular intervals from a sorted list.
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6
______ sampling involves dividing the population into groups based on certain traits and then randomly selecting from those groups.
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7
Stratified random sampling definition
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8
Stratified vs. simple random sampling
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9
In ______, countable categories like the number of offspring in a household are known as ______ variables.
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10
Variables that can assume any value within a certain interval, like ______ or ______, are termed ______ variables.
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11
Effect of inherent variability on sampling
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12
Purpose of random sampling in research
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13
______ sampling is particularly beneficial for reflecting the variety in a ______ population.
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