Research (Practicle, Real-World) Quiz # 6

Instructions
Quiz: Research (Practicle, Real-World) Quiz # 6
Subject: Preparing Quantitative Research Data
Total Questions: 30 MCQs
Time: 30 Minutes

Note

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  • Results along with correct answers will be shown at the end of the test.
Research (Practicle, Real-World) Quiz # 6
Question 1 of 30
00:00
  • _________ is in a codebook, this is usually the full-working question that corresponds to a piece of data, such as a full survey question.

  • ________ is usually a shortened name given to a piece of information, such as a survey question, that is used for computer identification of specific pieces of data.

  • Statistical comparisons that examine how two variables relate to each other is known as ?

  • Statistical test that summarizes the typical sample response to a variable.

  • Statistics that summarize or compare patterns in sample data without trying to relate those patterns to the wider population is known as ?

  • Statistical tests that summarize the spread of a distribution of sample responses to a variable.

  • Frequency is a graphical representation of the distribution of data that is commonly used to determine whether a distribution is normally distributed or not.

  • Inferential statistics are statistical tests that relate observed sample data to observed population characteristics.

  • Comparisons that involve two variables is known as Multivariate

  • Normal distribution is also known as the______.

  • _________ is a linear graphical representation of where data should be if the distribution is normal compared to what data actually look like.

  • _______ is a way to summarize and to compare patterns in information observed in data.

  • _________is in multivariate analyses, these are variables other than the main independent variable(s) of interest that might affect the dependent variable and threaten validity if not included in the model.

  • Statistical significance is a threshold, usually a minimum of ______that helps determine whether an observed result is likely because of chance (hence the null hypothesis cannot be rejected) or a phenomenon of interest.

  • Summarizing or examining two variables without relating it to other variables are known as Univariate.

  • In hypothesis testing, this cutoff point for which a researcher considers an insufficiently improbable value that is the outcome of the null region is known as Alpha level?

  • Degrees of freedom is the number of observations of data that are free to vary before constraints are violated when ______statistical parameters.

  • __________ statistical tests that relate observed sample data to unobserved population characteristics.

  • _________ is in hypothesis testing, this refers to when the critical area to reject the null is either in the right or left tail of the distribution, but not both.

  • The singular value in a probability sample that is obtained to approximate the population parameter

  • Short for probability of error value is known as ?

  • The part of distribution that is furthest from the mean is known as ?

  • T-distribution is a probability distribution used to estimate population parameters when the sample size is large and/or the population variance is unknown.

  • In hypothesis testing, this refers to when the critical area to reject the null can be in the right or left tail of the distribution is known as Two-tailed test.

  • Z- distribution is otherwise known as the normal distribution, this distribution helps find probabilities and percentiles of small , discontinuous , randomly selected data.

  • _________ samples in which being in one sample or having specific sample characteristics is necessary (dependent) for being part of the second sample, such as in before/after experimental designs and matched samples.

  • In the chi-square statistical formula, this is the approximate number of observations we would expect to see in each cell if the one variable was dependent, meaning unrelated is known as Expected frequency

  • When two or more samples have similar variances across the sample is known as Homogeneity of variances.

  • When two or more samples have similar variances across the sample is known as Homogeneity of variances.

  • When being in one sample means that you are not in the second sample is known as Independent samples.

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