Quick reminder inferential vs descriptive
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Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population itself, such as measures of central tendency, spread, and symmetry.
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Quick reminder inferential vs descriptive
Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population such as central tendency, spread, and symmetry without making broader generalizations.
Quick reminder inferential vs descriptive
Inferential statistics can generalize information from a sample to a larger population, while descriptive statistics do not generalize as they deal directly with the entire population. This document discusses the difference between inferential statistics, which can describe characteristics of a larger population based on a random sample, and descriptive statistics, which only describe the actual characteristics of the sample or population studied without generalization.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unequal intervals between ranks, such as 1st, 2nd, 3rd place. Scaled data has equal intervals between units like speed, height, or weight. Nominal proportional data shows results as a proportion or percentage, like 9 out of 10 doctors surveyed.
What is the Mode?
The document defines the mode as the most frequently occurring observation in a data set. It uses an example data set of [1, 2, 2, 2, 2, 3] to show that the mode is 2, as it occurs most frequently. The mode provides a measure of central tendency but can reveal multiple peaks in a distribution that may not be evident from just the mean or median.
Calculating the mean
The mean is calculated by summing all the data points and dividing by the total number of data points. The document shows an example of calculating the mean exam score of 7 students by adding up all their individual scores, which totaled 245, and then dividing that sum by 7 students to get a mean of 35. The formula for calculating the mean is expressed as the sum of all scores divided by the total number of data points.
Quick reminder levels of the independent variable
This document discusses independent variables and provides examples of variables with different levels. Gender is an independent variable with two levels - Male and Female. Religious Affiliation is an independent variable with four levels - Baptist, Catholic, Mormon, and Muslim. Treatment Status is an independent variable with three levels - Treatment, Control, and Placebo.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unknown intervals between ranks, like percentiles. Scaled data has equal intervals between units, like speed in mph. Nominal proportional data expresses something as a proportion or percentage, like 9 out of 10 doctors surveyed.
Quick reminder nonparametric tests
Nonparametric methods are used to examine sample statistics when representing population parameters for data that is skewed, ordinal, nominal, or ranked. These methods do not assume a specific distribution shape and can handle data represented by numbers or categories instead of continuous measurements. Examples include using percentiles to represent ranked data or assigning numbers to represent categories like nationality.
Recommended
Quick reminder inferential vs descriptive
Inferential statistics generalize information from a random sample to make predictions about an entire population, while descriptive statistics do not generalize because they simply describe characteristics of the entire population such as central tendency, spread, and symmetry without making broader generalizations.
Quick reminder inferential vs descriptive
Inferential statistics can generalize information from a sample to a larger population, while descriptive statistics do not generalize as they deal directly with the entire population. This document discusses the difference between inferential statistics, which can describe characteristics of a larger population based on a random sample, and descriptive statistics, which only describe the actual characteristics of the sample or population studied without generalization.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unequal intervals between ranks, such as 1st, 2nd, 3rd place. Scaled data has equal intervals between units like speed, height, or weight. Nominal proportional data shows results as a proportion or percentage, like 9 out of 10 doctors surveyed.
What is the Mode?
The document defines the mode as the most frequently occurring observation in a data set. It uses an example data set of [1, 2, 2, 2, 2, 3] to show that the mode is 2, as it occurs most frequently. The mode provides a measure of central tendency but can reveal multiple peaks in a distribution that may not be evident from just the mean or median.
Calculating the mean
The mean is calculated by summing all the data points and dividing by the total number of data points. The document shows an example of calculating the mean exam score of 7 students by adding up all their individual scores, which totaled 245, and then dividing that sum by 7 students to get a mean of 35. The formula for calculating the mean is expressed as the sum of all scores divided by the total number of data points.
Quick reminder levels of the independent variable
This document discusses independent variables and provides examples of variables with different levels. Gender is an independent variable with two levels - Male and Female. Religious Affiliation is an independent variable with four levels - Baptist, Catholic, Mormon, and Muslim. Treatment Status is an independent variable with three levels - Treatment, Control, and Placebo.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unknown intervals between ranks, like percentiles. Scaled data has equal intervals between units, like speed in mph. Nominal proportional data expresses something as a proportion or percentage, like 9 out of 10 doctors surveyed.
Quick reminder nonparametric tests
Nonparametric methods are used to examine sample statistics when representing population parameters for data that is skewed, ordinal, nominal, or ranked. These methods do not assume a specific distribution shape and can handle data represented by numbers or categories instead of continuous measurements. Examples include using percentiles to represent ranked data or assigning numbers to represent categories like nationality.
Calculating the Median
The document explains how to calculate the median of a data set. It involves ordering the values from lowest to highest, then finding the middle value. For data sets with an even number of values, the median is the average of the two middle values. Examples are provided to demonstrate calculating the median both conceptually and using an Excel function. A step-by-step formula for calculating the median manually is also outlined.
Identifying predictable patterns
This 1 sentence document discusses predictable patterns in student responses that may not need to be kept unless there is a compelling reason. It suggests eliminating typical student responses that do not provide unique value.
Range
The document defines the concept of range as the difference between the highest and lowest observed values. It provides an example of a data set with values from 1 to 9, where the lowest value is 1 and the highest is 9. The range is calculated by subtracting the lowest value from the highest value, which in this case is 9 - 1 = 8. Therefore, the range is the difference between the maximum and minimum observations.
Missing data item average
If there is a small sample size, the student's response is estimated based on how they answered other similar questions or how other students in their group answered that specific question.
Quick reminder two or more variables
This document discusses variables and their relationships in analyzing different questions. It explains that many questions look at the relationship between two variables, such as gender and driving speed. However, some questions examine the influence of three or more variables, for example how gender, age and driving speed are related. The document also notes that problems may include a covariate variable that is not directly counted if its effect is being eliminated from the analysis.
Sample to sample or sample to population
The document discusses distinguishing between sample to sample and sample to population comparisons when performing z-tests of proportions on nominal proportional data. An example is provided where a marketing team wants to determine if an infomercial claim of 90% customer satisfaction is accurate. A sample of 20 customers finds 15 satisfied, so the problem involves comparing this sample proportion to the claimed population proportion, making it a sample to population comparison.
Quick reminder sample to sample
The marketing team leader asked to determine if a sample of vacuum brand X owners have statistically different satisfaction results of 80% compared to vacuum brand Y owners' satisfaction of 75%. They also want to know if brand X owners' sample satisfaction of 75% is statistically different than the claimed population satisfaction of brand X of 80%. The document provides examples of how to perform a sample to sample and sample to population Z-test comparison of proportions.
Quick reminder nature of the data
This document defines and provides examples of four types of variable relationships: dichotomous by dichotomous, dichotomous by scaled, ordinal by another variable, and scaled by scaled with at least one variable skewed. It explains that dichotomous variables only have two values each, one variable can be dichotomous and one scaled, ordinal variables are rank ordered, and scaled variables have equal intervals with at least one being skewed. Examples involving gender and driving speed are given for each relationship type.
Quick reminder diff-rel-ind-gd of fit
This document defines four statistical concepts: difference compares results between groups, relationship examines how one variable changes with another, independence looks at whether two variables are unrelated, and goodness of fit compares actual results to expectations. Difference deals with comparing groups, relationship looks at how one variable affects another, independence means two factors are independent of each other, and goodness of fit evaluates how actual outcomes align with predicted values.
Quick reminder independent & repeated (t-tests)
This document discusses two types of independent variables: independent samples and repeated samples. Independent samples involve groups that are completely separate from each other, like singles and married people. Repeated samples involve the same group being measured more than once, like students taking a pre-test and post-test for a course. Marital status is an example of an independent variable with independent samples, while time is an independent variable with repeated samples as a group of students is tested before and after a course.
Quick reminder independent variable
The document discusses dependent and independent variables in studies. The dependent variable is the thing being measured or influenced, while the independent variable is the thing doing the influencing or manipulation between groups. It notes that when comparing a sample to a population on the same dependent variable, like IQ scores, there is no independent variable since nothing is being manipulated between the groups.
Mean conceptual
The document defines the mean as the balance point of a distribution. It uses the analogy of a teeter-totter to explain how the mean is calculated by finding the point that balances all values. Larger values farther from the mean have a greater "weight" and pull the mean toward them more than smaller closer values. The mean represents the central tendency after accounting for the relative influence of all values.
Parallel test reliabilty with surveys
Students take two different forms of the same survey that target the same construct but use different items, and their scores on each form are recorded. The scores from the two forms are then correlated, with higher correlations between 0.8 and 1.0 indicating the forms have high parallel test reliability in measuring the same attribute, while correlations lower than 0.8 represent low parallel test reliability.
Quick reminder ties or no ties
When there are ties in rank ordered or scaled data where the distribution is skewed, Kendall's Tau test should be used to analyze the data. Otherwise, if there are no ties, Spearman's Rho test is appropriate. Kendall's Tau can handle ties in rank ordered or scaled skewed data, while Spearman's Rho cannot account for ties.
Quick reminder distribution normal or skewed
A normal distribution has most data concentrated in the center with decreasing amounts evenly distributed to both sides. A skewed distribution has data clumped to one side trailing off as it decreases, showing skew to either the left or right. To assess skew, divide the skew value by its standard error - a result greater than 2 indicates right skew, less than -2 left skew, and in the given example of 1.20/0.30 = 4, the distribution is significantly right skewed.
Quick reminder diff-rel-ind-gd of fit
This document outlines four statistical concepts: difference compares results between groups like male and female heights; relationship examines how changes in one variable like height affect another like weight; independence determines if two variables like college admission and ethnicity are unrelated; and goodness of fit compares actual results to a claim or hypothesis, like surveying dentists' recommendations versus an initial claim.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unequal intervals between ranks, like place or percentiles. Scaled data has equal intervals between units, like speed, height, or weight. Nominal proportional data shows proportions or percentages, like percentages in surveys.
Quick reminder distribution normal or skewed
A normal distribution has most data concentrated in the center with decreasing amounts evenly distributed to both sides. A skewed distribution has data clumped to one side trailing off, being either right or left skewed. To assess skew, divide the skew value by its standard error - a result greater than 2 indicates right skew, less than -2 left skew, with an example showing a value of 4 indicating significant right skew.
Correlation matrix convergent validity
This document discusses construct-related evidence of validity using a correlation matrix. It shows high inter-item correlations between three items targeting reading efficacy and three items targeting math efficacy, with correlations of .70 or above. This provides evidence of convergent validity, as the items within each scale measuring reading efficacy and math efficacy are highly correlated.
Advance statistics 2
This document discusses random effects models and analysis of variance (ANOVA). It introduces one-way and two-way random effects ANOVA models, distinguishing between random and fixed effects. It describes how to perform inference on variance components in random effects models, including using Satterthwaite's procedure to obtain confidence intervals for variances. Mixed effects models are also introduced, where some factors are fixed and others random.
Diff rel gof-fit - jejit - practice (5)
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
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Calculating the Median
The document explains how to calculate the median of a data set. It involves ordering the values from lowest to highest, then finding the middle value. For data sets with an even number of values, the median is the average of the two middle values. Examples are provided to demonstrate calculating the median both conceptually and using an Excel function. A step-by-step formula for calculating the median manually is also outlined.
Identifying predictable patterns
This 1 sentence document discusses predictable patterns in student responses that may not need to be kept unless there is a compelling reason. It suggests eliminating typical student responses that do not provide unique value.
Range
The document defines the concept of range as the difference between the highest and lowest observed values. It provides an example of a data set with values from 1 to 9, where the lowest value is 1 and the highest is 9. The range is calculated by subtracting the lowest value from the highest value, which in this case is 9 - 1 = 8. Therefore, the range is the difference between the maximum and minimum observations.
Missing data item average
If there is a small sample size, the student's response is estimated based on how they answered other similar questions or how other students in their group answered that specific question.
Quick reminder two or more variables
This document discusses variables and their relationships in analyzing different questions. It explains that many questions look at the relationship between two variables, such as gender and driving speed. However, some questions examine the influence of three or more variables, for example how gender, age and driving speed are related. The document also notes that problems may include a covariate variable that is not directly counted if its effect is being eliminated from the analysis.
Sample to sample or sample to population
The document discusses distinguishing between sample to sample and sample to population comparisons when performing z-tests of proportions on nominal proportional data. An example is provided where a marketing team wants to determine if an infomercial claim of 90% customer satisfaction is accurate. A sample of 20 customers finds 15 satisfied, so the problem involves comparing this sample proportion to the claimed population proportion, making it a sample to population comparison.
Quick reminder sample to sample
The marketing team leader asked to determine if a sample of vacuum brand X owners have statistically different satisfaction results of 80% compared to vacuum brand Y owners' satisfaction of 75%. They also want to know if brand X owners' sample satisfaction of 75% is statistically different than the claimed population satisfaction of brand X of 80%. The document provides examples of how to perform a sample to sample and sample to population Z-test comparison of proportions.
Quick reminder nature of the data
This document defines and provides examples of four types of variable relationships: dichotomous by dichotomous, dichotomous by scaled, ordinal by another variable, and scaled by scaled with at least one variable skewed. It explains that dichotomous variables only have two values each, one variable can be dichotomous and one scaled, ordinal variables are rank ordered, and scaled variables have equal intervals with at least one being skewed. Examples involving gender and driving speed are given for each relationship type.
Quick reminder diff-rel-ind-gd of fit
This document defines four statistical concepts: difference compares results between groups, relationship examines how one variable changes with another, independence looks at whether two variables are unrelated, and goodness of fit compares actual results to expectations. Difference deals with comparing groups, relationship looks at how one variable affects another, independence means two factors are independent of each other, and goodness of fit evaluates how actual outcomes align with predicted values.
Quick reminder independent & repeated (t-tests)
This document discusses two types of independent variables: independent samples and repeated samples. Independent samples involve groups that are completely separate from each other, like singles and married people. Repeated samples involve the same group being measured more than once, like students taking a pre-test and post-test for a course. Marital status is an example of an independent variable with independent samples, while time is an independent variable with repeated samples as a group of students is tested before and after a course.
Quick reminder independent variable
The document discusses dependent and independent variables in studies. The dependent variable is the thing being measured or influenced, while the independent variable is the thing doing the influencing or manipulation between groups. It notes that when comparing a sample to a population on the same dependent variable, like IQ scores, there is no independent variable since nothing is being manipulated between the groups.
Mean conceptual
The document defines the mean as the balance point of a distribution. It uses the analogy of a teeter-totter to explain how the mean is calculated by finding the point that balances all values. Larger values farther from the mean have a greater "weight" and pull the mean toward them more than smaller closer values. The mean represents the central tendency after accounting for the relative influence of all values.
Parallel test reliabilty with surveys
Students take two different forms of the same survey that target the same construct but use different items, and their scores on each form are recorded. The scores from the two forms are then correlated, with higher correlations between 0.8 and 1.0 indicating the forms have high parallel test reliability in measuring the same attribute, while correlations lower than 0.8 represent low parallel test reliability.
Quick reminder ties or no ties
When there are ties in rank ordered or scaled data where the distribution is skewed, Kendall's Tau test should be used to analyze the data. Otherwise, if there are no ties, Spearman's Rho test is appropriate. Kendall's Tau can handle ties in rank ordered or scaled skewed data, while Spearman's Rho cannot account for ties.
Quick reminder distribution normal or skewed
A normal distribution has most data concentrated in the center with decreasing amounts evenly distributed to both sides. A skewed distribution has data clumped to one side trailing off as it decreases, showing skew to either the left or right. To assess skew, divide the skew value by its standard error - a result greater than 2 indicates right skew, less than -2 left skew, and in the given example of 1.20/0.30 = 4, the distribution is significantly right skewed.
Quick reminder diff-rel-ind-gd of fit
This document outlines four statistical concepts: difference compares results between groups like male and female heights; relationship examines how changes in one variable like height affect another like weight; independence determines if two variables like college admission and ethnicity are unrelated; and goodness of fit compares actual results to a claim or hypothesis, like surveying dentists' recommendations versus an initial claim.
Quick reminder ordinal, scaled or nominal proportional
Ordinal data ranks items but with unequal intervals between ranks, like place or percentiles. Scaled data has equal intervals between units, like speed, height, or weight. Nominal proportional data shows proportions or percentages, like percentages in surveys.
Quick reminder distribution normal or skewed
A normal distribution has most data concentrated in the center with decreasing amounts evenly distributed to both sides. A skewed distribution has data clumped to one side trailing off, being either right or left skewed. To assess skew, divide the skew value by its standard error - a result greater than 2 indicates right skew, less than -2 left skew, with an example showing a value of 4 indicating significant right skew.
Correlation matrix convergent validity
This document discusses construct-related evidence of validity using a correlation matrix. It shows high inter-item correlations between three items targeting reading efficacy and three items targeting math efficacy, with correlations of .70 or above. This provides evidence of convergent validity, as the items within each scale measuring reading efficacy and math efficacy are highly correlated.
Advance statistics 2
This document discusses random effects models and analysis of variance (ANOVA). It introduces one-way and two-way random effects ANOVA models, distinguishing between random and fixed effects. It describes how to perform inference on variance components in random effects models, including using Satterthwaite's procedure to obtain confidence intervals for variances. Mixed effects models are also introduced, where some factors are fixed and others random.
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Quick reminder ordinal, scaled or nominal proportional
Quick reminder ordinal, scaled or nominal proportional
More from Ken Plummer
Diff rel gof-fit - jejit - practice (5)
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
Inferential vs descriptive tutorial of when to use - Copyright Updated
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
Diff rel ind-fit practice - Copyright Updated
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updated
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updated
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data practice - Copyright updated
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updated
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
Mode practice 1 - Copyright updated
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
Skewed less than 30 (ties)
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
Skewed sample size less than 30
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
Ordinal (ties)
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
Number of variables (predictive)
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
Levels of the iv
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
Independent variables (2)
The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
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Inferential vs descriptive tutorial of when to use - Copyright Updated
Inferential vs descriptive tutorial of when to use - Copyright Updated
Normal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updated
Nature of the data (descriptive) - Copyright updated
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Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
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