Anova t test - je jit - practice
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The document discusses when to use an independent samples t-test versus a one-way ANOVA. It states that an independent samples t-test should be used when the explanatory variable has two levels, such as comparing customer satisfaction for those who did and did not take advantage of promotions. A one-way ANOVA should be used when the explanatory variable has three or more levels, such as comparing customer retention rates for those who purchased product A, B, C, or D.
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Anova t test - je jit - practice
This document discusses when to use independent t-tests and one-way ANOVAs. It states that an independent t-test is used when the explanatory variable has two levels, while a one-way ANOVA is used when there are three or more levels. It provides examples comparing two products and comparing lower and upper management. It also provides a practice problem about comparing lower, middle, and upper management, stating that a one-way ANOVA would be the appropriate test.
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Anova t test - je jit - practice
This document discusses when to use independent t-tests and one-way ANOVAs. It states that an independent t-test is used when the explanatory variable has two levels, while a one-way ANOVA is used when there are three or more levels. It provides examples comparing two products and comparing lower and upper management. It also provides a practice problem about comparing lower, middle, and upper management, stating that a one-way ANOVA would be the appropriate test.
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Independent Variable [IV]
(number of groups)Dependent Variable [DV]
(measurement level) Two Groups
Three + Groups
Independent
(“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
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t-testDependent
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“What is the effect of TREATMENT (IV) on our OUTCOME (DV) of interest?”
Example: TREATMENT independent groups (placebo versus drug), OUTCOME interval/ratio (blood pressure)
Example: TREATMENT dependent group (pre/post yoga therapy), OUTCOME ordinal (back pain levels)
Example: TREATMENT independent 3+ groups (yoga therapy, none, aerobics), OUTCOME categorical (pass/fail of driving test)CorrelationsPhi coefficientSpearman’s rhoPearson’s r
Independent Variable
(number of groups)Dependent Variable (measurement level) Two Groups
Three + Groups
Independent
(“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
(“paired”)
CategoricalNon-parametric TestsChi-squareMcNemar’sChi-square
Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed ranksKruskal Wallis HFriedman’sInterval / Ratio
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STEP #1
Check what measurement level your DV is.
STEP #2
Choose the column related to the number Groups in your study.
STEP #3
Choose the column where intervention groups are either “paired” or “unpaired.”
STEP #4
Match your column with the row to find which test
to run.
STEP #1
Look at your Dependent Variable or outcome.
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What is its measurement level?
Categorical (such as yes or no; dead or alive; pass or fail).
Ordinal (such as health status – poor, average, excellent).
Interval ratio (for instance blood pressure, cholesterol level, rates of infection, or workplace satisfaction scores on a scale of 0-100).
STEP #2
Next you will look for the column that corresponds to the number of groups you have for your Independent Variable (also called experimental or predictor variable).
Remember, the independent variable is the thing in your study that was controlled by you (such as a medical intervention, or training initiative, or implementation of a modified protocol) for the purpose of making a change on some outcome in the population you are studying.
So…how many groups were involved in this intervention?
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statisticscf_choose_a_statistical_test (1) (1).pptxIndependen.docx
statistics/cf_choose_a_statistical_test (1) (1).pptx
Independent Variable [IV]
(number of groups)Dependent Variable [DV]
(measurement level) Two Groups
Three + Groups
Independent
(“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
(“paired”)
CategoricalNon-parametric TestsChi-squareMcNemar’sChi-square
Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed ranksKruskal Wallis HFriedman’sInterval / Ratio
(continuous)Parametric TestsIndependent
t-testDependent
t-testANOVARM-ANOVA
“What is the effect of TREATMENT (IV) on our OUTCOME (DV) of interest?”
Example: TREATMENT independent groups (placebo versus drug), OUTCOME interval/ratio (blood pressure)
Example: TREATMENT dependent group (pre/post yoga therapy), OUTCOME ordinal (back pain levels)
Example: TREATMENT independent 3+ groups (yoga therapy, none, aerobics), OUTCOME categorical (pass/fail of driving test)CorrelationsPhi coefficientSpearman’s rhoPearson’s r
Independent Variable
(number of groups)Dependent Variable (measurement level) Two Groups
Three + Groups
Independent
(“unpaired”)Dependent
(“paired”)Independent
(“unpaired”)
Dependent
(“paired”)
CategoricalNon-parametric TestsChi-squareMcNemar’sChi-square
Cochran’s QOrdinal Mann-Whitney UWilcoxon Signed ranksKruskal Wallis HFriedman’sInterval / Ratio
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t-testDependent
t-testANOVARM-ANOVA
STEP #1
Check what measurement level your DV is.
STEP #2
Choose the column related to the number Groups in your study.
STEP #3
Choose the column where intervention groups are either “paired” or “unpaired.”
STEP #4
Match your column with the row to find which test
to run.
STEP #1
Look at your Dependent Variable or outcome.
The data that we are looking at here is from the instruments you used to measure the effect of your intervention. Maybe you chose to measure stress with a commonly used psychological questionnaire or maybe you measured cholesterol levels or test scores.
What is its measurement level?
Categorical (such as yes or no; dead or alive; pass or fail).
Ordinal (such as health status – poor, average, excellent).
Interval ratio (for instance blood pressure, cholesterol level, rates of infection, or workplace satisfaction scores on a scale of 0-100).
STEP #2
Next you will look for the column that corresponds to the number of groups you have for your Independent Variable (also called experimental or predictor variable).
Remember, the independent variable is the thing in your study that was controlled by you (such as a medical intervention, or training initiative, or implementation of a modified protocol) for the purpose of making a change on some outcome in the population you are studying.
So…how many groups were involved in this intervention?
For example, if you were testing the effect of an evidence-based training initiative on employee workplace satisfaction or happiness, you might be interested in comparing the training initiative in one group to no training in another group..
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Experimental research designs are considered the standard for research. They involve assigning one or more dependent variables to different treatments and observing the results to draw conclusions. Experimental research has both advantages and disadvantages. It allows full researcher control but can be resource-intensive. It aims to determine relationships between dependent and independent variables by supporting or rejecting hypotheses. Data must be quantifiable and include measurements of variables like area, weight, temperature etc. Qualitative observations also supplement the research. Overall, experimental research uses a scientific approach to test business matters and understand customer behavior through product testing and experiments.
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A study on consumer behavior of aavin milk in bhel township
This document summarizes a study on consumer behavior regarding the purchase of Aavin milk in Bhel Township, Trichy. The study had several objectives: to assess consumer satisfaction with Aavin milk, identify factors influencing milk consumption, assess consumer awareness of milk brands, and evaluate advertising effectiveness. A sample of 120 consumers was surveyed using an interview questionnaire. Key findings included that quality was the most important factor influencing brand loyalty, 60% of respondents preferred Aavin for its quality, and 45% of respondents were highly satisfied with Aavin. Suggestions included reducing Aavin's price to be more competitive and improving extra quality and sales promotion efforts.
A study on consumer behavior of aavin milk in bhel township
This document summarizes a study on consumer behavior regarding the purchase of Aavin milk in Bhel Township, Trichy. The study had several objectives: to assess consumer satisfaction with Aavin milk, identify factors influencing milk consumption, assess consumer awareness of milk brands, and evaluate advertising effectiveness. A sample of 120 consumers was surveyed using an interview questionnaire. Key findings included that quality was the most important factor influencing brand loyalty, 60% of respondents preferred Aavin for its quality, and 45% of respondents were highly satisfied with Aavin. Suggestions included reducing Aavin's price to be more competitive and improving extra quality and sales promotion efforts.
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ABOUT THE TOPIC
MARKETING MIX AND PROMOTIONS
PROMOTION – One of the important element
SALES PROMOTION
WHY MARKETERS USE
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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.
More from Ken Plummer (20)
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
Nature of the data (descriptive) - Copyright updated
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Anova t test - je jit - practice
- 1. Which method should be run? Every explanatory variable have at least two levels and many have more. Central Tendency, Spread, or Symmetry? Independent samples t-test One-way ANOVA
- 2. An independent samples t-test is used when the explanatory variable has two levels. For example: Do customers who take advantage of promotions have greater product satisfaction than those who do not tke advantage of promotions? Central Tendency, Spread, or Symmetry?
- 3. An independent samples t-test is used when the explanatory variable has two levels. For example: Do customers who take advantage of promotions have greater product satisfaction than those who do not take advantage of promotions? Central Tendency, Spread, or Symmetry?
- 4. A One-way ANOVA is used when the explanatory variable has three or more levels. For example: Which customers have better retention rates? Those who purchase product A, product B, product C, or product D Central Tendency, Spread, or Symmetry?
- 5. A One-way ANOVA is used when the explanatory variable has three or more levels. For example: Which customers have better retention rates: Those who purchase product A, product B, product C, or product D Central Tendency, Spread, or Symmetry?