Unit 8: Testing Null Hypotheses |
IMPORTANT: Unit 8: Assignment #5 will require advance planning!
- The purpose of this assignment is to learn about normal distributions and how they allow us to compute probability.
- First, read an excerpt from MathIsFun’s (no date) article, “Normal Distribution.” While reading this excerpt, make sure you understand the following:
- Not all distributions are normal distributions.
- Normal distributions are bell shaped.
- The more normal a distribution, the more likely the mean will equal the median, which will equal the mode.
- Second, read Poldrack’s (2020) Chapter 7 “The Central Limit Theorem.” While reading this excerpt, make sure you understand the following:
- “The Central Limit Theorem tells us that as sample sizes get larger, the sampling distribution of the mean will become normally distributed.”
- “The Central Limit Theorem is important for statistics because it allows us to safely assume that the sampling distribution of the mean will be normal in most cases.”
- Third, watch CrashCourse’s (2018) video, “The Normal Distribution.” While watching this video, make sure you understand the following:
- “The Central Limit Theorem states that the distribution of sample means for an independent, random variable, will get closer and closer to a normal distribution as the size of the sample gets bigger and bigger.”
- “with large samples, the sample means will be a pretty good estimate of the true population mean”
- First, read an excerpt from MathIsFun’s (no date) article, “Normal Distribution.” While reading this excerpt, make sure you understand the following:
- In CrashCourse’s (2018) video, they demonstrate the difference in a distribution of results after rolling only one die on each roll versus the distribution of results after rolling a pair of dice on each roll. Let’s demonstrate that phenomenon ourselves with this dice rolling simulator. If you need it, this instruction sheet might be helpful.
- First, slide the number-of-dice selector, which is in the bottom right, to “dice = 1” (the word for one dice is technically “die”).
- Second, simulate rolling only one die 50 times by manually clicking the “Roll dice – 1 [time]” button 50 times.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_OneDie_50_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- Third, click the “Reset” button, which will clear your previous simulation.
- Fourth, simulate rolling one die 100 times by clicking the “Roll dice – 100 [times]” button.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_OneDie_100_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- Fifth, click the “Reset” button, which will clear your previous simulation.
- Sixth, simulate rolling one die 10,000 times by clicking the “Roll dice – 10,000 [times]” button.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_OneDie_10,000_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- Seventh, slide the number-of-dice selector, which is in the bottom right, to “dice = 2”.
- Eighth, simulate rolling two dice 50 times by manually clicking the “Roll dice – 1 [time]” button 50 times.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_TwoDice_50_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- Ninth, click the “Reset” button, which will clear your previous simulation.
- Tenth, simulate rolling two dice 100 times by clicking the “Roll dice – 100 [times]” button.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_TwoDice_100_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- Eleventh, click the “Reset” button, which will clear your previous simulation.
- Twelfth, simulate rolling two dice 10,000 times by clicking the “Roll dice – 10,000 [times]” button.
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
**YourLastName**_PSY-210_Unit08_TwoDice_10,000_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).
- Take a screenshot of the resulting distribution (not your entire screen) and save the screenshot with the filename
- As you probably noticed, the bar graphs that the dice simulator generates for us do not fulfill the criteria of “good bar graphs.”
- The “bad bar graphs” generated by the dice simulator are missing their Graph Titles and Axis Labels.
- The “bad bar graphs” generated by the dice simulator present their x-axis Graph Units in numerals (e.g., 1, 2, 3).
- However, a “good bar graph” should display the x-axis Graph Units as categories using text (e.g., One, Two, Three) not as numerals, because using numerals might cause people to think the graphs are histograms, for continuous measurements, but they are bar graphs, for discrete measurements (each roll of a die or roll of a pair of dice can take on only a specific value).
- The “bad bar graphs” generated by the dice simulator don’t have gaps between the bars, which could cause people to think the graphs are histograms, not bar graphs.
- Therefore, you need to create “good bar graphs” using your chosen data management platform.
- Begin by creating an Absolute Frequency Distribution Table from EACH of the six screenshots you previously saved:
- YourLastName_PSY-210_Unit08_
**OneDie_50_Screenshot**.xxx - YourLastName_PSY-210_Unit08_
**OneDie_100_Screenshot**.xxx - YourLastName_PSY-210_Unit08_
**OneDie_10,000_Screenshot**.xxx - YourLastName_PSY-210_Unit08_
**TwoDice_50_Screenshot**.xxx - YourLastName_PSY-210_Unit08_
**TwoDice_100_Screenshot**.xxx - YourLastName_PSY-210_Unit08_
**TwoDice_10,000_Screenshot**.xxx
- YourLastName_PSY-210_Unit08_
- When making each Absolute Frequency Distribution Table, be sure to enter your categories as text (e.g., One, Two, Three, and so forth) not numerals (e.g., 1, 2, 3, and so forth), as in this example.
- Next, create six “good bar graphs” that look like this.
- Use the x-Axis Label, “Roll Outcome”
- Use the y-Axis Label, “Absolute Frequency”
- Use the Graph Title, “One Die xxx Rolls” for your one die bar graphs, where xxx is the number of rolls (e.g., “One Die 50 Rolls”)
- Use the Graph Title “Two Dice xxx Rolls” for your two dice bar graphs, where xxx is the number of rolls (e.g., “Two Dice 50 Rolls”)
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit08_OneDie_50_Screenshot.xxx.- Adjust the y-axis to a minimum of 0 and a maximum of 25 (you can refresh your memory on how to change the y-axis by reading this handout).
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_OneDie_50_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit08_OneDie_100_Screenshot.xxx- Adjust the y-axis to a minimum of 0 and a maximum of 50.
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_OneDie_100_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit08_OneDie_10,000_Screenshot.xxx- Adjust the y-axis to a minimum of 0 and a maximum of 2500.
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_OneDie_10,000_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit07_TwoDice_50_Screenshot.xxx.- Adjust the y-axis to a minimum of 0 and a maximum of 25.
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_TwoDice_50_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit07_TwoDice_100_Screenshot.xxx- Adjust the y-axis to a minimum of 0 and a maximum of 50.
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_TwoDice_100_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
**YourLastName**_PSY-210_Unit07_TwoDice_10,000_Screenshot.xxx- Adjust the y-axis to a minimum of 0 and a maximum of 2500.
- Take a screenshot of your “good bar graph” (not your entire screen) and name the file
**YourLastName**_PSY-210_Unit08_TwoDice_10,000_GoodBarGraph_Screenshot.xxx
- Create a “good bar graph” from your
- Go to the Unit 8: Assignment #1 Discussion Board and make a new Discussion Board post in which you do the following:
- embed your six bar graphs; remember to embed and size your images according to the procedures you learned from the Course How To; and
- discuss,
**in at least 50 words**, the differences in the distributions resulting from rolling one die versus two dice, for example:- Which distribution(s) resembled a normal distribution?
- Which distribution(s) did not resemble a normal distribution?
- The purpose of this assignment is to learn about confidence intervals and how they help us make predictions:
- First, read an excerpt from MathIsFun’s (no date) article, “Confidence Intervals.” While reading this excerpt, make sure you understand the following:
- A confidence interval marks a range of values in which we are confident our true value lies.
- A 95% confidence interval of a sample’s mean allows us to predict that 95 of 100 additional samples will include the true population mean, but 5 of 100 additional samples won’t.
- Second, watch CrashCourse’s (2018) video, “Confidence Intervals.” You only need to watch through the 6:50 (six minutes and 50 seconds) mark. While watching this video, make sure you understand the following:
- “A confidence interval is an estimated range of values that seem reasonable based on what we’ve observed.”
- “Giving a range of numbers instead of just an estimate … better represents the fact that there’s some uncertainty and variation.”
- “You don’t always need to use a confidence interval of 95%, you can calculate other confidence intervals, too.”
- Third, read McLeod’s (no date) article, “What are Confidence Intervals in Statistics?” While reading this excerpt, make sure you understand the following:
- A confidence interval provides a range of values that are likely to contain the true value.
- A 95% confidence interval of a sample’s mean allows us to predict that in 5 out of 100 additional samples, the population mean will lie outside the upper and lower bounds of the confidence interval.
- A 99% confidence interval of a sample’s mean allows us to predict that in 1 out of 100 additional samples the population mean will lie outside the upper and lower bounds of the confidence interval.
- First, read an excerpt from MathIsFun’s (no date) article, “Confidence Intervals.” While reading this excerpt, make sure you understand the following:
- Learn how to compute Confidence Intervals.
- First, read Poldrack’s (2020) Chapter 7 “Confidence Intervals.” While reading this chapter, make sure you understand the following:
- A conﬁdence interval provides a measure of how close our sample estimate is to the population parameter.
- The proper interpretation of a 95% confidence interval of a sample’s mean is that 95 out of 100 samples (drawn from the same population) will contain the true population
mean, but 5 of the 100 samples will not.
- Second, learn from Poldrack’s chapter the formula for computing a confidence interval of a mean, which is the Mean ± (the z-value * the SEM) and that translates to the following:
- the upper bound of a confidence interval is the sample’s Mean PLUS (the z-value * the SEM)
- the lower bound of a confidence interval is the sample’s Mean MINUS (the z-value * the SEM)
- First, read Poldrack’s (2020) Chapter 7 “Confidence Intervals.” While reading this chapter, make sure you understand the following:
- Using your chosen data management platform, compute a 95% confidence interval for
**(a)**the mean of your**Height Data**in your original**“Five Data,” (b)**the mean of your Chat Group’s**Combined Height Data**, and**(c)**the mean of your**Assigned Height Data**.- First, make a new Column Header called “Z-Values” and enter the z-value for a 95% confidence interval, which is 1.960
- Second, for each data set, identify the cell that contains (or type into a new cell) that data set’s mean.
- Third, for each data set, identify the cell that contains (or type into a new cell) that data set’s SEM (which you computed during Unit 7).
- Fourth, in a new cell type the formula
**=Mean+(Z-Value*SEM)**which is the formula for that data set’s**95% upper bound**.- Mean is the cell that contains that data set’s mean;
- + is the function for adding;
- Z-Value is the cell that contains the 95% z-value, which is 1.960;
- * is the function for multiplying;
- SEM is the cell that contains that data set’s SEM.
- Your formula should look something like this.
- Be sure to use parentheses around (Z-Value*SEM)
- After you press return/enter, you should have your data set’s 95% upper bound!
- Fifth, in another new cell type the formula
**=Mean-(Z-Value*SEM)**which is the formula for your data set’s**95% lower bound**.- Mean is the cell that contains that data set’s mean;
- – is the function for subtracting;
- Z-Value is the cell that contains the 95% z-value, which is 1.960;
- * is the function for multiplying;
- SEM is the cell that contains that data set’s SEM.
- Your formula should look something like this.
- Be sure to use parentheses around (Z-Value*SEM)
- Note that the only difference between your formula for your upper bound and your lower bound is the use of the + sign for the upper bound and the – sign for the lower bound.
- After you press return/enter, you should have your data set’s 95% lower bound!
- Check your work by using this online Confidence Interval calculator.
- Note that this online calculator computes confidence intervals with standard deviations rather than SEMs. That’s ok! You should get the same confidence intervals.
- Note also that this online calculator doesn’t provide three decimal places, and we always want three decimal places.
- However, the calculator does show us how to report confidence intervals and that is in the format
**xx.xxx, 95% CI [yy.yyy, zz.zzz]**, where xx.xxx is your data set’s mean, yy.yyy is your data set’s lower bound, and zz.zzz is your data set’s upper bound.
- Using your chosen data management platform, compute
**99% confidence intervals**of**(a)**your**Height Data**in your original**“Five Data,” (b)**your Chat Group’s**Combined Height Data**, and**(c)**your**Assigned Height Data**.- First, add to your “Z-Values” Column Header the z-value for 99% confidence intervals, which is 2.576
- All the other steps are the same, but be sure to use the cell for the 99% z-value rather than the 95% z-value.
- Again, you can check your work by using this online Confidence Interval calculator.
- Be sure to change the online calculator’s Confidence Interval from 95% to 99%.
- Go to the
__Unit 8: Assignment #2 Discussion Board__and make a new Discussion Board post in which you do the following.- First, in the first sentence of your Discussion Board post, state your unique data set number (e.g., “My unique data set number is 001″).
- Second, write the sentence “The 95% Confidence Interval of my “Five Data” Height Data is xx.xxx, 95% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Third, write the sentence “The 95% Confidence Interval of my Chat Group’s Combined Height Data is xx.xxx, 95% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Fourth, write the sentence “The 95% Confidence Interval of my Assigned Height Data is xx.xxx, 95% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Fifth, write the sentence “The 99% Confidence Interval of my “Five Data” Height Data is xx.xxx, 99% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Sixth, write the sentence “The 99% Confidence Interval of my Chat Group’s Combined Height Data is xx.xxx, 99% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Seventh, write the sentence “The 99% Confidence Interval of my Assigned Height Data is xx.xxx, 99% CI [yy.yyy, zz.zzz],” filling in the xs, ys, and zs.
- Discuss,
**in at least 50 words**:- What can you observe about the differences among the 95% confidence intervals for your three data sets (your Five Data, your Combined Data, and your Assigned Data)?
- Which confidence intervals are broader? Which confidence intervals are narrower?
- What can you observe about the differences between each data set’s 95% confidence intervals and that same data set’s 99% confidence interval?
- Which confidence intervals are broader? Which confidence intervals are narrower?
- What can you observe about the differences among the 95% confidence intervals for your three data sets (your Five Data, your Combined Data, and your Assigned Data)?
- The purpose of this assignment is to understand what we mean by null hypothesis significance testing, which is abbreviated as NHST.
- First, learn a simple definition of null hypothesis significance testing by reading an excerpt from Wikipedia’s (2020) article, “Null Hypothesis.” While reading this excerpt, make sure you understand the following:
- The null hypothesis is the “default position.” (A default position is like a pre-set or built-in position.)
- “The null hypothesis is assumed to be true until evidence indicates otherwise (similar to the case that a defendant of a jury trial is presumed innocent until guilty).”
- Second, read Poldrack’s (2020) Chapter 9, “Hypothesis Testing.” While reading this excerpt, make sure you understand the following:
- “NHST can be problematic” (which is why we aren’t going to dwell on the topic in this course).
- NHST is “widely misunderstood, largely because it violates our intuitions about how statistical hypothesis testing should work.”
- Therefore, understanding NHST is challenging!
- In NHST, “we ﬁrst take our hypothesis of interest (i.e., whether GRE test-prep training leads to higher GRE scores), and ﬂip it on its head, creating a null hypothesis.”
- The null hypothesis always involves some kind of equality (=, ≥, or ≤).
- The alternative hypothesis always involves some kind of inequality (≠, >, or <).
- Null hypothesis testing operates under the assumption that the null hypothesis is true unless the evidence shows otherwise.
- Null and alternative hypotheses can be directional, or they can be non-directional.
- Third, read an excerpt from Taylor’s (2019) article, “Null Hypothesis and Alternative Hypothesis.” While reading this excerpt, make sure you understand the following:
- “In a mathematical formulation of the null hypothesis, there will typically be an equal sign (or a combined equal to/greater than sign, ≥, or a combined equal to/less than sign, ≤).”
- “In a mathematical formulation of the alternative hypothesis, there will typically be an inequality, such as a not-equal-to symbol, ≠, or a greater than sign, >, or a less than sign, <.”
- “If the null hypothesis is not rejected, then we must be careful to say what this means.”
- First, learn a simple definition of null hypothesis significance testing by reading an excerpt from Wikipedia’s (2020) article, “Null Hypothesis.” While reading this excerpt, make sure you understand the following:
- Now, try your hand at writing pairs of null and alternative hypotheses.
- By a pair of hypotheses, we mean a null hypothesis and its corresponding alternative hypothesis.
- You may write pairs of hypotheses about any domain you want; however, it will probably be easier if you think of hypotheses that can be tested with psychological research.
- Write five pairs of hypotheses that are non-directional (if you can’t remember what a non-directional hypothesis means, re-read the excerpt from Poldrack’s Chapter 9).
- Write five pairs of hypotheses that are directional (if you can’t remember what a directional hypothesis means, re-read the excerpt from Poldrack’s Chapter 9).
- You must write your pairs of hypotheses in English. But if you ALSO want to try your hand at writing your pairs of hypotheses using mathematical formulation (e.g., H0: BMI-active ≥ BMI-inactive; HA: BMI-active < BMI-inactive), please do so.
- You’ll probably need to Google to learn how to make ≥, ≤, and ≠ symbols on your keyboard; on a Mac, it simply requires holding down the option-alt key while typing >, <, or =.
- Remember, as you learned in the assigned reading: The null hypothesis always involves some kind of equality (=, ≥, or ≤), and the alternative hypothesis always involves some kind of inequality (≠, >, or <).
- Go to the
__Unit 8: Assignment #3 Discussion Board__and make a new Discussion Board post in which you list your five pairs of hypotheses that are non-directional and your five pairs of hypotheses that are directional.
- The purpose of this assignment is to understand what we mean by
*p*-values. As you’ll see,*p*-values are as un-intuitive as NHST.- First, read the abstract and first page of Cassidy et al.’s (2019) article, “Failing Grade: 89% of Introduction-to-Psychology Textbooks That Define or Explain Statistical Significance Do So Incorrectly.” While reading this excerpt, make sure you notice the following:
- Although “null-hypothesis significance testing (NHST) is commonly used in psychology,” it is “not well understood by either psychology professors or psychology students.”
- In Cassidy et al.’s study, they “investigated whether introduction-to-psychology textbooks accurately define and explain statistical significance.”
- From their study, Cassidy et al. report that “89% [of introduction-to-psychology textbooks] incorrectly defined or explained statistical significance.” (Yikes!)
- Second, to see how widely misunderstood
*p*-values are, see this Tweet about even the brainy TV game show*Jeopardy*getting*p*-values wrong. - Third, learn a correct (and simple) definition of
*p*-values by reading an excerpt from Wikipedia’s (2020) article, “p value.” While reading this excerpt, make sure you understand the following:- “The
*p*-value … is the probability of obtaining test results at least as extreme as the results actually observed, assuming that the null hypothesis is correct.” - “A very small
*p*-value means that the observed outcome is not very likely under the null hypothesis, although it is possible.” - Because “the precise meaning of
*p*-value is hard to grasp, misuse is widespread.”
- “The
- Fourth, read through Cassidy et al.’s (2019) slides that, as they promise in their article, correctly describe
*p*-values.- Prior to reading through Cassidy et al.’s slides, you might need to learn a bit about correlation coefficients, which you can do by reading through an excerpt of Investopedia’s article, “Correlation Coefficient.”
- While reading through Cassidy et al.’s slides, make sure you understand:
- We begin by assuming the null hypothesis.
- We estimate the percent of results that could be as extreme, or more extreme, than our sample’s result.
- A
*p*-value of .030 means that, assuming the null hypothesis is true, there is a 3% probability of obtaining results as extreme, or more extreme, than our study’s results. - A
*p*-value of .030 does not mean there is a 3% probability of our being wrong if we reject the null hypothesis. - If the
*p*-value is low enough (e.g,*p*< .050) we can reject the null hypothesis.
- While reading through Cassidy et al.’s slides, make sure you understand:
- Prior to reading through Cassidy et al.’s slides, you might need to learn a bit about correlation coefficients, which you can do by reading through an excerpt of Investopedia’s article, “Correlation Coefficient.”
- Fifth, read through Cassidy et al.’s (2019) Table 1 and Figure 2, which show the most frequent fallacies about
*p*-values found in Intro Psych textbooks. (A fallacy is a “mistaken belief.”)- Notice that many of the fallacies mistakenly forget about the null hypothesis.
- Notice that other fallacies incorrectly assume we are accepting or rejecting the alternative hypothesis, rather than accepting or rejecting the null hypothesis.
- First, read the abstract and first page of Cassidy et al.’s (2019) article, “Failing Grade: 89% of Introduction-to-Psychology Textbooks That Define or Explain Statistical Significance Do So Incorrectly.” While reading this excerpt, make sure you notice the following:
- Go to the
__Unit 8: Assignment #4 Discussion Board__and make a new Discussion Board post in which you explain, in at least 200 words, what you previously knew about*p*-values and what you now know.
- At this point in the course, you will construct new Chat Groups. Please follow closely the instructions below
**.****IMPORTANT: If the due date for this assignment is approaching**, and you haven’t been contacted about being in a new Chat Group,**reach out to ALL the students in your Section who are responsible for recruiting new Chat Groups**- You probably also want to monitor the
__Unit 8: Assignment #5 Discussion Board__so that you will know which new Chat Groups have already been formed.
**If your****last name is LAST alphabetically**in your__“old” Chat Group__, these are your responsibilities for forming a new Chat Group:- You’re responsible for recruiting new students to your new Chat Group. You can contact students via their wisc.edu email (which you can find on the “old” Chat Group List by clicking, or right-clicking, on any student’s name).
- You may recruit any student to your new Chat Group from your Section (but only from your Section)
**except**- you may NOT recruit a student who was a member of your “old” Chat Group (because the goal during the second half of the course is to mix up the Chat Groups a bit); and
- you may NOT recruit a student whose last name is also last alphabetically in their “old” Chat Group (because those students whose last names are last alphabetically in their old Chat Groups will also be the students responsible for recruiting students to a new Chat Group).
- If you can’t remember what Section you are in, you might want to return to Unit 1: Assignment #2 (and the part of the assignment that said “From the Course How To, learn how to find out which section you are in and find out which section you are in”).
- If your “old” Chat Group had three members (including you), then your new Chat Group should also have three members (including you); if your
__“old” Chat Group had only two members, then your new Chat Group will have only two members (including you).__ - When recruiting new members to your new Chat Group:
- It does NOT matter if the students you are recruiting were previously in a two-member or three-member “old” Chat Group.
- It only matters how many members you (the person whose last name comes LAST alphabetically and therefore the person doing the recruiting) had in your “old” Chat Group.
- Similarly, it does NOT matter if the students you are recruiting previously worked together in their “old” Chat Group; it only matters that none of the students you are recruiting are students with whom you worked in your “old” Chat Group.
- Appoint one member of your
**new Chat Group**to set up the new Group Chat space.- Remind this person that the instructions for setting up a new Group Chat space are below in c.
- You must NOT be the Chat Group member to set up the new Group Chat space; rather, you must appoint someone else — a member of your new Chat Group — to set up the new Group Chat room.
- Arrange with your new Chat Group a one-hour time period when all of you can meet online to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
- Decide which Chat Group member will be responsible for using the browser Chrome on a laptop (not a mobile device) during your Group Chat.
- Go to the
__Unit 8: Assignment #5 Discussion Board__and make a post in which you list:- the full name (e.g., The Medians: Group X) of your new Chat Group (which will be the name of your “old” Chat Group);
- the first and last name of the members of your new Chat Group (including you);
- the first and last name of the student you appointed to create the new Group Chat room;
- the first and last name of the student who will be responsible for using the browser Chrome on a laptop (not a mobile device) during your Group Chat; and
- the day (e.g., MON, TUE, WED), the calendar date (e.g., JUL 18, JUL 19, JUL 20), and the hour-long timeslot (e.g, 2:30 – 3:30 pm) that your new Chat Group has agreed to meet to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
**If you are appointed to set up the new Group Chat space**for your new Chat Group, these are your responsibilities:- Follow the instructions in the
__Course How To__for “How To Set Up a Chat Group’s Chat Space on Your Laptop” OR “How To Set Up a Chat Group’s Chat Space on Your Mobile Device.” - Title the Chat Space
**the name of the old Chat Group Space of the student who recruited you**to the new Chat Group, AND**add an underscore and a 2**to the Chat Space name to indicate the second half of the term (e.g., TheMedians_GroupX_SU24_2)- You are responsible for finding out the name of the old Chat Group Space of the student who recruited you to the new Chat Group.
- Take a screenshot of the name of your new Group Chat room (if you don’t know how to take a screenshot, this website will help you).
- Arrange with your new Chat Group a one-hour time period when all of you can meet online to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
- Decide which Chat Group member will be responsible for using the browser Chrome on a laptop (not a mobile device) during your Group Chat.
- Go to the
__Unit 8: Assignment #5 Discussion Board__and make a post in which you- embed (not attach, but embed) the screenshot of your new Chat Group’s Chat Space; remember from the
__Course How To__that you might need to size your image, and - list the day (e.g., MON, TUE, WED), the calendar date (e.g., JUL 18, JUL 19, JUL 20), and the hour-long timeslot (e.g, 2:30 – 3:30 pm) that your new Chat Group has agreed to meet to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
- embed (not attach, but embed) the screenshot of your new Chat Group’s Chat Space; remember from the
- Follow the instructions in the
**If you are in a NEW THREE-student Chat Group; if****your****last name was NOT last alphabetically**in your “old” Chat Group; and**if****you were NOT appointed to set up the new Group Chat room**for your NEW Chat Group, these are your responsibilities:- Arrange with your new Chat Group a one-hour time period when all of you can meet online to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
- Decide which Chat Group member will be responsible for using the browser Chrome on a laptop (not a mobile device) during your Group Chat.
- Go to the
__Unit 8: Assignment #____5 Discussion Board__and make a new post in which you list- the full name (e.g., The Medians: Group X) of your new Chat Group (which will be the name of the “old” Chat Group of the student who recruited you to the new Chat Group);
- the first and last names of all the members of your new Chat Group (including you);
- the first and last name of the student who recruited you to the new Chat Group;
- the first and last name of the student who will be responsible for using the browser Chrome on a laptop (not a mobile device) during your Group Chat; and
- the day (e.g., MON, TUE, WED), the calendar date (e.g., JUL 18, JUL 19, JUL 20), and the hour-long timeslot (e.g, 2:30 – 3:30 pm) that your new Chat Group has agreed to meet to hold your Group Chat for the next Unit (Unit 9: Assignment #5).
Congratulations, you have finished Unit 8! Onward to |