Assignment Grading Criteria

Maximum Points

Explained whether each hypothesis is a one-tailed test or a two-tailed test.

5

Correctly identified the type I error for each hypothesis.

5

Correctly identified the type II error for each hypothesis.

5

Used correct spelling, grammar, and professional vocabulary. Used APA format.

5

Total:

20

W5A2

H1: ___ tailed

H2: ___ tailed

H3: ___ tailed

Type I error for:

H1 =

H2 =

H3 =

Type II error for:

H1 =

H2 =

H3 =

W1A2

Subject (ID) – Alphanumeric – Use two numbers for all IDs. No letters.

Age – Numeric – Use two numbers only. No letters.

Sex – Alphanumeric – Uses one letter only: M=male or F=female.

Height - Numeric - This must be only two numbers: the total height in inches only.

Year in college – Alphanumeric – Forty students who are five males and five females in each year of college. Use one letter only: F=freshman, S=sophomore, J=junior, and S=senior. (You must choose a different letter to show the difference between sophomores and seniors, it is your decision how to show this difference)

W2A1

EXAMPLE:

There were seventy-two participants recruited from an introductory psychology class at South University. There were x males and x females, with x Caucasian, x African Americans, and x other ethnicities represented. The ages ranged from x to x years, with a mean age of x years, SD x. Each participant watched a movie and rated his or her satisfaction on a scale of 1 to 10. The mean level of satisfaction was x, SD x.

W2A2

EXAMPLE:

There were seventy-two participants recruited from an introductory psychology class at South University. There were thirty-six males and thirty-six females, with twenty-four Caucasian, twenty-four African Americans, and twenty-four other ethnicities represented. The ages ranged from x to x years, with a mean age of x years, SD x. Each participant watched a movie and rated his or her satisfaction on a scale of 1 to 10. The mean level of satisfaction was x, SD x.

W3A1

Age Results

The top 5% (z-score above 1.645)

Subject Id: _____

Bottom 5% (z-score below -1.645)

Subject Id: _____

Top 2.5% (z-score above 1.96)

Subject Id: ____

Bottom 2.5% (z-score below -1.96)

Subject Id: ____

Height Results

Top 5% (z-score above 1.645)

Subject Id: ____

Bottom 5% (z-score below -1.645)

Subject Id: _____

Top 2.5% (z-score above 1.96)

Subject Id: ____

Bottom 2.5% (z-score below -1.96)

Subject Id: _____

W3A2

Q#2

Extremely high score =

z score of ___ above the mean = participant #___ scored ___

Extremely low score =

z score of ___ below the mean = participant #___ scored ___

Q#3

W5A1

Between groups design:

DV =

IV =

Level 1 =

Level 2 =

Within subjects design:

DV =

IV = diet

Level 1 =

IV =

Level 1 =

Level 2 =

W5A2

H1: ___ tailed

H2: ___ tailed

H3: ___ tailed

Type I error for:

H1 =

H2 =

H3 =

Type II error for:

H1 =

H2 =

H3 =

W6A1

EXAMPLE:

A one-sample t-test was conducted to find whether age in the sample was different from age in the general population. The t-test (was/was not) significant; t(x)= x, p = x; participants in the sample (M = x, SD = x) were significantly (more/not more) than the general population (M = x). The null hypothesis (is/is not) rejected.

ALSO MUST INCLUDE:

If the t statistic is in the rejection region, reject the null hypothesis.

OR

If the t statistic is not in the rejection region, accept the null hypothesis.

W6A2

EXAMPLE:

A one-sample t-test was conducted to find whether the overall stress of participants in the eyewitness experiment was different from that of the general population of students in online universities. The t-test (was/was not) significant; t(x)= x, p = x; participants in the eyewitness experiment (M = x, SD = x) (were/were not) significantly more stressed than the general population of students in online universities (M = x). The null hypothesis (is/is not) rejected.

ALSO MUST INCLUDE:

If the t statistic is in the rejection region, reject the null hypothesis.

OR

If the t statistic is not in the rejection region, accept the null hypothesis.

W7A1

EXAMPLE:

An independent-samples t-test was run to determine whether there were differences in height between men and women. The test (was/was not) significant (t(x) = -x, p = x). Men (M = x, SD = x) (do/do not) differ from women (M = x, SD = x) in height.

ALSO MUST INCLUDE:

If the t statistic is in the rejection region, reject the null hypothesis.

OR

If the t statistic is not in the rejection region, accept the null hypothesis.

W7A2

ALSO MUST INCLUDE: Was the null accepted or rejected? What does this mean to the results of the analysis? Explain.

EXAMPLES:

Independent Samples:

An independent-samples t-test was run to determine whether there were differences in satisfaction between men and women. The test (was / was not) significant (t(x) = -x, p = x). Men (M = x, SD = x) (do / do not) differ from women (M = x, SD = x) in their levels of satisfaction.

Paired Samples:

The participants were tested immediately after they viewed the movie and again one week later to see whether their satisfaction with the movie changed significantly. A paired-samples t-test was run, and it was found that the scores (did / did not) change significantly (t(x) = x, p = x). The mean satisfaction score immediately following the movie was x, SD = x. One week later, the satisfaction significantly (increased / not increased) with mean x, SD = x. The null hypothesis is (accepted/rejected) because...

W8A1

EXAMPLE:

A simple ANOVA was run to test the hypothesis that there are significant age differences across years in college. The results indicated that a significant difference (does / does not) exist, with F(x) = x, p = x. Post hoc tests using the Tukey method indicated that the age of freshmen (M = x) (were / were not) significantly lower than the age of seniors (M = x). The ages of sophomores (M = x) and juniors (M = x) (were / were not) significantly different from either freshman or seniors.

ALSO MUST INCLUDE:

The interpretation of the results – what do the results tell us about the hypothesis of age differences across years in college? What do the results mean?

W8A2

EXAMPLE:

A simple ANOVA was run to test the hypothesis that there would be differences in satisfaction levels depending on the type of movie participants viewed. The results indicated that a significant difference (does / does not) exist, with F(x) = x, p = x. Post hoc tests using the Tukey method indicated that the satisfaction with comedies (M = x) (was / was not) significantly higher than the satisfaction with action movies (M = x). Romantic comedies (M = x) (were / were not) rated differently from either comedies or action movies.

ALSO MUST INCLUDE:

The interpretation of the results – what do the results tell us about recall? About stress levels? About the relationship between the two? What do the results mean?

W9A1 and W9A2

ALSO MUST INCLUDE: Discuss the nature of the correlation when writing the results. Do not just state if there is a correlation or not. Explain what the correlation coefficient means. What do the results tell us about the relationship between the variables?

EXAMPLE for no correlation

To determine if a relationship exists between the age at which a movie is viewed and the satisfaction rating for that movie, a correlation was run and it was found there was no significant relationship between these two variables (r = x, p = x).

EXAMPLE for negative correlation

To determine if a relationship exists between the age at which a movie is viewed and the satisfaction rating for that movie, a correlation was run and it was found that there was a significant negative correlation (r = -x, p= x). As age increases, satisfaction decreases.

EXAMPLE for positive correlation

To determine if a relationship exists between the age at which a movie is viewed and the satisfaction rating for that movie, a correlation was run and it was found that there was a significant positive correlation (r = -x, p= x). As age increases, satisfaction increases.

W10A1 and W10A2

EXAMPLE:

Researchers were interested in determining whether a relationship exists between owning a home and owning a pet. A chi-square analysis was conducted, which returned x (x) = x, p = x suggesting that there is/is not a significant relationship between the two variables. Looking at the data , for those who own homes, x out of x people also own pets. On the other hand, for those who do not own their homes, only x out of x people own pets.

ALSO MUST INCLUDE: Discuss the nature of the results. Do not just state the results. Explain what the results mean. What do the results tell us about these variables and their relationship?