Two Parts: Part I – Qualitative; Part II – Quantitative. Part I (Qualitative) Build Your Own Questionnaire! In your Week 7 Discussion Question, you explore

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Two Parts: Part I – Qualitative; Part II – Quantitative. Part I (Qualitative)
Build Your Own Questionnaire! In your Week 7 Discussion Question, you explored and identified factors that influence adjustment issues and their impact on academic performance of university students. In this assignment, your task is to create a survey instrument (consisting of no less than 10 items) to assess various dimensions of student adjustment in the US. 
Before creating your survey questionnaire, review Chapters 14 and 15 of the Zikmund textbook and use the Library and Information Resources Network of Westcliff University (LIRN) to find resources (including dissertations and theses) that address a similar topic. 
Note that you are not allowed to use existing survey instruments at all for this assignment. Existing instruments are mostly copyrighted materials and should not be used without proper permission from the author. 

Based on your response and the feedback from the professor to your Week 7 DQ, construct your own survey instrument and present it as an attachment to your PA 2.       
After constructing the instrument, write a paper of minimum three (3) APA-formatted pages, and provide the background on the process of creating the instrument: Explain different dimensions and subscales of the instrument; discuss reliability and validity issues; and then explain the scoring process (including reverse coding if needed).

Part II (Quantitative)
Note: If you do not have access to SPSS, you can do this assignment in Excel as instructed below.
Use the Excel file titled, General-Electric (GE) available on GAP under the “Supplemental Material” folder. This file contains GE’s daily stock market data covering the period of 12/13/2010 to 12/11/2018. The file includes a total of 2,013 daily transaction records including date, opening price of the GE stock for the day, highest price, lowest price, closing price, closing price adjusted for dividends, and the number of stocks traded (volume). 
Complete the following tasks on GE’s stock and copy your results in a word document and submit your report.

Use the explore command in SPSS and explain whether the trading volume of the stock is normally distributed. Make sure to discuss, mean, median, standard deviation, skewness, kurtosis, and results from the test of normality. 

(If you do not have access to SPSS, in Excel use the Data Analysis on the “DATA” tab, then select descriptive statistics).

Select a random sample of exactly 125 observations. Then run the descriptive command and calculate the mean and standard deviation of the sample. Calculate the 95% confidence interval for the mean and verify if the population mean is within the estimated confidence interval.

(If you do not have access to SPSS, in Excel use the Sampling on the Data Analysis tab to select your sample).

Suppose you believe that the true average daily trade volume for General Electric stock is 49,829,719 shares and a standard deviation of 21,059,637 shares. Considering a 95% confidence level: 

What is the minimum required sample size if you would like your sampling error to be limited to 1,000,000 shares? 
What sample size would offer a sampling error of not more than 2,000,000 shares?

Refer to the sample size of 125 that you found in the previous section above, 

Conduct a one-population test of hypothesis for the mean of the volume and determine if the null hypothesis should be rejected or not. Running head: ANALYSIS 1

QUESTIONS 2

Analysis

Student Name

Professor’s Name

Course Title

Due Date

Question one

Discussion on whether the trading volume of the stock is normally distributed

The following are the results after running the data in SPSS on trading volume.

Data skewness is 3.742; its kurtosis is 21.678. An acceptable range for skewness and kurtosis are (-1,1) and (-2,2), respectively, for any normally distributed data (Polat, 2017). The above two values for skewness and kurtosis are not within the range, indicating that the data is not normally distributed.

From the above, both tests of Kolmogorov and Shapiro show that the test p-value is less than 0.05 alpha level. If the significance is less than the alpha level, it means that the data is not normally distributed.

The histogram also confirms the above; it doesn’t show normality. The Q-Q plot chart above also gives a visual representation of the volume in trading. If the data distribution follows a normal distribution, it means that the dots would mostly follow the trend line above (SPSS, 2020). As from the above, the data fails to cluster along the line that is further showing proof that the data is not normal.

Part three

Histogram of the mean value

Histogram of the standard deviation

Part Four

Descriptive

Statistic

Std. Error

volume Mean

Average

55503343.904000

255008.1319279

95% CI for the average

Lower Value

54990885.876811

Upper Value

56015801.931189

Five % Trimmed average

55434651.920000

Median

55463082.400000

Variance

3251457367467.773

Std. Deviation

1803179.7934393

Least

5.2607E+007

Highest

5.9697E+007

Range

7089888.0000

Interquartile Range

977258.6000

Skewness

.635

.337

Kurtosis

.253

.662

Volume Standard deviation

Average

23571236.936785

466150.7050674

95% CI for the average

Lower Value

22634472.023111

Upper Value

24508001.850459

5% Trimmed Average

23659093.280301

Median

24480818.124752

Volume Variance

10864823991743.800

Volume Std. Deviation

3296183.2460808

Least

1.7411E+007

Highest

2.8189E+007

Range

10777618.1063

Interquartile Range

2568914.7069

Skewness

-.770

.337

Kurtosis

-.511

.662

The mean and standard deviation of the sample data created are 55503343.90 and 1803179.793, and that for the volume standard deviation data are 23571236.94 and 3296183.246, respectively. The skewness and kurtosis for Volume mean are 0.635 and 0.253, respectively. The two do not lie between the range discussed of the data following a normal distribution. Thus, the volume formed from the sample mean does not show any sign of normality. The value for the volume standard deviation does not follow a normal distribution too.

Testing for Normality

Kolmogorov-Smirnova

value

Degrees of freedom

Significance

Statistic

Degrees of freedom

Significance

volume Mean

.199

50

.000

.907

50

.001

The test for normality for the newly formed sample data is as shown above. The significance level for both Kolmogorov-Smirnov and Shapiro-Wilk is 0.000 and 0.001, respectively. Since both are less than 0.05 alpha level, it means that the calculated data is not normal.

The Q-Q graph above displays how the data points fails to align with the trend line indicating that they are not normal data points or the data fails to show normality.

Tests of Normality

Kolmogorov-Smirnova

Value

Degrees of freedom

p-value

Value

Degrees of freedom

p-value

Volume Standard deviation

.331

50

.000

.812

50

.000

The tests of normality above indicate that the level of significance is 0.000 for the two indicated tests. Since the two values are below 0.0 alpha level, it means that the data does not follow a normal distribution.

As can be viewed from the Normal Q-Q graph above, the standard deviation values do not follow the trend line, indicating that they are not following the normal distribution (Laerd, 2020).

Part Five

With a 95 percent confidence level, it means that the corresponding value from the z-statistics is 1.96. We are told that the average daily trade volume is 49829719, and the standard deviation is 21,059,637 shares. The formula gives sampling error

Z* sigma/ sqrt (n)

So, the minimum required sample size would be as shown below.

10,000,000 = 21,059,637/ sqrt(n)

Sqrt (n) = 21,059,637/10,000,000

Sqrt (n) = 2.1059637

n =2.10937 * 2.10937

n = 4.4308

An n of 4.4 would represent an approximate of around five members as the sample.

For a sampling error of 20,000,000, the sample size would be as follows?

20,000,000 = 21,059,637/ sqrt(n)

Sqrt (n) = 21,059,637/ 20,000,000

Sqrt (n) = 21,059,637

Sqrt (n) = 1.05 * 1.05

n= 1.108

n would be around 2 samples.

If N = 2013, then the calculations would be as follows

Sampling error would be calculated as 21,059,637/ sqrt (2013)

The sampling error would be given as 469385

Part Six

The independent t-test is calculated using the formula

Since M2017 = 46108055, S2017 = 34099055, n2017 = 251, M2018 = 87241844, S2018 = 50977722, n2018 = 238.

t= (46108055 – 87241844)/ sqrt (340990552/251 + 509777222/238)

t=-21986

The t-statistic is -21986, the df from the calculation is n for 2017 + n for 2018 – 2.

The calculation for df would be 251 +238 -2 which gives 487. From these values, the average trading volume in 2017 and 2018 is not statistically significant.

References Laerd. (2020). Testing for normality using SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/testing-for-normality-using-spss-statistics.php#:~:text=value%20of%20the%20Shapiro-Wilk,enhanced%20testing%20for%20normality%20guide. Polat. (2017). What is the acceptable range of skewness and kurtosis for normal distribution of data if sig value is <0.05? Retrieved from https://www.researchgate.net/post/What_is_the_acceptable_range_of_skewness_and_kurtosis_for_normal_distribution_of_data_if_sig_value_is_005#:~:text=Some%20says%20for%20skewness%20(−1,skewness%20is%20an%20acceptable%20range. SPSS. (2020). Test for Normality in SPSS. Retrieved from https://ezspss.com/test-for-normality-in-spss/

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