Inferential Research and Statistics Project

PSYCHOLOGY 1

PSYCHOLOGY 2

 

Applied Psychology

Patrice Scope

PSY/315

University of phoenix

Instructor’s Name

Date

 

Applied Psychology

The issue addressed in this paper missed appointments in its primary and specialty care clinics. Therefore, our main objective would be to devise methods that will help to minimize the average number of missed appointments. This will be done by the comparison of the effects of the two methods employed by the facility to handle the appointments made by the facility. Also, the management has previewed some articles in order to determine which method provides the least amount of the missed appointments and as a result, the organization will have to send out text messages to the clients to remind them of the coming appointment events so as to always keep them aware.

Background of the research that can affect research hypothesis

The research on the text-messaging versus telephone reminders to reduce missed appointments in primary and specialty care clinic is likely to affect the research hypothesis since it contradicts the findings of the hypothesis. It has been found that both the telephone and text-message reminders portray a positive response in minimizing the rate of missed appointments. Therefore, due to the fact that text messaging is both cost effective and less time consuming, the research hypothesis is that the patient responds well to text messages reminders as compared to how they respond to the voice call reminders.

Null hypothesis is HO: text messaging reminder=telephone calls reminder

Research hypothesis is H1: text messaging reminder ≥ telephone call reminders

Dependent variable: the patients responded to the reminder

Independent variables: telephone calls reminder and text-messaging reminders

Convenience sampling method

The sampling method I will use is the randomization method where by the first group will belong to the text-messaging reminders, and the second group to the telephone call reminders of the total number of people who volunteered to take part in the study. The convenience sampling method is useful in the comparison of two phenomenon in this case this method will be important since it compares the two methods of reminders, that include the telephone call and text-messaging reminders (Hu & Qin, 2018).

Simple random sample

This type of sampling method takes into consideration of the each element having an equal opportunity of selection rather than just concentrating on a cluster data. Each element in this simple random sampling method is selected independently.

Importance of collecting descriptive data

I will include the descriptive data in the research because of the fact that it enhances the visualization of data in a more clear and diversified manner. Taking an example of the gender age, it will be important to factor in how the people of the opposite gender and different age group respond to both the text-messaging reminder and telephone call reminders. Generally, the old people are more likely to respond to telephone reminders as compared to the young people who are more likely to respond to both telephone calls and text-messaging reminders (Oakshott, 2016).

In addition, comparing the age and gender of the people taking part in the analysis will help in the development of the tabulated description and therefore easy to represent the results in graphical manner, which is useful in summarizing the group. Inferential Research and Statistics Project

 

References

Hu, Z., & Qin, J. (2018). Generalizability of causal inference in observational studies under retrospective convenience sampling. Statistics in Medicine, 37(19), 2874-2883. doi:10.1002/sim.7808

Oakshott, L. (2016). Collecting data: surveys and samples. Essential Quantitative Methods, 30-49. doi:10.1007/978-1-137-51856-9_3

SAMPLE TEST STATISTICS.

 

SAMPLE TEST STATISTICS 2

Sample Test Statistics

Patrice scope

PSY/315

University of Phoenix

Supervisor

 

Describe what method you are using to compare groups

In the analysis, the two-sample t-statistics were used, the test is used to carry out the mean difference of the two samples.

ADKAR		Prosci
Mean	5.61	Mean	7.326667
Standard Error	0.399924	Standard Error	0.248718
Median	5.6	Median	7.3
Mode	5.6	Mode	8.7
Standard Deviation	2.190473	Standard Deviation	1.362283.
Sample Variance	4.798172	Sample Variance	1.855816
Kurtosis	-0.07445	Kurtosis	-0.11892
Skewness	-0.69709	Skewness	-0.01529
Range	7.9	Range	5.6
Minimum	1.1	Minimum	4.6
Maximum	9	Maximum	10.2
Sum	168.3	Sum	219.8
Count	30	Count	30

 

Mean	6.875	3
Variance	3.7789888	12.993738738
Observations	30	30
Pooled variance	12.222333
Hypothesized mean difference	0
Df	10
t stat	-1.921237697
P(T <= t) one-tail	0.0066669023
t Critical one-tail	1.33445889
P(T <= t) two-tail	0.34455893
t Critical two-tail	1.335

 

 

The null hypothesis: u- u= 0

Alternative hypothesis: u- u ≠ 0

The hypothesis consists of a two-tailed test. We will reject the null hypothesis if we obtain the between the sample mean to be too small or too large.

By use of the sample data that is available we shall compute the SE (standard error), degree of freedom, and the test statistics

SE= sqrt [(s12/n1) + (s22/n2)]

Therefore SE= 047096

D.F = 49 obtained from the calculator

t = [(x1 – x2) – d ] / SE

 

t= -3.65

Where S1 represent standard deviation for sample 1, and S2 is that of sample 2, n1 is the sample size of 1 and n2 represent that of 2. The d that was used in this scenario represents the hypothesized difference the standard error, and the population means

since we are using two-tailed statistics the p-value is the probability of having t statistic to be 49 degrees of freedom and obtain -3.6. Thus, the p-value is 0.0000636

What is the significance level of the comparison?

In this test, we are going to use o.05 level of significant. The sample data will be used to conduct a two-sample t-test for the null hypothesis.

What was the means and variance for each variable?

The two variables resulted in different means and variance. Mean, and sample variances were observed directly from the table of analysis. The mean of ADKAR is 5.61 while that of Prosci was obtained to be 7.23667 from the sample of the information that is obtained.

What was the alpha level you identified in Week 3?

In week three, we took the alpha level to be taken as α= 0.05, this thus representing 0.10 level of significance.

 What was the test statistic?

The t-statistic in this variable was obtained to be -1.921237697 for the population.

What was the critical value for both the one- and two-tailed test?

The critical value of two value obtained for both one tailed and two tailed samples is 1.33445889, and that of two-tailed was obtained to be 1.335.

Were you able to reject the null hypothesis? In other words, did you prove there was a difference?

We obtained the p-value to be 0.000636, which is less than the significance level (0.10); thus, we reject the null hypothesis. There is a huge difference between the two samples. Therefore, the test concludes that the mean of ADKAR and that of Prosci are significant different

Talk about what these results mean in everyday language and context to your chosen scenario

The result from t-statistic shows there is a high difference between the two changes, and thus, a lot of changes is expected if the firm decides to implement any of the changes. Human being responds slowly to the changes, and therefore this makes it hard for the organization to implement changes effectively. Adoption of policy will mean a lot of things must deviate from people regular conducts.

Make a recommendation based on the findings.

In conclusion, according to the result and observation, I would recommend the organization not to make changes since these changes are going to affect how workers operate directly, and thus, this may affect the workers. Workers are not used to this change, and it makes them change their ways of doing a thing, and thus it’s essential to consider finding an alternative way to respond to what is affecting the organization rather than implementing these changes. Inferential Research and Statistics Project