## PSY 315 Inferential Research and Statistics Project Part 2 Missed Appointments

PSY 315 Inferential Research and Statistics Project Part 2 Missed Appointments

PSY/315

Introduction

Our clinic is experiencing missed follow-up. This article is to ascertain whether there is any difference in the missed follow-up in the clinic due to text message or remainder call. Whether missed follow-up is less as compared to remainder call missed follow-up in our clinic.

Method Used

To reach a statistical conclusion we divide the sample into two groups. Sample size in each group is taken as 30. One group is getting a remainder call for follow-up appointments, and the other groups are getting follow-up message to decrease missed appointments. Missed appointments are observed in a facility database on a monthly foundation. So here we will examine one particular month and note down the missed appointments utilizing facility database. The data for the follow-up appointments for both the cases are shown in Table 1, where ‘1’ represent a person who follows up the appointment, and ‘0’ designate the person with missed appointments. Since the population standard deviation is not provided, we will use two independent sample t-test assuming equal variances.

Significance level:

First, examine both groups at the point level of 0.05 which is most common. Then, to discover whether at the 0.05 level of significance text message follow-up in a clinic is more efficient as compared to remainder calls.

Mean and Variance:

The mean of each group is computed by adding up all the answers divided by the total sample quantity. It is produced by the formula Mean =∑X/n. The variance of each group represents the spread of the data set.

 Descriptive Text message Remainder call Mean 0.6333 0.4 Variance 0.2402 0.2483

As the response,’0’, designate the individual who missed the follow-up appointment and,’1’, represent the individual who has taken a follow-up appointment, it is evident from the above table that individuals who were addressed using text messages are more to come for next scheduled appointment as compared to remainder calls.

Test Statistic:

We can consider the hypothesis statement as Ho:µ (text messages) ≤µ (remainder calls). Ha:µ ( text messages) > µ (remainder calls) where µ is the average of the responses obtained from the digital facility database for a specific month. From table 2 in the appendix, the t-value is 1.8285.

Critical value:

A critical value of the test is the minimum amount above that if the test statistic is greater leads to the rejection of the null hypothesis. We have measures of freedom for the above test as 58. From Table 2, critical t-value. For one-tailed, critical =1.6716 and for two-tailed critical = 2.0017 at the 0.05 level of significance. Right-Tailed t-test: As we have to validate the research claim that text message has less missed appointments or more follow-up as compared to remainder calls, therefore, it is a right-tailed test. Decision: Reject Ho, if t(58) > 1.6716. From table 2 in the appendix, the t-value is 1.8285. Utilizing the critical value approach, rejecting the null hypothesis as the t(58) = 1.8285 > t-critical (1.6716).

Conclusion:

Since the null hypothesis is rejected, it can be resolved that the text message follow-up is higher as opposed to remainder calls follow up at the 0.05 level of significance.

Conclusion

As a manager of the clinic, I came to the statistical outcome that the text message follow-up is higher when compared to remainder calls follow up. It is true as individuals at the time of the call may be involved in some productive task that prevents them from answering a call at that point. Following up with the text message is better suited in this busy world where the individual can read the text message later and can respond accordingly. Therefore, I conclude that text messages are a more efficient means to decrease missed appointments.

Tables

Table 1

Remainder call Text message 1 1 0 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 1 0

Sample size of 30 in each case.

Table 2

Text message Remainder call
Mean 0.6333 0.4
Variance 0.2402 0.2483
Observations 30 30
Pooled Variance 0.2443
Hypothesized Mean Difference 0
df 58
t Stat 1.8285
P(T<=t) one-tail 0.0363
t Critical one-tail 1.6716
P(T<=t) two-tail 0.0726
t Critical two-tail 2.0017

References

Miller, I., Miller, m., Freund, J. E., & Miller, I. (2004). John E. Freund’s mathematical statistics with applications. Upper Saddle River, NJ: Prentice hall. Rice, J. A. (1995). Mathematical statistics and data analysis. Belmont, CA: Duxbury Press.

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