# Week 4 SPSS Output Study

Week4 SPSS Output Study

Week4 SPSS Output Study

ResearchQuestion

Areteams with more experience per member more effective than the teamswith less experience per member?

Nullhypothesis (H0):There is no difference in the projects completed per month(effectiveness) between the three-team types.

Alternativehypothesis (H1):There is a difference in the projects completed per month(effectiveness) between the three-team types.

Theindependent variable is Team type, which is discrete, categorical andhas a nominal scale of measurement while our dependent variable isprojects completed per month (effectiveness) which is a discrete,quantitative variable with an interval scale of measurement.

DescriptiveStatistics

Thisstudy included 15 individuals who participated in the study with eachgroup having five members. In overall all the teams had an average of4.44 (SD=2.273) projects completed per month. The mean score for TeamA was at 3.42, while the standard deviation stood at 1.311 for theprojects completed per month. For team B, the mean was at 7.17 andstandard deviation at (SD=1.115) for the projects completed permonth. On the other hand, for team C, the average was 2.75 with astandard deviation at 1.055 for the projects completed every year.The measures of central tendency are the mean, median, and mode. Themean gives us the average while the median gives us the middle scoreand the mode give us the most frequent score. The mean for projectscompleted per month is 4.44 while the median is 4 and the mode is 3.For the team type, the median is 2. The measures of variation are thestandard deviation, range, and interquartile range. The standarddeviation tells us how the values of the variable are dispersed fromthe mean while the range is the difference between the maximum valueand the minimum value and the interquartile range is the differenceof the upper quartile and the lower quartile(Groebneret al., 2004). The standard deviation for projects completed permonth is 2.273 the range is 2, and the interquartile range is 3.75.For team type, the standard deviation is 0.828, the range is 2.

Analysis

 Table 1: ANOVA Table   Sum of Squares df Mean Square F Sig. Between Groups 136.056 2 68.028 50.072 .000 Within Groups 44.833 33 1.359     Total 180.889 35
 Table 2: Tukey post hoc test (I) Team type Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Team A Team B -3.750* .476 .000 -4.92 -2.58 Team C .667 .476 .352 -.50 1.83 Team B Team A 3.750* .476 .000 2.58 4.92 Team C 4.417* .476 .000 3.25 5.58 Team C Team A -.667 .476 .352 -1.83 .50 Team B -4.417* .476 .000 -5.58 -3.25 *. The mean difference is significant at the 0.05 level. Table 3: Team type Descriptive statistics   N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Lower Bound Upper Bound Team A 12 3.42 1.311 .379 2.58 4.25 Team B 12 7.17 1.115 .322 6.46 7.87 Team C 12 2.75 1.055 .305 2.08 3.42 Total 36 4.44 2.273 .379 3.68 5.21

Inthis study, a one-way ANOVA was carried out to determine if there wasa significant difference in the number of projects completed permonth between the three teams that are a discrete quantitativedependent variable and only one dependent variable with more than twocategories(Heiberger &amp Neuwirth, 2009).From the analysis (Table 1), it is established that there was asignificant difference in the completed projects every month betweenthe three teams F (2, 33) = 50.072, P=0.0001. Hence, from theresults, the null hypothesis is rejected since the p-value is lessthan 0.05. A turkey post hoc test was conducted to determine whichteams had a significant difference in the projects completed permonth. The tukey post hoc test (Table 2) revealed that projectscompleted per month (Effectiveness) was statistically significantlylower for team A (3.417+-0.336) and team C (2.75+-0.336) compared toteam B (7.167+-0.336). However, there was no statisticallysignificant difference in the projects completed per month betweenteam A and C (P=0.352).

Theone-way ANOVA carried out was two tailed. From the results, we canconclude that team B was more effective than both team A and B. Alsothere is no significant difference in the effectiveness of team A andteam However we cannot conclude that the only experience is the onlyfactor in the effectiveness of employees. This can be clearly shownas much as team B, which had more experienced than team A was moreeffective there is also the case of team B being more effective thanteam C yet team C had more experienced members than team c.

Therefore,more research needs to be done to determine which other factors dolead to more effective of a team so that the manager can use a bettercriterion to come up with the teams so that he can improve on theeffectiveness of his employees and not just teaming up employeesbased on their years of experience.

Ininterpreting the data, the researcher should take into considerationany form of biases or existence of outlier to help make goodconclusions (Morris, 2007). These considerations are made especiallywhen the data is normally distributed. Additionally, the researchquestion has to be considered to help in drawing effective andappropriate conclusions and interpretation.

References

Groebner,D. F., Shannon, P. W., Fry, P. C., &amp Smith, K. D. (2004).Business statistics.&nbspADecision making approach,&nbsp5.

Heiberger,R. M., &amp Neuwirth, E. (2009). One-way anova. In&nbspRThrough Excel(pp.165-191). Springer New York.

Morris,S. B. (2007). &quot Methods of Meta-Analysis: Correcting Error andBias in Research Findings&quot by John E. Hunter and Frank L.Schmidt.OrganizationalResearch Methods.