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Analyzing the Psychological Characteristics of Basketball Players in SPSS

October 26, 2023
Luca Ruiz
Luca Ruiz
🇨🇦 Canada
SPSS
Luca Ruiz is an experienced SPSS Assignment Helper who has completed more than 1800 assignments. He is from Canada and holds a Master’s in Statistics from Dalhousie University. Luca specializes in SPSS assignments, providing expert guidance and support to students, ensuring their success in mastering statistical software.
SPSS
Key Topics
  • Problem Description:
  • Solution
  • Conclusion
  • Discussion
  • References
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In this comprehensive SPSS analysis, we examine relationship between playing positions, gender, and the psychological characteristics of basketball players. Our objective is to scrutinize whether these factors play a significant role in shaping the mental attributes of athletes in the basketball arena. To accomplish this, we have undertaken a thorough examination, comprising a series of statistical tests and assessments. This investigation aims to provide valuable insights into the intricate interplay of these variables within the context of the sport and contribute to the broader understanding of psychological aspects in basketball.

Problem Description:

In this SPSS assignment, we explore the relationship between playing positions, gender, and psychological characteristics among a sample of basketball players. The primary aim is to assess whether these factors have a significant impact on the psychological traits of the players. To achieve this, we conducted various statistical analyses and tests, providing a comprehensive understanding of the dataset.

Solution

Table 1:

Descriptive Statistics of the Data

GroupN
Gender:Male:14
Female:57
Age:10-19:3
20-29:54
30+:13
Missing:1
Playing Positions:Guards15
Forwards37
Centers19
Years Competing:0 to 2 years10
3 to 5 years29
6-10 years16
11 to 15 years6
More than 15 years10
N= 71

[PARAGRAPH ABOUT DESCRIPTIVE STATISTICS]

Normality and Reliability tests were performed in order to provide the conditions of MANOVA, nothing was found to cause a problem.

Table 2:

Reliability Test

VariableCronbach's Alpha
AdvRespMean0,839
ImagActPrepMean0,864
SelfDirContManMean0,598
PerfecTendMean0,709
SocialSuppMean0,544
ActiveCopingMean0,738
ClinIndicMean0,763

In accordance with Cramer and Bock (1966), a MANOVA was first performed on the means to help protect against inflating the Type 1 error rate in the follow-up ANOVAs and post-hoc comparisons. However, prior to conducting the MANOVA, a series of Pearson correlations were performed between all of the dependent variables in order to test the MANOVA assumption that the dependent variables would be correlated with each other in the moderate range (i.e., .20 - .60; Meyers, Gampst, & Guarino, 2006). As can be seen in Table 1, a meaningful pattern of correlations was observed amongst most of the dependent variables, suggesting the appropriateness of a MANOVA.

Table 3:

Pearson Correlations

1.2.3.4.5.6.7.MSD
AdvRespMean12,830,53
ImagActPrepMean0,15613,380,62
SelfDirContManMean0,520,19212,920,47
PerfecTendMean0,4390,370,39513,020,55
SocialSuppMean0,5150,4340,40,56412,970,50
ActiveCopingMean0,1860,5610,30,4170,4413,210,55
ClinIndicMean0,4440,0070,4730,5520,4110,10413,000,64

Note. N = 72; correlations greater than .10 are statistically (p < .01).

MANOVA was conducted to test the hypothesis1 that playing position among basketball players has a positive impact on psychological characteristics (guards, forwards, centers). A statistically non-significant MANOVA effect was obtained, Wilks’ Lambda = .68; F = 1,26 p > 0.05. Based on the findings, it can be said that "the positions of basketball players have no effect on their psychological characteristics" and therefore hypothesis1 was rejected.

Table 4:

MANOVA test result of Hypothesis 1

VariablesPlaying Position of ParticipantsLevene's
pFFp
AdvRespMean0,8650,1450,7610,581
ImagActPrepMean0,2241,5290,6050,696
SelfDirContManMean0,1781,7751,8170,122
PerfecTendMean0,2581,3850,6270,68
SocialSuppMean0,2361,4770,5520,736
ActiveCopingMean0,6070,5030,3870,856
ClinIndicMean0,6630,4141,1990,32

Even though the question was structured as 3 groups (Male, Female, and Prefer not to Specify) since there are no “Prefer not to Specify” options selected. Since MANOVA test cannot be done with 2 groups, instead of the MANOVA test, a T-test was conducted to test hypothesis2 that gender has a great influence on the development of psychological characteristics of basketball players. According to the T-test which is given an insignificant result, “Gender doesn’t have a great influence on the development of psychological characteristics of basketball players.”

Table 5:

T-test results of Hypothesis 2

VariablesGroupsNXSdT-test
tsdp
AdvRespMeanMale142,730,55-0,8345690,407
Female572,860,53
ImagActPrepMeanMale143,450,540,4864690,628
Female573,360,64
SelfDirContManMeanMale142,900,42-0,1834690,855
Female572,930,48
PerfecTendMeanMale143,060,470,3247690,746
Female573,010,57
SocialSuppMeanMale142,820,42-1,2592690,212
Female573,000,51
ActiveCopingMeanMale143,260,580,4181690,677
Female573,190,55
ClinIndicMeanMale143,130,690,8757690,384

Conclusion

Based on the MANOVA test that was conducted it is clear that gender or players’ position does not impact their psychological state.

Discussion

The small scale (N = 71) of the data on which the analyzes were made may have caused the scale not to yield the desired result. It can be tested with a larger scale of data (for example N = 404) to be of global importance and scale to work better.

References

Cramer, E. M., & Bock, R. D. (1966). Multivariate analysis. Review of Educational Research, 36, 604-617.

Meyers, L.S., Gamst, G., & Guarino, A. (2006). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publishers.

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