Psychology Data Interpretation for Lab Report -myassignmenthelp

Question: Discuss about thePsychology Data Interpretation for Lab Report. Answer: Lab Report Plan: The lab report plan includes research planning; five peers reviewed by journal articles, clearly stated hypotheses and their testing results and finally predicted relationship between several variables. Our research plot includes the aim to analyze the descriptive summary, scatter plot among several variables, correlation coefficient between variables and individual index rating of the variables. Our five peer reviewed by journal articles is given with relevant to the topic. We are going to provide clearly stated five null hypotheses that are tested in the assignment. Cross Cultural Differences:- The dataset involves the dataset of 312 students and the factors are Gender, Age, Nationality, Individualism Rating, Idiocentric score, Group score and Allocentric score. The responses were chosen according to group, idiocentric and allocentric self-references. The different scores were highlighted by the individualism rating appeared in the questionnaire. As predicted by the analyzed data, students from collectivist culture generate significantly more idiocentric and group descriptions than the students from individualist cultures of different nationality. The data backs up a pan cultural model in which cross-cultural variability in the self-concept are not marked as categorically different. However, it would reflect the relative ethics of the constituent components. Cross-cultural studies of self-concept remain to be an interest to the psychologists. Psychological factors in Asia and Asia-Pacific Island cultures have constructed the more relational, collectivistic or socio-centric ideas in these cultures with more individualistic and idiocentric ideas of Western concepts (Lonner, 2013). Psychologists argue that the individuality is a cultural construction (Guchner, 1994). We could expect significant cultural differences associated with the gender and age in the different nationality. Cross-cultural studies and concepts in a sampled limited range of cultures depend on the data measures. Explanatory variables are hypothesized to predict cross cultural effects and cultural differences in inter-nations (Mezulis et al., 2014). The report addresses the limitations while testing three theoretical ratios on cultural differences in self content. The psychologists have identified the existence of adaptive significance in combination with the aspects of all cultures. We expect that the people of all cultures infer the variables as part of individuality. Trait psychology dimensions would support in all cultures described in terms of trait attributes and variables with at least moderate frequency (Shiraev and Levy, 2014). Cultural psychologists view socially constructed variables across cultures. We tested alternative theoretical hypotheses successfully on cultural differences in the context of cross-cultural equivalence. The titles of reviewed journal articles those are relevant to the topic: Introduction to Statistics and SPSS in Psychology. Pearson. The article provides us the idea about what types of psychological data variables and factors could be. Statistics explained. Routledge. The article suggests us about what are the different measures of descriptive statistics (such as mean, standard deviation, correlation coefficient, 2-tail test). Tests of statistical significance. The article helps us to test the statistical significance of two-tailed test. Performing data analysis using IBM SPSS. John Wiley Sons. We got the idea about how inference could be drawn from scatter plots of SPSS. Cross-cultural psychology. In Encyclopedia of Sciences and Religions - This article helped us to develop ideas about cross-cultural psychology and their relevance with data attributes. Aim of the study report: The objective of the study report is to find the correlation coefficient between different psychological indexes and find out the inferences. We can have a clear report of nation wise and gender wise variation of psychological indexes. The report would help to provide the necessary measures and steps concluded from the findings. Hypotheses: Tests of Hypothesis: The five hypotheses given in the report are Percentage ratio of different genders according to the nationality is equal. Averages of different ratios (Idiocentric, group and Allocentric ratio) are equal. Correlation between Individual ratings and Idocentric statement Scores is zero. The relationship between Individual ratings and Group statement Scores is zero. Correlation between Individual ratings and Allocentric Scores is zero. Inferences from Hypothesis: The descriptive summary of the gender indicates that female category has maximum and others category has minimum frequency and percentage (Mayers, 2013). The descriptive summary of 312 participants provided data concludes that Australia has maximum frequency (157) and hence percentage (50.3) of nationality. Singapore is preceding Australia with frequency (83) and the percentage (26.6). In case of Individual rating, the highest individual rating is 91 among 312 participants with the frequency 3. The individual rating (90) has maximum frequency (157). Surprisingly, 92 (second highest frequency) students provided vary poor individual rating of 20. The age summary of 312 individuals show that the minimum age of a student is 17 and maximum is 56. Mean and standard deviation of age are respectively 23.657 and 5.9781. Similarly, descriptive statistic of idiocentric, Group and Allocentric ratios interpret that both the mean and standard deviation is highest in idiocentric ratio and least in allocentric ratio. The maximum and minimum values are also higher and lower in the same two ratios respectively (Mayers et al., 2013). With the help of scatter plots, we infer that - Individual rating and Idiocentric ratio: Pearson correlation coefficient (r) = 0.454. Significance (2-tailed) = 0. Correlaion is positive and moderate. These two values have significant relationship with each other at 10% confidence level. Individual rating and Group ratio: Pearson correlation coefficient (r) = (-0.29). Significance (2-tailed) = 0. Correlaion is negative and weak. These two values have significant relationship with each other at 10% confidence level. Individual rating and Allocentric ratio: Pearson correlation coefficient (r) = (-0.401). Significance (2-tailed) = 0. Correlaion is negative and moderate. These two values have significant relationship with each other at 10% confidence level. Relevant Information for Selection of Method: Participants: The columns of the dataset tell about the data of 312 participants. They all are the students of Murodoch University in Australia. Among them 218 are females, 90 are males and 4 are others. They have delivered their gender, age, nationality and individualism rating of their nationality. Materials: The used materials of the report are presented in the list of references. Procedure: The procedure is simple and easy. Firstly, we find out descriptive statistic, frequency and relative frequency table, percentage calculation of the variables like gender, nationality and individualism ratings. Next, we calculate the descriptive statistics and summary measures such as minimum, maximum, mean and standard deviation of Age, Idiocentric ratio, Group ratio and Allocentric ratio (Hinton, 2014). We could compare mean of different types of ratio. We created a scatter plot and correlation between of Individualism ratio and Idiocentric ratio, Individual rating and Group score, Individual rating and Allocentric ratio. Persons coefficient of correlation (r) is deciding the significance of effects between all relationships. References: Bochner, S. (1994). Cross-cultural differences in the self concept: A test of Hofstede's individualism/collectivism distinction.Journal of cross-cultural psychology,25(2), 273-283. Hinton, P. R. (2014).Statistics explained. Routledge. Lonner, W. J. (2013). Cross-cultural psychology. In Encyclopedia of Sciences and Religions (pp. 561-564). Springer Netherlands.. Mayers, A. (2013).Introduction to Statistics and SPSS in Psychology. Pearson. Meyers, L. S., Gamst, G. C., Guarino, A. J. (2013).Performing data analysis using IBM SPSS. John Wiley Sons. Mezulis, A. H., Abramson, L. Y., Hyde, J. S., Hankin, B. L. (2014). Is there a universal positivity bias in attributions? A meta-analytic review of individual, developmental, and cultural differences in the self-serving attributional bias.Psychological bulletin,130(5), 711. Shiraev, E., Levy, D. A. (2014). Cross-cultural psychology. Pearson Education Limited