StudentShare
Contact Us
Sign In / Sign Up for FREE
Search
Go to advanced search...
Free

Quantitative Data Analysis and Computing With SPSS - Report Example

Summary
The paper "Quantitative Data Analysis and Computing With SPSS" describes that the percentage of respondents that were personally victimized differed by age group that is there is an association between respondent’s age (group) and personal victimization…
Download full paper File format: .doc, available for editing
GRAB THE BEST PAPER91.6% of users find it useful

Extract of sample "Quantitative Data Analysis and Computing With SPSS"

Quantitative Data Analysis and Computing With SPSS (Mohammed) 25/05 Quantitative Data Analysis and Computing With SPSS Introduction There are two types of statistics: descriptive statistics and inferential statistics. Descriptive statistics refers to the collection, organization, presentation, and summary of data by either using charts and graphs or using a numerical summary and inferential statistics refers to generalizing from a sample to a population, estimating unknown parameters, drawing conclusions, and making decisions (Doane & Seward, 2009). This paper will describe the nature of the population of England and Wales as it appears in the British Crime Survey Statistics 1984 (by selecting approximately six variables) using the appropriate statistical tests and graphs. Further, this paper will investigate whether any of variables are associated with the variable personal victimization and, if so, how. The main aim of this assessment is to use SPSS to analyse data and knowledge of statistics to interpret the output and demonstrate an understanding of both univariate and bivariate analysis. Question – 1 Describe the nature of the population of England and Wales as it appears in the British Crime Survey Statistics 1984 (bcsteac.sav file) using the appropriate statistical tests and graphs. The univariate statistical analysis presents the demographic and socio-economic profile of England and Wales for the year 1984. The univariate statistical analysis involves only one statistical variable. The variables taken to answer this part of question from the British Crime Survey Statistics 1984 are age, sex, marital status, age on leaving full time education, house structure, duration of stay at present address and working status (last week) of the respondents. 1.1) Age of Respondents To describe the demographic profile of the respondents, it is important to know the age of the respondents. Age is measured on ratio scale. Table 1 shows the summary statistics for the age of the respondents. Table 1 Summary Statistics for age of the respondents Age of respondent N Valid 1394 Missing 12 Mean 45.40 Median 44.00 Mode 25 Std. Deviation 18.817 Variance 354.065 Skewness .262 Kurtosis -1.000 Range 77 Minimum 16 Maximum 93 The mean is the arithmetic average of a distribution of scores and is determined by adding the scores and dividing this sum by the number of scores (Hinkle, Wiersma & Jurs, 1981). The average (mean) age of the respondents was about 45.4 years. The median represents the middle value of the data that divides the population into two equal parts. About half of the respondent’s age was equal to or less than 44 years. The mode is the value that occurs more frequently. Most of the respondent’s age was equal to 25 years. The minimum and maximum age of the respondents was 16 years and 93 years, respectively. The range gives us an idea of dispersion of the data and is the difference between the highest and lowest values (Harper, 1991). The range of the respondent’s age was 77 years. The minimum and maximum values could be outliers, which is why the range might exaggerate the extent of dispersion around the mean. Thus, with range, it is necessary to be very careful and it cannot be trusted because it depends upon two values only. The variance indicates the extent to which the data is dispersed and is defined as the ‘mean of the squared deviated scores (Hinkle, Wiersma & Jurs, 1981:44). The standard deviation is an ‘an index of the amount of variability of scores around the mean of the scores’ (Howitt & Cramer, 2003:54). Interpreting the standard deviation along with the mean tells us something about how much spread there is in the scores, and important properties of the distribution relate to how far away from the mean we move, in terms of standard deviation (see Punch, 2000). The larger the standard deviation, the more spread out is the distribution (Wright, 1998). The standard deviation for the age of the respondents was about 18.8 years. This indicates that the age of respondents was more dispersed. Skewness is defined as ‘an index of the asymmetry or lop-sidedness of the distribution of scores on a variable (Howitt & Cramer, 2003:47-8). If the value of skewness coefficient is positive ( > 0) than the distribution is right skewed and if the value of skewness coefficient is negative (< 0) than the distribution is left skewed. For normal distribution, the value of skewness coefficient is close to zero. The value of skewness coefficient for the age of respondents is 0.26, which is close to zero. This suggests that the distribution of age of the respondents is approximately normal. Kurtosis measures the degree of peakness of a distribution. In other words, Kurtosis refers to the relative length of the tails and the degree of concentration in the center. If a distribution has a relatively high peak, it is called ‘leptokurtic’ (Hawkins & Weber, 1980:38). According to Doane & Seward (2009), a normal bell-shaped population is called mesokurtic (kurtosis = 0) and serves as a benchmark. Further, a population that is flatter than a normal (i.e., has heavier tails) is called platykurtic (kurtosis < 0) while one that is more sharply peaked than a normal (i.e., has thinner tails) is leptokurtic (kurtosis > 0). The value of kurtosis coefficient for the age of the respondents is -1.00 that suggests heavier tails. Figure 1 shows the histogram of age of the respondents. The histogram suggests that the distribution of age of the respondents is approximately normal. Figure 1: Distribution (Histogram) of age of the respondent 1.2) Sex and Marital Status of Respondents Table 2 shows the cross-tabulation of martial status and gender of the respondent. Table 2 Crosstabulation of Marital Status and Sex of respondent Sex of respondent Male N (%) Female N (%) Total N (%) Marital Status Single 169 (12.1) 143 (10.2) 312 (22.3) Married 416 (29.7) 436 (31.1) 852 (60.8) Separated 8 (0.6) 12 (0.9) 20 (1.4) Divorced 23 (1.6) 43 (3.1) 66 (4.7) Widowed 35 (2.5) 116 (8.3) 151(10.8) Total 651 (46.5) 750 (53.5) 1401 (100.0) Majority (53.5%) of the respondents was female. Further, majority (60.8%) of the respondents were married. Figure 2 shows the cluster bar chart for marital status and sex of respondent. Except widowed, the other distribution for martial status for male and female are approximately same. Figure 2: Cluster bar chart for marital status and sex of respondent 1.3) Working status of respondents Figure 3 shows the pie chart for working status (last week) of the respondents. About half (48%) of the respondents were working more than 10 hours in a week. About 39% of the respondents were working more than 30 hours in a week. The percentage of unemployed, sick/disabled, retired and housewife was 9%, 3%, 17% and 19%, respectively. Figure 3: Pie chart for working status (last week) of respondents 1.4) Age on leaving full time education Figure 4 shows the bar graph for age on leaving full time education of respondent. Figure 4: Bar chart for age on leaving full time education of respondent The figure suggests a poor picture of the educational background of the respondents. More than three-fourth (83%) of the respondents had left full time education at the age of 16 years. Only a small percentage (6%) of the respondents continued in education more than 20 years. Further, about 2% of the respondents were still engage in studies. Thus, it can be said that the educational level of the respondents were very low. 1.5) Stay duration at present address One cannot stay at the same place because of many reasons. Figure 5 shows the pie chart for stay duration at present address. Figure 5: Pie chart for stay duration at present address About half (49%) of the respondents were living at their present address for more than a decade. About 18% of the respondents were living at their present address from 5 to 10 years. Only about 8% of the respondents stay duration at present address was less than 12 months. 1.6) Type of House (Structurally) Figure 6 shows the bar chart for type of house of respondents. About 39% of the respondents were living in semi-detached type of house. About 29% of the respondents were living in mid-terrace type of house. Figure 6: Bar chart for type of house of respondents Question – 2 Investigate whether any of your variables from part 1 are associated with the variable personal victimization (‘incpers’) and, if so, how? Make personal victimization a dependent variable. This part of the paper will analyze the association between respondent’s age and personal victimization (if any) using the inferential statistical technique of correlation and regression and Chi-square test of Independence (association). Personal Victimization Table 3 shows the summary statistics for the number of personal victimizations. Table 3 Summary Statistics for the number of personal victimizations Number of personal victimizations N Valid 1405 Missing 1 Mean .34 Median .00 Mode 0 Std. Deviation 2.748 Variance 7.549 Skewness 16.023 Kurtosis 313.471 Range 67 Minimum 0 Maximum 67 Figure 7: Distribution (Histogram) of number of personal victimizations The average number of personal victimizations for respondents was about 0.34 (SD = 2.75). About half of the respondents had 0 number of personal victimization. The range of the personal victimization was 67 with minimum and maximum being 0 and 67, respectively. The distribution of number of personal victimizations for respondents is heavily positively skewed (Skew = 16.02). This is also confirmed by the histogram of the number of personal victimizations for respondents (figure 7). Correlation and Regression Analysis Regression analysis is used to develop a predictive model to predict values of dependent variable based on one or more independent variables. A simple linear regression involves drawing a straight line through the data when they are presented as a scatter plot (Wright, 1998). Figure 8 shows the scatterplot of respondent’s age and number of personal victimization. The scatterplot suggest very weak direct linear relationship between respondent’s age and number of personal victimization. Figure 8: Scatterplot of number of personal victimization vs. age of respondents The strength of the relationship is measured using correlation coefficient, which is a numerical measure or index of the amount of association between two sets of scores. The range of correlation coefficient is between -1 to +1. According to Howitt & Cramer (2003), the ‘+’ sign indicates a positive correlation, i.e. the scores on one variable increase as the scores on the other variable increase and a ‘-’ sign indicates a negative correlation, i.e. as the scores on one variable increase, the scores on the other variable decreases. A correlation coefficient value of |1| indicates a perfect association between the two variables and a value of 0 indicates no association between the two variables. A correlation of |0.5| would indicate a moderate (negative or positive) relationship between the two variables (Howitt & Cramer, 2003). Although a scatterplot revealed very weak positive relationship between respondent’s age and number of personal victimization. Let us check whether this relationship is statistically significant or not. The null and alternate hypotheses are (There is no significant relationship between age and personal victimization.) (There is a significant relationship between age and personal victimization.) The selected level of significance, α is .05 and the selected test is test for Zero Correlation. Table 4 shows the correlation matrix. Table 4 Correlations Matrix Number of personal victimizations Population of area Population of area -.023 Age of respondent -.106** -.016 * p < .05, **P Read More

Interpreting the standard deviation along with the mean tells us something about how much spread there is in the scores, and important properties of the distribution relate to how far away from the mean we move, in terms of standard deviation (see Punch, 2000). The larger the standard deviation, the more spread out is the distribution (Wright, 1998). The standard deviation for the age of the respondents was about 18.8 years. This indicates that the age of respondents was more dispersed. Skewness is defined as ‘an index of the asymmetry or lop-sidedness of the distribution of scores on a variable (Howitt & Cramer, 2003:47-8).

If the value of skewness coefficient is positive ( > 0) than the distribution is right skewed and if the value of skewness coefficient is negative (< 0) than the distribution is left skewed. For normal distribution, the value of skewness coefficient is close to zero. The value of skewness coefficient for the age of respondents is 0.26, which is close to zero. This suggests that the distribution of age of the respondents is approximately normal. Kurtosis measures the degree of peakness of a distribution.

In other words, Kurtosis refers to the relative length of the tails and the degree of concentration in the center. If a distribution has a relatively high peak, it is called ‘leptokurtic’ (Hawkins & Weber, 1980:38). According to Doane & Seward (2009), a normal bell-shaped population is called mesokurtic (kurtosis = 0) and serves as a benchmark. Further, a population that is flatter than a normal (i.e., has heavier tails) is called platykurtic (kurtosis < 0) while one that is more sharply peaked than a normal (i.e., has thinner tails) is leptokurtic (kurtosis > 0).

The value of kurtosis coefficient for the age of the respondents is -1.00 that suggests heavier tails. Figure 1 shows the histogram of age of the respondents. The histogram suggests that the distribution of age of the respondents is approximately normal. Figure 1: Distribution (Histogram) of age of the respondent 1.2) Sex and Marital Status of Respondents Table 2 shows the cross-tabulation of martial status and gender of the respondent. Table 2 Crosstabulation of Marital Status and Sex of respondent Sex of respondent Male N (%) Female N (%) Total N (%) Marital Status Single 169 (12.1) 143 (10.2) 312 (22.3) Married 416 (29.7) 436 (31.1) 852 (60.8) Separated 8 (0.6) 12 (0.9) 20 (1.4) Divorced 23 (1.6) 43 (3.1) 66 (4.7) Widowed 35 (2.5) 116 (8.3) 151(10.8) Total 651 (46.5) 750 (53.5) 1401 (100.0) Majority (53.5%) of the respondents was female.

Further, majority (60.8%) of the respondents were married. Figure 2 shows the cluster bar chart for marital status and sex of respondent. Except widowed, the other distribution for martial status for male and female are approximately same. Figure 2: Cluster bar chart for marital status and sex of respondent 1.3) Working status of respondents Figure 3 shows the pie chart for working status (last week) of the respondents. About half (48%) of the respondents were working more than 10 hours in a week.

About 39% of the respondents were working more than 30 hours in a week. The percentage of unemployed, sick/disabled, retired and housewife was 9%, 3%, 17% and 19%, respectively. Figure 3: Pie chart for working status (last week) of respondents 1.4) Age on leaving full time education Figure 4 shows the bar graph for age on leaving full time education of respondent. Figure 4: Bar chart for age on leaving full time education of respondent The figure suggests a poor picture of the educational background of the respondents.

More than three-fourth (83%) of the respondents had left full time education at the age of 16 years. Only a small percentage (6%) of the respondents continued in education more than 20 years. Further, about 2% of the respondents were still engage in studies. Thus, it can be said that the educational level of the respondents were very low. 1.5) Stay duration at present address One cannot stay at the same place because of many reasons.

Read More

CHECK THESE SAMPLES OF Quantitative Data Analysis and Computing With SPSS

Integrating Software Applications in Cloud-Enabled Enterprises

Facilitators The group will also be facilitators of the study, will facilitate data collection processes, and technical aspects of data analysis and documentation of the study's findings.... The section also discusses validity and reliability of the selected data collection instruments, data collection procedure, proposed data analysis approach and pilot study for the research.... To develop an integrated computing system that can co-exist with an Enterprise network....
4 Pages (1000 words) Research Proposal

Qualitative Research Proposal

Data will be analyzed by the researcher's rationale before coding and application of spss software for descriptive statistics and comparisons.... Questionnaires and interviews will be used in collecting data The employees will be informed of the scope of the research and their consent sought before sampling 137 participants through stratified random sampling....
15 Pages (3750 words) Essay

Up Move European Marketing Strategy

Quantitative research design is concerned with assigning numerical values to research phenomena to form data that can be analyzed to determine how the phenomena under study affect each other.... Research Design According to Teddlie and Tashakkori (2009), the major types of research designs are qualitative and quantitative research designs.... quantitative design is interested in finding the level of correlation between variables in the research....
13 Pages (3250 words) Research Proposal

Analyzing a research

he data analysis techniques used in the articles includes qualitative, quantitative and mixed methods.... In this case, the questioners' data regarding the teachers is fed into the spss program in order to generate the teachers' population descriptive information.... Alison Kington, Christopher Day, Pam Sammon and Elaine Regan are the authors of the journal article, which is under analysis.... In addition, a coding application was developed to establish the themes of organizational for analysis....
4 Pages (1000 words) Essay

SPSS Output Summary

The data was subjected for analysis on spss statistical package. ... The data was subjected for analysis on spss statistical package.... IBM spss Advanced Statistics 20.... The follow-up, post hoc analysis were done at 95% CI or α = .... The survey lasted for an year and the data was recorded on completing a calendar month.... A judgment method was to collect the data. ... The survey lasted for an year and the data was recorded on completing a calendar month....
2 Pages (500 words) Essay

Understanding of Human Behavior and the Associated Reasons that Cause this Behavior

An analysis of the external and internal environment of the competing universities is essential so that there is an overall understanding of the decisions of the students.... Numerical data is essential so that the results may be represented in mathematical form and thus the subjects are asked to answer questions in numerical form so that such computations may be made.... Sampling, as defined by Saunders et al (2009) is the collection of data from a smaller group size rather than collecting the data from the entire population....
12 Pages (3000 words) Research Paper

Synthesis of Systematic Analysis Results

Though the research draws on the framework proposed to provide analysis and raise further ... nterviewing, data collection and analysis using Statistical Programme for Social Science (spss).... egression and factor analysis using spss ... egression and factor analysis using spss ... egression and factor analysis Using spss ... nterviewing, data collection and analysis using Statistical Programme for Social Science (spss) ...
16 Pages (4000 words) Assignment

Transport Data Analysis: Quantitative Analysis

"Transport data analysis: Quantitative Analysis" paper aimed at making inferences concerning data collection, its reliability, and performing statistical analysis when it comes to qualitative data.... Having 100 vehicles per hour passing the station, 20% of the total population is good for doing the analysis as it will give both model types and hours are evenly presented in the data.... The questions and hypothesis may be great, but based on the sample one is able to make inferences concerning the differences you found from the analysis (Masters et al....
7 Pages (1750 words) Assignment
sponsored ads
We use cookies to create the best experience for you. Keep on browsing if you are OK with that, or find out how to manage cookies.
Contact Us