The prevention of ID Theft against women while shopping - Statistics Project Example

The prevention of ID Theft against women while shopping Affiliation Characteristics Total Identity Theft Existing Account New Account (%)Personal Information (%)Multiple Types (%)Female1513435Male1412436Age: 16-24 years old 554116162225-34 years old2925871335-49 years old191563850-64 years old191653665 years old and Above2016537Source: Langton & Michael Planty, (2010)From the data presented above, it is clearly depicted that female in shopping malls suffer more from identity theft cases than men do.

Further from the same statistics, most of the affected individuals fall in the age of between 16 and 24 year. In fact, 55% of the affected people are within this age group, with women being the majority. This age group is then followed closely by people of the age of 25-34 years of age, who account for 29 per cent. It seems that due to increase in one’s age, the ability to fall victim of identity theft decrease. People aged between 35 and 49 years and 50-64 account for only 19 per cent each.

The implication here could be that these people are mature enough to determine possible threats and they can therefore avoid cases of identity theft. The aged people fall victim even more frequently due to their inability to take care of themselves. Like women, they would prefer seeking assistance regarding the tasks that could lead to identity theft. This data is further analysed using SPSS as follows: One-Sample StatisticsNMeanStd. DeviationStd. Error MeanTotal Identity Theft724.428614.327805.

41540Existing Account719.714310.291003.88963New Account76.85714.259441.60992Personal Information75.42864.894121.84980Multiple Types79.57146.078852.29759From the SPSS output on one-sample statistics, the mean value for all victims of identity theft irrespective of the specific characteristic is 24.429 with a standard deviation of 14.327. One-Sample TestTest Value = 0tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the DifferenceLowerUpperTotal Identity Theft4.5116.00424.4285711.177637.

6796Existing Account5.0686.00219.7142910.196729.2319New Account4.2596.0056.857142.917810.7965Personal Information2.9356.0265.42857.90239.9549Multiple Types4.1666.0069.571433.949415.1934The table above is an SPSS output of the data depicting the test of significant using one-tailed t test. From the analysis, all variables are significant in explaining the model. Besides, from the ANOVA statistics are variables are also found to be significant in explaining the model. The ANOVA statistics is as shown below: ANOVASum of SquaresdfMean SquareFSig.

ExistingAccountBetween Groups634.9295126.986253.971.048Within Groups.5001.500Total635.4296NewAccountBetween Groups108.357521.67143.343.115Within Groups.5001.500Total108.8576PersonalInformationBetween Groups143.714528.743..Within Groups.0001.000Total143.7146MultipleTypesBetween Groups219.714543.94321.971.161Within Groups2.00012.000Total221.7146ReferencesLangton, L., & Michael Planty. (2010,). National Crime Victimization Survey Supplement: Victims of Identity Theft, 2008. Bureau of Justice Statistics: Special Report, NCJ 231680, 1-20.