Research Proposal on "Criminal Justice and Criminology Interpreting Simple Data"

Research Proposal 7 pages (2030 words) Sources: 6

[EXCERPT] . . . .

Criminal Justice and Criminology

Interpreting simple data

The data collection exercise involved posting a picture of a bear on Facebook. The caption of the picture asked viewers to provide their thoughts on the picture. This caption was kept simple in order to prevent bias that could arise from asking viewers to like or dislike the pictures. The data that could be collected included likes, positive and negative comments. The data was collected over a 24-hour period.

The data collected can be summarized as below.

Number of respondents

Likes

Positive comments

Neutral comments

Negative comments

Gender

Total

Likes

Positive comments

Neutral comments

Negative comments

Males

Females

Level of education

Total

Likes

Positive comments

Neutral comments

Negative comments

High School

Bachelor's

From the simple data collection exercise, it is possible to determine the number or proportion of people who like the picture or post, the proportion of negative and positive reactions, the gender distribution of the responses, and the amount of time between posts. It is also possible to show the relationship between education level and the proportion of each response.



A bivariate analysis is used in the descriptive statistics. This involves using two variables to describe the data. Descriptive statistics will be able to describe the data appropriately. The data is ordinal and thus the mode is the best measure of central tendency to use.

What can be determined from the data collected?

In this post we wanted to know if the public liked bears so we chose the bivariate analysis to distinguish if two (2) variables were involved would be used in the identification of the male to female gender for the collection of the source data. We were also interesting in knowing the reactions of individuals to the picture. This is determined by collecting information on the proportion of people who like and who dislike the post.

What would be the appropriate measures of central tendency?

In this collection of data were interested in relationships among the variables. The appropriate measures of central tendency for this type of data collection are mean and mode. The mean will help to know different statistics such as the average number of males and females who posted and the average time between posts. The mode will help to know whether males or females posted more or whether the number of positive reactions exceeds negative reactions or vice versa. Additionally, we used a subgroup, to determine if a univariate analysis was appropriate so that we could find comparisons that focused on people's description and/or if there were different units for this analysis for study.

What types of variables exist?

The variables that exist in this type of data are independent variables. This is because they do not vary as other variables vary. For example the number of males or females does not determine whether their response is negative or positive. The gender variables are discrete variables since one can only be male or female. Time is a continuous variable though with some limits. In this example we know that bivariate analysis will focus more on the variables of each gender for themselves as noted below.

Table 1: Bear "Likes" (10 Females), (8 Males) Respondents, June 14, 2013

Gender

Response

Female

Male

Do you like the Bear?

Yes

22.2%

38.9%

Neutral

No

27.8%

Total

55.5%

44.5%

Using the data collected, determine what can be derived using descriptive and inferential statistics.

In this case we chose to use a percent aging table. In constructing and presenting this Table we used a convention called "percentage down." This means that the percentages are calculated downward through each column to a 100% total. We read this form of table across a row. In the column labeled "Yes," what percentage of the males liked or posted positive comments on the bear? What percentage of the females like or posted positive comments on the bear? The percentage down is a conventional practice that most researchers prefer to calculate by using the percentage across method.

We also decided to study the example of a univariate analysis to examine the distribution of ages on only one variable at a time. The logical formats were designed to simply study analysis of univariate data. The univariate data used was basic to report on individual case where the attribute for each yes or no was listed to reflect the terms of the variable in question for example: 1) age to 2) gender. This was derived because I was interested in the ages of women and men responding to the post of the "bear like yes" or "bear no like"; other data could also be collected from a World Wildlife society, but we did not have time to post to the website to collect the datasets.

Age

Percent

Under 40

8%

40-50

10%

50-60

72%

60-65

10%

No response

0%

Total

In this case we also concluded that in addition to the inferential model could use statistics for computation in the suggestion of inferences a sample observation from a population that was larger. Inferential statistics in this case help us research and form our own conclusions direct from observations; and this fact finding involved conclusions about a certain age of population where the previous study drew samples from. In addition before numbers were challenged, the level of statistics used was non-toxic and safe for humans because the collection was made on social media.

Using descriptive statistics, it is possible to determine the number of each gender group who gave positive and negative responses as well as the average time between responses. Using inferential statistics, it is possible to determine whether males or females were more likely to give negative or positive responses. However when making these inferences, one would need to make assumptions that all males and all females react in the same way. It is also possible to determine whether females or males were more likely to post during a certain time of the day using descriptive statistics. The trend of postings by time can also be determined using descriptive statistics. Using this descriptive statistics, it is also possible to make inferences. One inference that can be made is on what time a specific gender is most probably to post and when more responses are expected. These inferences are also subject to the assumption that the population as a whole is likely to behave in the same way.

Conclusions that can be made

Our conclusions will include measures of central tendency. Therefore, the most frequent attribute will be either grouped or un-grouped and the arithmetic mean which will show the sum of values for all observations will be divided by the number of observations and finally the median which means the middle attribute will be ranked as the distribution of observed attributes.

The tables below show how the mode, arithmetic mean and median of grouped data are calculated. The mode is calculated by looking at the frequencies and finding the most frequent group which is this case is those aged 41 years. The arithmetic mean is calculated by multiplying the age by the frequency then adding them all up and dividing it by the total number of respondents which is 18. Since the total number of respondents considered is 18, the median or middle value is that represented by the position of the 9th value (18/2). So in this case, the median is in the 41-year-old group so this is the median value. (Maxfield, & Babbie, 2011).

Age

Number

People

Mode

Under 40

41

9

Mode = 9 (Most Frequent)

42

2

43

2

44

5

Total = 18 people

Age

Number

People

Mode

Formula

Arithmetic Mean

Under 40

41

9

Mode=9

Most Frequent

41x9=369

42

2

42X2=84

43

2

43X2=86

44

5

44X5=220

Total = 18

=369+84+86+220

Total 759 divided by 18 people

42.17

Age

Number

Median = 9 'Midpoint'

Under 40

41

9

1-9

( | V

42

2

9-11

9

43

2

11-13

44

5

13-18

9.05

Total = 18 people

The negativity score of the bear photo can be deduced by using inferential statistics. By taking a backhanded positive and negative scoring system to classify the positive and negative comments, the negativity score can be found. In this case, the type by sex or education or age mean score which is the correlation of sex to the average 'negativity score' can be calculated.

From the data collection exercise, it can be inferred or deduced that women use Facebook more than men or they are more likely to comment on posts more than men. This is attributed to the number of women who responded being more than that of men. However, this may not have reached statistical significance since the sample size was small and not powered enough. The distribution of level of education also shows that there are more people who have reached college and graduate level of education using Facebook than those in high school. This reaches statistical significance though the sample size is small. The possibility of this… READ MORE

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