A sociology study of male self image
Abiola Olanipekun A-level sociology 28/11/05 AS sociology coursework Section A: Title: A sociology study of male self image Author: Matthew Laza Date of publication: 1993 Publisher of source: Collins educational student Awards in sociology Section B: The research chose to examine whether the male self image has been changed by feminism. The stated aims of this research were to: * Investigate attitudes to masculinity from a representative sample of males in the local population * Compare these views with those of a group of his peers and fellow sixth formers at christleton high school * Examine how his findings can be explained, evaluated and understood in the light of other sociological studies Other aims the author wanted to explore were: * How far the practical demands of feminism in terms of the fair division of domestic labour between the sexes has affected the everyday lives of a group of ordinary men * How far any concept of the 'new man' has entered the consciousness of actual men. His primary method was 40 structured questionnaires therefore the method used collected quantitative data and adopted a positivist approach. The research may have decided to adopt a positivist method to conduct his study because he wanted to compare the views of his peers those of a mixed group drawn from the adult's population of Chester.
An Investigation Into An Aspect Of Human Variation.
An Investigation Into An Aspect Of Human Variation Aim The aim of this investigation was to explore and analyse an aspect of human variation. Hypothesis There will be a positive correlation between an individual's foot length and their hand span. This may be due to linkage; the genes that create these characteristics may be positioned on the same chromosome so that they become linked, and are inherited together. This linkage causes the two characteristics to become associated so that a person with large feet will also have large hands. Variables In order to create a fair investigation, it was necessary to consider the variables that affect an individual's foot length or hand span. Any variables that were likely to disrupt the results were controlled and those measured were recorded accurately so that any correlation could be observed and evaluated successfully. i) Age - The majority of individuals stop growing by the age of eighteen. Below this age, people grow at different rates and for this reason, cannot be compared accurately. In this investigation, the sample included only, people above the age of seventeen. Above this age, most people will have attained their maximum growth and therefore their maximum foot length and hand span. ii) Gender - The average male is ten centimetres taller than the average female. It follows that this difference will also be translated
I believe that boys in year 10 are better at estimating time than girls are.
Maths Statistical Coursework For this piece of coursework I am going to analyse some data from a guesstimate survey that was carried out at school. It was asked to students in years 8 and 10. The survey was organised into several categories. These were: * Girl And Boy * Year 8 and Year 10 Each student was given the questionnaire, which tested their accuracy in guessing lengths, weights and times. I myself did not collect the data from the students so it is classed as secondary data. I shall go through all of the results to check for bias results so that I can get the most accurate result. If I end up with bias results I could end up with inaccurate results. Before observing the data I will make 2 clear hypothesis's. Hypothesis 1 I believe that boys in year 10 are better at estimating time than girls are. To start off I shall start of by using the 60 seconds question to attempt to prove or disprove it. My hypotenuse might expand the more I get into my results. Proving To prove this I shall take a sample of the data taken in by this questionnaire. Form this I shall use graphs including histograms, box and whisker diagrams and other means of receiving results that I can analyse to either prove or disprove my hypotenuse. Sampling Due to the massive amount of data collected I shall have to decide on a type of sample to take to get a suitable amount of data. I have
Testing Materials Coursework
Testing Materials Coursework Tommy Patton Plan In this investigation we are going to measure the tensile strength of four different materials, these are: * Elastic * Wool * String * Plastic fishing wire We will measure the tensile strength in grams and kilograms. To make sure that the comparisons we make are fair we are going to repeat the investigation 3 times for each sample of material, use the same apparatus throughout the investigation, use the same weights for each sample and use the same length of material for each sample. For this investigation we are going to use a clamp stand, a hook, a cotton reel, a clamp and weights. We are going to put the clamp stand on the bench and place the cotton reel, with the sample tied around it, in the clamp. We will then increase the load on the material until it breaks and finally we will measure the load needed. We will make sure that our work is safe by wearing safety goggles throughout the investigation and keeping the weights away from the bench so that when the sample material snaps the weights won't fall on the bench, we will also keep well away from the weights so that they won't fall on our feet. Results The table below shows our results. Sample Material Broke Didn't Break Weight Needed (Kg) Wool test 1 x 2.5 test 2 x 2.35 test 3 x 2.5 String test 1 x 3+ test 2 x 3+ test 3 x 3+
Statistics. The purpose of this coursework is to investigate the comparative relationships between the depreciation of a cars price, in relation to the factors that affect it.
Statistics: Analysis of used cars database Introduction: The purpose of this coursework is to investigate the comparative relationships between the depreciation of a car's price, in relation to the factors that affect it. The factors that I wish to investigate are the age/mileage of a car, being the easiest to compare to depreciation. To do this, I shall use random sampling. I shall give a number of hypotheses, claiming whether each influential factor has an adequate effect on depreciation. I shall attempt to validate this using data given to me on Excel. I have done this in terms of percentage depreciation to make sure that I have relevant data to compare depreciation over each car in my sample. Here are the hypotheses and questions: << Hypothesis 1 >> The older the car, the greater the percentage depreciation of the price - I believe this because as a car travels further, essential parts may perhaps wear down, and stop the car from working to its optimum standard. After a certain level of mileage, the car's fuel costs may begin to increase, as its decreased efficiency uses up more fuel per mile. These following data values are necessary to calculate the depreciation of a value of a car (as a rule), when there is more or less mileage: * Sale price (no miles attached) * Mileage Mileage will affect the percentage depreciation of the original car's price, so there
Investigating the relationship between the number of lessons taken prior to the test, and the number of mistakes made in the driving test itself.
Introduction I will be investigating the relationship between the number of lessons taken prior to the test, and the number of mistakes made in the driving test itself. I will then use this information in order to determine which instructor is the best, depending on the number of lessons needed for the person to obtain the least mistakes in order to pass. But the other factors affect these results, such as the following * Time of day the test was taken - times such as the rush hour in the morning or evening could affect the number of mistakes * Gender of the driver - one sex maybe better at driving than the other, because they can concentrate better etc. * Day of the week - on Fridays there maybe a lot of traffic because people want to get home, or Monday mornings there might be a lot more drivers traveling to work than any other morning for example. * Weather - this could affect the test profoundly, but there was no data on this factor so I will not be analyzing this factor. I will be investigating these factors appropriately with different sampling techniques. I have come to the conclusion that I will not include the results of the tests with missing data. I am doing this because it doesn't provide me with any information and could have a negative effect on my final results. Hypothesis I predict that the pupil with the most lessons will make lees minor mistakes in
Hypothesis - The older the students are the more accurate they will be in estimating the size of the angle.
Guestimate Course work Hypothesis The older the students are the more accurate they will be in estimating the size of the angle. Plan I will take 20 random samples from the 3rd year and 20 random samples from the 1st year out of a total of 292 individual sets of data. I will sort this data out and plot a cumulative frequency graph for each year. I will take random samples by using the ran# button on my calculator. When is use the calculator will give a random number from 0-999. I will use the number after the decimal point. E.g. for 0.088 I would use number 88 on my list of data. I will use this table: Random number on calculator Subtract by... -292 0 301-592 300 601-892 600 e.g. if the number on the calculator came out to 345. I would subtract 300 from 345. Which would give me an answer of 45, and I can get that from my data. Whereas I couldn't have with the number 345 because I only have 292 individual data. People can give false information because they might be messing around. So this information will not be accurate and I will ignore this data. I would identify this by looking at the guesses that they take and the ones that are odd and widely different to all the other guesses students have taken. Prediction I predict that the 3rd years will be more accurate than the 1st yrs, in estimating the size of the angle. The actual size of the angle is 160
Application of number: level 3 - Is House Buying a Good Idea or Not?
Application of number: level 3 IS House Buying - a Good Idea or Not? A table to show the data collated for the samples for other houses and first-time buyers' houses (in ascending order): Other houses Price (£) First-time buyers' houses Price (£) 32,500 8,500 2 35,000 2 29,950 3 37,950 3 37,500 4 39,950 4 39,500 5 44,950 5 39,950 6 46,250 6 39,950 7 54,950 7 39,950 8 64,950 8 41,950 9 64,950 9 43,950 0 65,000 0 44,950 1 71,950 1 46,500 2 74,950 2 46,950 3 82,500 3 49,950 4 87,500 4 50,950 5 89,950 5 51,950 6 94,950 6 51,950 7 10,000 7 52,500 8 10,000 8 52,995 9 31,950 9 53,000 20 39,500 20 54,950 21 45,000 21 54,950 22 45,000 22 55,950 23 49,950 23 57,950 24 75,000 24 69,500 25 75,000 25 69,950 26 95,000 26 70,950 27 95,000 27 72,950 28 210,000 28 79,500 29 215,000 29 79,950 30 249,950 30 84,950 The mean of a set of data is the sum of the values divided by the number of values. Mean = sum of values / number of values The range is a measure of spread and the range of a set of data is the difference: Greatest value - least value First-time buyers' houses: * Mean: Sum of house prices / 30 = £1,584,445 / 30 = £52,815 (to the nearest £) * Range: highest house price - lowest house price = £84,950 - £18,500 = £66,450 (to the nearest £)
Factors influencing girls athletic performance throughout secondary school.
Introduction Athletics data has been collected for a number of years at Colchester County High School. Colchester County High School is a selective school for girls in the Colchester district. This means that it is not representative of the whole population. Upon entry to the school, forms are chosen on the basis of musical, sporting and academic talent from previous years in primary school. This means, that in theory, all the forms that are the outcome of one selective test should be equal in sporting ability. However, this is not to say that they would be equal in athletic activity, as in primary school, most pupils play sports such as netball, hockey, tennis and rounders. Even primary schools that do some athletics do more common things like the 100m run, and long jump. Most primary schools do not teach the athletic events such as 1500m or discus. Girls that are good at sport are not necessarily good at athletics, and vice versa. Also, girls whose schools do teach athletics are clearly priveliged. This data is available to the pupils through the maths and sports departments. The data includes times for running various distances and distances for long jump high jump and triple jump. The data also includes distances that the girls can throw the rounders ball, discus and shot. This data is to be treated as though it were primary data, as it is from a reliable
GCSE Maths Coursework: proportions of different parts of the body and thier relationship to height
BOYS: Rnd x Total no. of samples Sample no. Sample no. (rounded to nearest whole number) THUMB WRIST NECK WAIST 0.503*60 30.18 30 7 5 31.5 72 0.571*60 34.26 34 6 6.5 33 79 0.138*60 8.28 8 6 5 32 79 0.176*60 0.56 1 6.5 7.5 35 79 0.055*60 3.3 3 6.5 5 33 85 0.282*60 6.92 7 7 6 36 75 0.332*60 9.92 20 9 6.5 32 65 0.526*60 31.56 32 7 7.4 35 77 0.633*60 37.98 38 6 7 33 70 0.692*60 41.52 42 6 7 29 74 0.917*60 55.02 55 7 7.3 37 88.2 0.110*60 6.6 7 7 8 36 85 0.549*60 32.94 33 7 7.2 34 76 0.642*60 38.52 39 8 4.5 34.5 73 0.767*60 46.02 46 7 7 40.5 75.5 0.781*60 46.86 47 6.5 6.7 34 73 0.965*60 57.9 58 7.5 5.5 34 69 0.206*60 2.36 2 7 5 33 74 0.758*60 45.48 45 5.5 4 31 72 0.435*60 26.1 26 7 7 36.5 85.5 0.021*60 .26 6.5 6 34 75 0.502*60 30.12 30 7 5 31.5 72 0.558*60 33.48 33 7 7.2 34 76 0.846*60 50.76 51 7.2 8.5 36 94.8 0.472*60 28.32 28 5.5 6 34 76 0.874*60 52.44 52 7.3 7 33.3 74 0.476*60 28.56 29 6.5 8 36 80 0.647*60 38.82 39 8 4.5 34.5 73 0.898*60 53.88 54 6.5 7 31 72.5 0.666*60 39.96 40 7 5.5 35 90 These are the 50% (30 samples out of 60) samples of boys data used in this investigation. GIRLS: Rnd x Total no. of samples Sample no. Sample no. (rounded to nearest whole