Are Kids Too Busy? Early Adolescents’ Perceptions of Discretionary Activities, Overscheduling, and Stress.

**Create** a 7- to 10-slide presentation
with speaker notes examining the differences between descriptive and
inferential statistics used in the journal article you were assigned.
Presentation should be at least 20 minutes and presented in class.

**Address** the following items as they
apply to the article:

- Describe the functions of statistics.
- Define descriptive and inferential statistics.
- Provide at least one example of the relationship between descriptive and
inferential statistics .

**Format** your presentation consistent
with APA guidelines.

Abstract:

BACKGROUND: The activity patterns of children, especially after-school patterns, are receiving more professional attention. However, evidence regarding the value of various activities in children’s lives is contradictory. The purpose of this study was to assess perceptions of discretionary activities, overscheduling, and levels of stress from adolescents’ perspective.

METHODS: A sample of 882 children, ages 9 to 13, recruited at 9 health education centers in the United States was selected for this study. Children answered questionnaires using remote, handheld devices. Data were analyzed using descriptive statistics and multivariate logistic regression. The outcomes of interest were activity-based stress and desire for more free time.

RESULTS: The primary predictor for the desire for more free time was hours of screen time (television, computer, video games): those who reported 3 or more hours were nearly 3 times more likely to desire more free time. Further, children who chose their own activities experienced more activity-related stress than those who shared decisions with parents. The single greatest predictor of activity-related stress was the reported number of hours spent on homework. Students who averaged at least 2 hours on homework per night were nearly twice as likely to report frequent activity-related stress. CONCLUSION: Parents of school-aged children should assess activity-related stress and the degree to which children perceive they are busy. Teachers, school counselors, and school administrators should be aware of these perceptions as they are making decisions regarding school schedules and should teach personal skills such as time management and stress control.

Hello

Thanks for the quick work

a couple of things Should be bullet formatted not paragraphs

the footnotes are brief but the pp is not

my instructor just gave me the below items

please consider providing descriptions/explanations of the following inferential statistics, such that what are they use for, what are they used to test – Chi square test and multivariate logistic regression. These are the major inferential statistics used in this are kids to busy study article. How are these tests related to meet the study article objective/purpose. Can discuss descriptive statistics in relation to the sample (results from demographics – gender and age for kids busyness levels) Check in with you other team members to be sure they discuss what is the sample for the study, what are the independent and dependent variables of the study and again how these relate to the descriptive and selected inferential statistics used in the study article.

Functions of stats

**(1) **Statistics helps in providing a better
understanding and exact description of a phenomenon of nature.

**(2) **Statistical helps in proper and efficient
planning of a statistical inquiry in any field of study.

**(3) **Statistical helps in collecting an appropriate
quantitative data.

**(4) **Statistics helps in presenting complex data in a
suitable tabular, diagrammatic and graphic form for an easy and clear
comprehension of the data.

**(5) **Statistics helps in understanding the nature and
pattern of variability of a phenomenon through quantitative obersevations.

**(6) **Statistics helps in drawing valid inference,
along with a measure of their reliability about the population parameters from
the sample data.

**7 most essential functions of statistics **

Statistics as a discipline is considered indispensable in almost all spheres of human knowledge. There is hardly any branch of study which does not use statistics. Scientific, social and economic studies use statistics in one form or another. These disciplines make-use of observations, facts and figures, enquiries and experiments etc. using statistics and statistical methods. Statistics studies almost all aspects in an enquiry. It mainly aims at simplifying the complexity of information collected in an enquiry. It presents data in a simplified form as to make them intelligible. It analyses data and facilitates drawal of conclusions. Now let us briefly discuss some of the important functions of statistics.

**1. Presents facts in. simple form: **

Statistics presents facts and figures in a definite form. That makes the statement logical and convincing than mere description. It condenses the whole mass of figures into a single figure. This makes the problem intelligible.

**2. Reduces the Complexity of data: **

Statistics simplifies the complexity of data. The raw data are unintelligible. We make them simple and intelligible by using different statistical measures. Some such commonly used measures are graphs, averages, dispersions, skewness, kurtosis, correlation and regression etc. These measures help in interpretation and drawing inferences. Therefore, statistics enables to enlarge the horizon of one’s knowledge.

**3. Facilitates comparison: **

Comparison between different sets of observation is an important function of statistics. Comparison is necessary to draw conclusions as Professor Boddington rightly points out.” the object of statistics is to enable comparison between past and present results to ascertain the reasons for changes, which have taken place and the effect of such changes in future. So to determine the efficiency of any measure comparison is necessary. Statistical devices like averages, ratios, coefficients etc. are used for the purpose of comparison.

**4. Testing hypothesis: **

Formulating and testing of hypothesis is an important function of statistics. This helps in developing new theories. So statistics examines the truth and helps in innovating new ideas.

**5. Formulation of Policies : **

Statistics helps in formulating plans and policies in different fields. Statistical analysis of data forms the beginning of policy formulations. Hence, statistics is essential for planners, economists, scientists and administrators to prepare different plans and programmes.

**6. Forecasting : **

The future is uncertain. Statistics helps in forecasting the trend and tendencies. Statistical techniques are used for predicting the future values of a variable. For example a producer forecasts his future production on the basis of the present demand conditions and his past experiences. Similarly, the planners can forecast the future population etc. considering the present population trends.

**7. Derives valid inferences : **

Statistical methods mainly aim at deriving inferences from an enquiry. Statistical techniques are often used by scholars planners and scientists to evaluate different projects. These techniques are also used to draw inferences regarding population parameters on the basis of sample information.

With **descriptive statistics** you are simply **describing
what is** or what the data shows. With **inferential statistics**, you are
trying to reach conclusions that extend beyond the immediate data alone. For
instance, we use **inferential statistics** to try to infer from the sample
data what the population might think.

**Descriptive Statistics**

Let’s say you’ve administered a survey to 35 people about their favorite ice cream flavors. You’ve got a bunch of data plugged into your spreadsheet and now it is time to share the results with someone. You could hand over the spreadsheet and say “here’s what I learned” (not very informative), or you could summarize the data with some charts and graphs that describe the data and communicate some conclusions (e.g. 37% of people said that vanilla is their favorite flavor*). This would sure be easier for someone to interpret than a big spreadsheet. There are hundreds of ways to visualize data, including data tables, pie charts, line charts, etc. That’s the gist of descriptive statistics. Note that the analysis is limited to your data and that you are not extrapolating any conclusions about a full population.

Descriptive statistic reports generally include summary data tables (kind of like the age table above), graphics (like the charts above), and text to explain what the charts and tables are showing. For example, I might supplement the data above with the conclusion “vanilla is the most common favorite ice cream among those surveyed.” Just because descriptive statistics don’t draw conclusions about a population doesn’t mean they are not valuable. There are thousands of expensive research reports that do nothing more than descriptive statistics.

Descriptive statistics usually involve measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation, etc.). Here’s a great video that explains the concept of “average” very well.

Inferential Statistics

OK, let’s continue with the the ice cream flavor example.
Let’s say you wanted to know the favorite ice cream flavors of everyone
in the world. Well, there are about 7 billion people in the world, and it
would be impossible to ask every single person about their ice cream
preferences. Instead, you would try to sample a representative population
of people and then extrapolate your sample results to the entire population.
While this process isn’t perfect and it is very difficult to avoid
errors, it allows researchers to make well reasoned *inferences* about the
population in question. This is the idea behind inferential statistics.

Image Source: Wikipedia

As you can imagine, getting a representative sample is
really important. There are all sorts of sampling
strategies, including random sampling. A true random sample means
that everyone in the target population has an equal chance of being selected
for the sample. Imagine how difficult that would be in the case of the
entire world population since not everyone in the world is easily accessible by
phone, email, etc. Another key component of proper sampling is the *size
of the sample*. Obviously, the larger the sample size, the better, but
there are trade-offs in time and money when it comes to obtaining a large
sample. There are some nice calculators
online that help determine appropriate sample sizes. That’s enough
on market research sampling techniques for now. Let’s get back on track here…

When it comes to inferential statistics, there are generally two forms: estimation statistics and hypothesis testing.

**1. Estimation Statistics**

“Estimation statistics” is a fancy way of saying that you are estimating population values based on your sample data. Let’s think back to our sample ice cream data. First, let’s assume that we had a true random sample of 35 people on this globe and that our full target population is every human alive (7 billion people). Let’s say that 37% of people in our sample said that vanilla is their favorite flavor. Can we safely extrapolate that 37% of all people in the world also think that vanilla is the best? Is that the true value of the world? Well, we can’t say with 100% confidence, but–using inferential statistical techniques such as the “confidence interval”–I can provide a range of people that prefer vanilla with some level of confidence.

2. Hypothesis Testing

Hypothesis testing is simply another way of drawing conclusions about a population parameter (“parameter” is simply a number, such as a mean, that includes the full population and not just a sample).

With hypothesis testing, one uses a test such as T-Test, Chi-Square, or ANOVA to test whether a hypothesis about the mean is true or not. I’ll leave it at that. Again, the point is that this is an inferential statistic method to reach conclusions about a population, based on a sample set of data.

I hope you now have a solid understand of the differences between descriptive and inferential statistics. If you have any comments or contributions, please leave them in the comments below.

REFERENCES

**DeCaro, S. A. (2003). A
student’s guide to the conceptual side of inferential statistics. Retrieved
[December 01, 2011], from http://psychology.sdecnet.com/stathelp.htm**

**The Math Forum:
http://mathforum.org/library/drmath/view/69143.html**

**Descriptive and Inferential
Statistics**

When analysing data, such as the
marks achieved by 100 students for a piece of coursework, it is possible to use
both descriptive and inferential statistics in your analysis of their marks.
Typically, in most research conducted on groups of people, you will use both
descriptive and inferential statistics to analyse your results and draw
conclusions. *So what are descriptive and inferential statistics? And what
are their differences?*

**Descriptive Statistics**

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

Descriptive statistics are very important because if we simply presented our raw data it would be hard to visulize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. For example, if we had the results of 100 pieces of students’ coursework, we may be interested in the overall performance of those students. We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this. How to properly describe data through statistics and graphs is an important topic and discussed in other Laerd Statistics guides. Typically, there are two general types of statistic that are used to describe data:

**Measures of central tendency:**these are ways of describing the central position of a frequency distribution for a group of data. In this case, the frequency distribution is simply the distribution and pattern of marks scored by the 100 students from the lowest to the highest. We can describe this central position using a number of statistics, including the mode, median, and mean. You can read about measures of central tendency here.

**Measures of spread:**these are ways of summarizing a group of data by describing how spread out the scores are. For example, the mean score of our 100 students may be 65 out of 100. However, not all students will have scored 65 marks. Rather, their scores will be spread out. Some will be lower and others higher. Measures of spread help us to summarize how spread out these scores are. To describe this spread, a number of statistics are available to us, including the range, quartiles, absolute deviation, variance and standard deviation.

When we use descriptive statistics it is useful to summarize our group of data using a combination of tabulated description (i.e., tables), graphical description (i.e., graphs and charts) and statistical commentary (i.e., a discussion of the results).

**Inferential Statistics**

We have seen that descriptive
statistics provide information about our immediate group of data. For example,
we could calculate the mean and standard deviation of the exam marks for the
100 students and this could provide valuable information about this group of
100 students. Any group of data like this, which includes all the data you are
interested in, is called a **population**. A population can be small or
large, as long as it includes all the data you are interested in. For example,
if you were only interested in the exam marks of 100 students, the 100 students
would represent your population. Descriptive statistics are applied to
populations, and the properties of populations, like the mean or standard
deviation, are called **parameters** as they represent the whole population
(i.e., everybody you are interested in).

Often, however, you do not have
access to the whole population you are interested in investigating, but only a
limited number of data instead. For example, you might be interested in the
exam marks of all students in the UK. It is not feasible to measure all exam
marks of all students in the whole of the UK so you have to measure a smaller **sample**
of students (e.g., 100 students), which are used to represent the larger
population of all UK students. Properties of samples, such as the mean or
standard deviation, are not called parameters, but **statistics**.
Inferential statistics are techniques that allow us to use these samples to
make generalizations about the populations from which the samples were drawn.
It is, therefore, important that the sample accurately represents the
population. The process of achieving this is called sampling (sampling
strategies are discussed in detail here on our sister site). Inferential statistics arise out of the
fact that sampling naturally incurs sampling error and thus a sample is not
expected to perfectly represent the population. The methods of inferential statistics
are (1) the estimation of parameter(s) and (2) testing
of statistical hypotheses.

**Descriptive Statistics**

Descriptive statistics is the type of statistics that probably springs to most people’s minds when they hear the word “statistics.” Here the goal is to describe. Numerical measures are used to tell about features of a set of data. There are a number of items that belong in this portion of statistics, such as:

- The average, or measure of center, consisting of the mean, median, mode or midrange.
- The spread of a data set, which can be measured with the range or standard deviation.
- Overall descriptions of data such as the
five number summary. - Other measurements such as skewness and kurtosis.
- The exploration of relationships and correlation between paired data.
- The presentation of statistical results in graphical form.

**Inferential Statistics**

For the area of inferential statistics we begin by differentiating between two groups. The population is the entire collection of individuals that we are interested in studying. It is typically impossible or infeasible to examine each member of the population individually. So we choose a representative subset of the population, called a sample.

Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came. There are two major divisions of inferential statistics:

- A confidence interval gives a range of values for an unknown parameter of the population by measuring a statistical sample. This is expressed in terms of an interval and the degree of confidence that the parameter is within the interval.
- Tests of significance or hypothesis testing test a claim about the population by analyzing a statistical sample. By design, there is some uncertainty in this process. This can be expressed in terms of a level of significance.

**Difference Between These Areas**

As seen above, descriptive statistics is concerned with telling about certain features of a data set. Although this is helpful in learning things such as the spread and center of the data we are studying, nothing in the area of descriptive statistics can be used to make any sort of generalization. In descriptive statistics measurements such as the mean and standard deviation are stated as exact numbers. Though we may use descriptive statistics all we would like in examining a statistical sample, this branch of statistics does not allow us to say anything about the population.

Inferential statistics is different from descriptive statistics in many ways. Even though there are similar calculations, such as those for the mean and standard deviation, the focus is different for inferential statistics. Inferential statistics does start with a sample and then generalizes to a population. This information about a population is not stated as a number. Instead we express these parameters as a range of potential numbers, along with a degree of confidence.

It is important to know the difference between descriptive and inferential statistics. This knowledge is helpful when we need to apply it to a real world situation involving statistical methods.