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Steps In Running A Survey

Show details for Step 1: Planning a surveyStep 1: Planning a survey
Show details for Step 2: Collecting dataStep 2: Collecting data
Hide details for Step 3: Organising dataStep 3: Organising data

After you have collected the data, it needs to be organised so that it is useful and ready to display.

Frequency tables
A useful way to record raw data is a tally table or frequency table.
A frequency table counts the number of times – or frequency – a value occurs in the data. For example, twenty people are asked "How many TVs do you have in your household?" If 2 households have 1 TV, the frequency of households with 1 TV is 2.

Frequency tables with class intervals
When a variable has a large spread, the values can be grouped together to make the data easier to manage and present.
For example, if you asked students how much time it takes them to get to school each day, their responses may vary considerably. In this case, you can group the responses together in 5 minute intervals. These intervals are called class intervals. All class intervals should have an equal range. Class intervals are usually in groups of 5, 10, 20, 50 etc.

0l l l l4
1l l2
2l l l l l6
3l l l l l l l8
Figure 1: Frequency table of number of TVs per household

1 - 5l l l l l l7
6 - 10l l l l l l l l9
11 - 15l l l l l l l l9
16 - 2011
Figure 2: Example of a frequency table showing five minute class intervals

Hide details for Step 4: Displaying informationStep 4: Displaying information

How will you present your findings? Will you use tables, graphs or both? What sort of table or graph is most appropriate?
One of the most powerful ways to communicate data is by using graphs. Data presented in a graph can be quick and easy to understand.

A graph should:

  • be simple and not too cluttered
  • show data without changing the data’s meaning
  • show any trend or differences in the data
  • be accurate in a visual sense – for example, if one value is 15 and another 30, then 30 should be twice the size of 15
Ambiguity can be reduced by
  • avoiding 3D representations
  • avoiding broken or uneven scales
It is important that you give each graph a heading. Axes must be labelled and any scale must have equal intervals. A key should be included where it is needed to interpret the data.
Different graphs are useful for different types of information and it is important that the right graph for the type of data is selected. See What graph or display to use when for more information.
Consider the type of data you have collected when choosing a display or graph.
Type of dataAppropriate display or graph
CategoricalBar graph, pie chart, dot plot
Numerical (discrete)Bar graph, histogram, line graph, box and whisker plot, stem and leaf plot, age pyramid
Numerical (continuous)Histogram, line graph, box and whisker plot, stem and leaf plot

Bar graphs – Figure 3
A bar graph is used to represent categorical or discrete numerical data. A bar graph has a gap between each bar or set of bars and the widths of the gaps and bars are consistent. The length of the bars in a bar graph is also important: the greater the length, the greater the value. A bar graph can be either horizontal or vertical – vertical bar graphs are also known as column graphs.

Horizontal bar graphs – Figure 4
The advantage of using a horizontal bar graph over a column graph is that the category labels in a horizontal bar graph can be fully displayed making the graph easier to read.

Side by side bar or column graphs – Figure 5
Side by side bar graphs are useful to compare two or more groups for the same data.

Stacked bar or column graphs – Figure 6
Stacked or segmented bar graphs are sometimes used to display a breakdown of data for a particular group – for example, the breakdown of types of internet use by sex. They are most useful when the data is shown as a percentage value and the number of categories is very small. If there are too many categories, the frequency of the datasets can be difficult to read and compare.

Histograms – Figure 7
A histogram is similar to a column graph, however, there are no gaps between columns. Histograms are used for numerical data only. Data can be either discrete or continuous and grouped or ungrouped. A histogram should have equal width columns when possible.

Line Graphs
Line graphs are used for numerical data. They show how one variable changes in relation to another. The independent variable is always displayed on the horizontal axis. It is important to use a consistent scale on each axis so you can get an accurate sense of the data.

Time Series – Figure 8
Time series graphs are line graphs that display a trend or a pattern in data over time. The slope can be upward or negative. Patterns can be seasonal (over a short period), or cyclic (over a longer period). Alternately, a time series can show a random variation.
To use a times series to make predictions, you can smooth fluctuations by using a suitable smoothing process such as deseasonalising the data.

Pie Charts – Figure 9
Pie charts, often called pie graphs, sector graphs or sector charts, show how much each sector contributes to the total. Pie charts are useful when there are only a few categories as more than five categories can make them difficult to read.
It is very important to label each sector with its value to make comparison easier.

Dot Charts – Figure 10
A dot chart can convey a lot of information in a simple, uncluttered way. Each dot can represent one or many.

Box and Whisker Plots – Figure 11
Box and whisker plots display 5 figure summary statistics: minimum, quartile 1, median, quartile 3 and maximum for a set of numerical data.
Box and whisker plots are useful for looking at the shape of a data set. In particular, parallel box and whisker plots are used to compare two data sets and are drawn on the same number line. To help identify possible outliers, 'fences' can be drawn at 1.5 x, the IQR above Q3 and below Q1. For more information, see summarising Data, Measures of Spread.

Stem and Leaf Plots – Figure 12
Stem and leaf plots are an efficient way of recording numerical data because numbers in the stem apply to all values in the leaves. They are also very useful to show the shape of a distribution and, since they order data, can be used to identify median and quartiles.
Back to back stem and leaf plots are used to compare the distribution of two data sets.

Age Pyramids – Figure 13
Age pyramids are used to represent a population age structure. Age pyramids are a very effective way of showing change in a country’s age structure over time or for comparing different countries. Estimates and projections of Australia's population from 1971 to 2050 are available on the ABS Animated Age Pyramid page.

Figure 3: Bar graph showing importance of conserving water.
Input from 0 (Not Important) to 999 (Very Important)
Source: 2010 C@S National summary table 28.

Figure 4: A horizontal bar graph

Figure 5: A side by side column graph

Figure 6: A stacked bar graph

Figure 7: A histogram showing data with grouped intervals

Figure 8: A time series graph

Figure 9: A pie chart

Figure 10: A dot plot with many to one correspondence

Figure 11: Parallel box and whisker plots

Figure 12: A back to back stem and leaf plot of arm span

Figure 13: An age pyramid (Source: Australian Bureau of Statistics Education Services)

Show details for Step 5: Analysing the dataStep 5: Analysing the data
Hide details for Step 6: Drawing conclusionsStep 6: Drawing conclusions

Communicating the results of your investigation is a critical part of the survey process. Ensuring the accuracy of any interpretations and avoiding misinterpretations are crucial. Keeping in mind the purpose of the investigation and your audience will help to keep your conclusions on track and avoid including unnecessary information.

Accuracy of Data
All your calculations need to be accurate, verifiable from the data and clearly communicated using simple language.

To effectively communicate your results, you will need to be aware of avoiding any misinterpretation of the data such as using the mean when the median is more appropriate or not taking seasonal variation into account.

Stating your conclusions
With statistics, there is always a risk that the results you have do not tell the whole story. You can use the following checklist to help judge the reliability of your statistical information.

  • Do your conclusions communicate the message told by the data?
  • Are your conclusions based on results rather than on your opinions?
  • Have you considered alternative explanations for the same results?
  • Is your report set out logically including using an organisational framework such as headings and sub headings?
  • Have you included the source of any information you have used or referred to?
  • Have you included relevant tables and graphs?
  • Are your findings clear, related to your aim and only contain necessary information?

  • Have you considered your audience and used appropriate language?
  • Have you anticipated questions your reader might have? For example, have you explained unusual or unexpected results? Have you justified your choice of analysis, indicated your sampling process etc?
  • Can your reader check your conclusions by viewing your analysis?

Sampling Error
Finally, if you intend for your results to be applicable in other contexts, it is important to understand the limits that might apply.
The difference between an estimate based on a sample survey and the true value that would result if a census of the whole population was taken is called the sampling error. Sampling error can be measured mathematically and is influenced by the size of the sample. In general, the larger the sample size the smaller the sampling error.
The way a sample is drawn is also important. In general, a random sample will result in data that is more able to be generalised to the population.

Commonwealth of Australia 2008

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