GI Flashcards
What are the types of survey questions
Dichotomous questions
Multiple choice
Likert scale questions (strongly agree, agree etc.)
Rating scale questions
Checkbox questions
Ranking questions
What are the types of sampling methods
Stratified
Systematic
Random
Types of graphs to present data
Bar graph
Pie chart
Line graph
Give example of stratified sampling method
Need 20 Chinese, 20 Malays, 20 Indians for survey
What is a line graph best used for
Comparing trends/data that changes over time
When should you use a pie chart
When 1 person -> 1 vote only, or parts that make up a whole
Give example of a systematic sampling method
Ask for response from every 4th person you see
When is a bar graph best used
to compare categories. the height of the bar makes comparison easier.
Give example of a random sampling method
First go up to 2nd person you see then go up to 7th person you see
what are examples of primary research methods?
-interviews
-surveys
-on-site observation
-field sketches
-photograph analysis
what are examples of secondary research methods?
-newspapers/articles
-books
-online resources
why administer surveys?
-easy to administer
-can collect data from a large number of respondents
-can be administered remotely (e.g. via email, mobile phone, kiosk)
-can collect a broad range of data (behaviours, opinions, facts)
disadvantages of surveys
-respondents may not understand the question and provide false answers, leading to unreliable results
-data collected is largely quantitative, hard to collect qualitative data
advantages of closed questions
-eliminates ambiguous/irrelevant responses
-fast and easy, so more respondents are willing to attempt
-easily interpreted and organised
disadvantages of closed questions
-unable to get a wide variety of answers
-different interpretations of responses
advantage and disadvantage of stratified sampling
advantage: fairer representation of the population
disadvantage: more time-consuming and not always easy to apply
advantage and disadvantage of systematic sampling
advantage: collecting data at regular intervals reduces the potential for bias
disadvantage: individuals may be overlooked because of their position in the periodic interval
advantage and disadvantage of random sampling
advantage: ensures that everyone has an equal chance of being selected
disadvantage: requires you to have a list of members of the population & access to members who are chosen
things to take note of when drawing graphs
-graphs must have axis titles
-short, simple graph titles
-if there is more than one graph, label them
-consistent width and gap
-shade the bars for bar graphs
-make sure graph is accurate
-provide legend/key if necessary
types of survey questions to avoid asking
-leading questions: worded to suggest that there is only one correct answer
-double-barrelled questions: ppl can only choose one answer despite having to answer two qns, ans may not be the same for both qn
-ambiguous questions
-sensitive questions: instead of asking directly for age, income, address etc. can instead provide categories
-too many open-ended questions: difficult to analyse and unable to clarify with respondents
-complex questions
-irritating questions
-loaded questions: questions that make assumptions about the respondent’s habits
-jargon: complicated language not often used/difficult to understand, e.g. technical terms
Adv and disadv of bar graphs
Adv:
Simple to interpret: Bar graphs are straightforward and easy to understand for most viewers, making comparisons between categories clear.
Good for categorical data: They are effective for comparing quantities across different categories or groups.
Visual emphasis on differences: Differences between categories are easily seen due to the height or length of the bars.
Versatility: Bar graphs can display both positive and negative values and can be arranged vertically or horizontally.
Disadv:
Limited to categorical data: Bar graphs are typically used for discrete, categorical data, making them unsuitable for continuous data representation.
Visual clutter: For large datasets or too many categories, bar graphs can become overcrowded and difficult to read.
Over-simplification: Complex relationships between variables might not be well-expressed using bar graphs.
Difficulty comparing bars of similar length: When bars are close in value, it may be hard to perceive small differences visually
Adv and Disadv of pie charts
Adv:
Visualizes proportions effectively: Pie charts excel at showing the relative proportions of different categories in a dataset as parts of a whole.
Easy to understand: Simple and intuitive for audiences to grasp at a glance, especially with a limited number of categories.
Great for emphasizing dominance: They highlight which category dominates the dataset, as the largest slices stand out.
Good for qualitative data: Works well when illustrating qualitative data, especially where categories don’t require exact comparisons but highlight relative proportions.
Disadv
Limited to parts of a whole: Pie charts are only effective for showing proportions of a whole and aren’t suitable for displaying raw values or trends.
Hard to compare sizes: Human perception struggles with comparing angles and areas, making it difficult to differentiate between similar-sized slices.
Ineffective with many categories: With too many slices, a pie chart becomes cluttered and hard to interpret.
No precision: Pie charts lack the precision of other types of graphs like bar charts for comparing exact values
Adv and disadv of line graphs
Adv
Great for showing trends over time: Line graphs are ideal for displaying data that changes continuously over time, making trends, fluctuations, and cycles easy to spot.
Easily shows relationships: Multiple data sets can be plotted on the same graph to compare relationships between different trends.
Clear data progression: Line graphs show how one variable progresses relative to another, especially useful for time-series data.
Effective for small or large datasets: Whether you have a small number of data points or a large dataset, line graphs can adapt well
Disadv
Misinterpretation of non-continuous data: Line graphs imply continuity between points, which can be misleading if the data isn’t continuous.
Clutter with multiple lines: Comparing multiple data sets on the same graph can result in overlapping lines, making it harder to distinguish between them.
Overemphasis on trend: Line graphs may exaggerate trends between data points that aren’t as significant as they appear.
Requires evenly spaced data: Data points should be evenly spaced for a line graph to be effective. Uneven intervals can distort trends.
Adv and disadv of scatter graphs
Adv
Shows relationships between variables: Scatter plots are excellent for identifying correlations, trends, or clusters between two variables, especially in large datasets.
Displays spread of data: It effectively shows how much the data is spread out and identifies outliers or anomalies.
Good for large datasets: Scatter plots handle large amounts of data without becoming visually overwhelming, making them suitable for complex datasets.
Helps in trend prediction: When relationships between variables are present, scatter plots can be useful for predicting future values.
Disadv
Overlapping points: When data points overlap, it becomes hard to see individual values or detect patterns, especially with large datasets.
Hard to interpret without correlation: Scatter graphs are most effective for showing relationships between variables. If there is no clear relationship, the graph may seem confusing or uninformative.
Requires large datasets for effectiveness: With small datasets, scatter plots might not clearly show a pattern or trend.
No clear groupings or categories: Scatter plots are not ideal for categorical data or showing groups of data with specific labels
