# Knowledge Problems â€‹

## Solving: â€‹

### Do activity: (tasks) â€‹

get an overview of activities you need to solve the problem.

Support Tools:

- Gantt charts: dependencies & parallel in time.
- Kanban/Scrum: For agile projects. evaluate each iteration.

### Discover activity: (knowledge) â€‹

find knowledge you require to solve the problem. search for relevant knowledge problems. results in a list of research questions.

### Decide Activity: (scope) â€‹

select the key areas you will focus on. state the limits of your research. Make use of a theoretical framework.

### Types of problem analysis activities: â€‹

- Detailed problem analysis.
- Search for causes.
- Search for known solutions.

## Causes: â€‹

- evidence based causal relationship.
- context related causes.

## Explanatory analysis: â€‹

- variables
- relationships (+/- increase or decrease with variable)
- research population
- A conceptual model visualizes the relationship between variables.
- Independent variables influence a dependent variable.

WARNING

A conceptual model is different from a problem cluster. the model has variables instead of problems example: (research model): `delivery speed`

(problem cluster): `slow delivery speed`

Data Types:

Type | Name | Properties | Example |
---|---|---|---|

Qualitative | Nominal | data used for naming or labeling variables with no ranking | types of cars on the road |

Qualitative | Ordinal | categorical data with an order | grades (A, B, C, D, E, or F) |

Quantitative | Interval | operations(+,-), equal intervals between data points, 0 is arbitrary | temperature, with celsius 0 is arbitrary temperature can be negative |

Quantitative | Ratio | operations(+,-,*,/), equal intervals between data point, 0 is absolute, non negative | Height, cannot be negative |

Check if a relationship is causal:

- Sequence of events.
- Statistical relationship.