Spreadsheet design

an overview of further issues

Research Methods Group

Wim Buysse – ICRAF-ILRI Research Methods GroupOctober 2004

How to get the perfect spread sheet ?

Data entry: When ?

ASAP !(As Soon As Possible)

before you forget the details

Data entry: what ?

•RAW DATA• Do calculations and conversions

afterwards, in the spreadsheet using the computer– Formula in MS Excel– Also consistency check

9/11 or 11/9 ?

Outside the USA,

customize the regional settings !!

Do not leave ‘gaps’

Heading information

• Design factors, measurement variables => detailed information

•Column titles, codes

•Don’t leave blank

•Short

•Avoid using “strange” characters (%, $, @, …)

•Otherwise

• summaries / filtering

• importing in other software

Heading information

Heading information

Calculations ?

Calculations

- Use spreadsheet or statistical software

- Use figures

- Let computer do the calculations

- Use Forms to easily write down information when in the field

- Enter data on the computer in a way that they are easy to use in further calculations

Calculations

Same data entered in our standard format

Calculations

Now it is just a matter of presenting the data in the way that best fits the needs

CalculationsEven a participants list can be used to discover trends

One observation/answer per cell- How many training activities took place per location ?

- What was the total number of training days ?

One observation/answer per cell- Our standard format…

- (still data missing)

One observation/answer per cell- How many training activities took place per location ?

- What was the total number of training days ?

Confusing entries- What does 0 mean ?

- What does a blank cell mean ?

- What does an asterisk (*) mean ?

- What does 99 mean ?

AGAIN:

- DO NOT mix figures and text => calculations

- make sure the same code is written in the same way (N/A, n.a., not avail., ….)

- do not forget or mix meaning of codes, ex. 1 = male, 2 = female or vice versa ?? => should not be problem when using standard format

Confusing entries- Solutions

- documentation (description of design factors, description of measurement variables, experiment details)

- insert comment

- add column with comments

- if lots of comments

- easy to find (AutoFilter)

Include or exclude ??

- Include all cases ??

-The short answer is INCLUDE

- Analysis later can be done on subsets

- Insert comment if you think it is a strange observation

Include or exclude ??- Include all variables ??

-The short answer is INCLUDE

- Variables => depend on objectives

- Variables => do not forget to include important design factors like location

Include or exclude : example

Is there any significant difference between apple variety Anna and Golden Dorset in terms of flowering, fruiting and agronomy in the different orchards (= different altitudes) ?

Include or exclude : exampleExample = ‘nice try’

- Gives useful information

- Easy to collect data

(workers in orchards can do it)

- So, research is carried out at almost no cost

Include or exclude : example- Problem: which trees ?

- What happened with the apples (harvested, fallen, eaten by birds, … ?)

- (Probably) flowers and apples were only counted on those trees that were flowering.

- The problem is that this way we’re looking at groups of different size and with different variability.

- We can only answer a question like: of all those trees that are flowering at a given moment, where do they flower the most and where do they have most apples hanging on the trees?

- Solution = include ‘tree number’ as an extra factor + record what happened with the apples.

Unit or level of analysis• e.g. farm/household, person, plot, community,…• Is determined by objectives• Multiple objectives may require data at different levels• One row of data per case• If data are measured at several levels, move data between levels:

Unit or level of analysis• replicating data down levels

farm HH sex HH income1 M no2 F yes3 M yes.

Analysis at plot level

plot crop area farm hhsex1 m 0.1 1 M2 b 0.3 1 M3 b 0.2 1 M4 m 0.4 2 F5 m+b 0.2 2 F6 b 0.3 3 M.

Data at farm level

Unit or level of analysis• Summarising up levels

Data at plot level Analysis at farm level

farm HH sex mono beans bean area1 M y 0.52 F n 03 M y 0.3.

plot crop area farm1 m 0.1 12 b 0.3 13 b 0.2 14 m 0.4 25 m+b 0.2 26 b 0.3 3.

No unique way – think!