Learning objectives

• You should be able to:– Identify the requirements for the Data

Collection and Processing section of the Internal Assessment

– Collect and tabulate raw data– Process your data effectively (step by step

with me)

Data collection and processing—DCP

    Complete / 2 Partial / 1 Not at all / 0

ASPECT

Recording data

Systematically records appropriate 

quantitative and/or qualitative data (primary and/or 

secondary), including units. 

Records appropriate quantitative and/or

qualitative data (primary and/or 

secondary), but with some mistakes and/or omissions (e.g. units).  

Data is not recorded or is recorded 

incomprehensibly. 

Processing  data

Processes primary and/or secondary data 

correctly. 

Processes primary and/or secondary data but with some mistakes and/or omissions. 

No processing of data is carried out or major mistakes are made in 

processing. 

Presenting processed  data

Presents processed data appropriately and effectively to assist 

analysis. 

Presents processed data appropriately but lacks clarity or with some mistake and/or omissions. 

Presents processed data inappropriately or incomprehensibly.

Data collection and processing—DCP

Aspect 1: Recording raw dataDefine problem & select variables

Complete / 2States a focused problem/ research question and identifies the relevant variables.

Partial / 1

States a problem/research question that is incomplete OR identifies only some relevant variables.

Not at all / 0Does not state a problem/ research question AND does not identify any relevant variables.

Recording data

Complete / 2Systematically records appropriate quantitative and/or qualitative data (primary and/or secondary), including units. 

Partial / 1

Records appropriate quantitative and/or qualitative data (primary and/or secondary), but with some mistakes and/or omissions (e.g. units).

Not at all / 0Data is not recorded or is recorded incomprehensibly. 

Data Collection and Processing Aspect 1: Recording Raw Data

• You must collect and process data accurately. 

• But equally important—you must present the data so the reader can easily interpret it. 

• This means it must be organized and legible.  The best

way to present data is by using data tables. 

Types of Data• Raw data is the actual data measured. The term

“quantitative data” refers to numerical measurements of the variables associated with the investigation.

• Qualitative observations are just as important as quantitative measurements!  Make sure you take note of and record the physical characteristics of substances or solutions involved in the experiment, their changes, whether something is hot or cold, etc. 

Collecting and Recording Raw Data

• Must have a RAW DATA TABLE – make sure this is raw data only! (You can make this “neat” later, but keep your raw data table and include it in your laboratory report.)

• Make sure that all columns, etc. are properly headed & units are given.

• DO NOT SPLIT A DATA TABLE BETWEEN PAGES!!

• Uncertainties are mandatory!!!

• Drawings: appropriate size and relative position, accuracy

– Microscopic drawings: Magnification, size bars, treatment (stain used, smear, sectioned, squashed, whole tissue, maintained at ambient temperature, etc.)

Units• A measurement without units is meaningless! 

When you make quantitative observations you are expected to use the appropriate units. The system of units used is the International System of Units - SI units. In the table below you are given some of the more common SI units you will need to use.

Uncertainties • All measurements have uncertainties

and you must indicate them in your data tables. 

• This is best done by paying attention to significant digits, and by using the ‘plus-or-minus” (±) notation. 

• The accepted rule is that the degree of precision is +/- the smallest division on the instrument. Ex: 4.5cm +/- 0.1cm.

Data Collection and Processing Aspect 2: Processing Raw Data

Processing data

Complete / 2 Processes primary and/or secondary data correctly. 

Partial / 1Processes primary and/or secondary data but with some mistakes and/or omissions. 

Not at all / 0No processing of data is carried out or major mistakes are made in processing.  

Data Collection and Processing Aspect 2: Processing Raw Data • This is the part of the report in which you take

your raw data and transform it into results that answer (hopefully!) your research question.   

• Data processing involves, for example, combining and manipulating raw data to determine the value of a physical quantity (such as adding, subtracting, squaring, dividing), and taking the average of several measurements and transforming data into a form suitable for graphical representation.

 • The recording and processing of data may be

shown in one table provided they are clearly distinguishable.

Calculations of Results • You will often have to show calculations. 

• Use plenty of room; make sure they are clear and legible.  • Show the units of measurements in all calculations.

• Pay attention to significant digits!  Don’t lose accuracy by carelessly rounding off.  Round only at the end of a calculation.  Do not truncate.

• Identical, repetitive calculations do not have to be repeated.  Show one sample calculation (labeling it as such) and then you don’t have to repeat it for all the trials, but only show the results obtained.

.

Descriptive Statistics

• Statistics are useful mathematical tools which are used to analyze data.  

Statistical Analysis• Mean • Standard Deviation • Species Diversity Index• Lincoln Index

Data Collection and Processing Aspect 3: Presenting processed

data

Presenting processed data

Complete / 2 Presents processed data appropriately and effectively to assist analysis. 

Partial / 1Presents processed data appropriately but lacks clarity or with some mistake and/or omissions. 

Not at all / 0 Presents processed data inappropriately or incomprehensibly.

Data Collection and Processing Aspect 3: Presenting Processed Data

• Students are expected to decide upon a suitable presentation format themselves (for example, spreadsheet, table, graph, chart, flow diagram, and so on).

• There should be clear, unambiguous headings for calculations, tables or graphs.

• Graphs need to have appropriate scales, labeled axes with units, and accurately plotted data points with a suitable best-fit line or curve (not a scatter graph with data-point to data-point connecting lines).

• Students should present the data so that all the stages to the final result can be followed.

• Inclusion of metric/SI units is expected for final derived quantities, which should be expressed to the correct number of significant figures.

• The uncertainties associated with the raw data must be taken into account.