Theories of change: sense and nonsense

Theories of Change diagrams are a kind of visual language for saying how one thing leads to another.
Unfortunately there is not yet any dictionary or grammar for this language to tell us how to draw a
diagram and what the elements mean: only lots of dialects. Still, we can identify the difference
between a Theory which makes sense and one which mumbles or just talks nonsense.
These slides are a bit of a rant on what makes sense and what doesn't, and on how to make sense and
be usefully expressive when we write a Theory. I also make some suggestions for some common
conventions and standards, based on a language for Theories of Change called Theorymaker.
The slides are in some kind of order but should be relatively independent of one another, so you
might find some points get repeated a few times. If you get bored of one, just click through to another.
The most recent versions are at theorymaker.info/slides.html.
If you click on the small "Clone" link at the top-right of any diagram, you will be invited to play with a
version of the same diagram at theorymaker.info.
If you are just starting with Theories of Change, read an introduction instead. Also, here I only deal
with the theory of Theories of Change, not the practical or political aspects (who creates them, how
and why), though those things are important too.
Abouttheseslides

TheoriesofChangearemadeupofVariables
So what are those rectangles which make up a Theory of
Change? They are Variables: they stand for things that
can be different. So a law might be passed or not passed,
and a President's support for a proposed law can vary
from, say, low to high. But look at this …
Clone
These are not Variables. This is not a Theory of Change.
NGO Network President
lobby about
proposed law
This kind of network diagram can be very useful but it
isn't a Theory of Change because the rectangles are not
Variables. We can replace that diagram with something
like this:
Clone
Try this instead
NGO Network: how
much lobbying
activity carried
out on proposed
law
President: how
positive is her
opinion about
proposed law
Now, both rectangles represent Variables which can take
different levels or values. Each of them - the level of the
lobbying activity, and the President's level of support,
can be low or high, or anywhere in between. And the
arrow suggests (perhaps naively) that the lobbying
influences in some way the President's opinion.
3/30

MostVariablesinTheoriesofChangeare(lo-hi) Variables
"The President's support for the new law" is something
that could be very low, or very high, or any level in
between. In the Theorymaker language it is called a (lo-
hi) Variable, otherwise known as a continuous ordinal
Variable.
Clone
Amount of lobbying (lo-hi)
President's support for
proposed law (lo-hi)
(lo-hi) Variables are incredibly common in real-life
Theories of Change: quality of implementation, level of
tolerance, satisfaction, support …
Any possible level of a lo-hi Variable can always be
compared with any other possible level. So it always
makes sense to say, for example, the President's support
is stronger (or weaker) than it would have been without
any lobbying, or, in the case of Variables which "stretch"
over time, that the support is stronger (or weaker) than
it was yesterday, … or, in the case of a Variable which is
defined for more than one person, that it is stronger (or
weaker) than the Vice-President's.
Don't worry at this stage about how exactly you would
measure something like "The President's support for the
new law" in numbers. We understand ideas like "the
more the lobbying, the more the support" - one (lo-hi)
Variable influencing another - even before we
understand numbers; and we can agree it is true, or not,
without having numerical versions of the Variables.
Actually, numbers are "too accurate" for (lo-hi)
Variables. Forcing them into numerical form can create
more problems than it solves (though that is usually
what happens in real-life research and evaluation, and
has been science's default strategy since Galileo). But we
can still do serious reasoning with (lo-hi) Variables
using what we call Soft Arithmetic - working with
comparisons rather than measurements.
In the Theorymaker language we assume, if we have no
other information, that any given Variable is (lo-hi) -
because they are so common. If it isn't, for example if it
is a (no,yes) Variable, it should be marked as such.
4/30

SaywhatkindyourVariablesare
Clone
What kind are these Variables?
Enabling law
on same-sex
marriage at
Federal level District law
on same-sex
marriage
Support among
legislators
Support among
population
It is often useful to show on your Theory of Change what
kinds your Variables are: what different values or
"Levels" they can take. You might be familiar with
numerical Variables and categorical Variables from
software like SPSS. The Theorymaker language
distinguishes several more kinds, but these two are
particularly useful:
"The law is passed" is an example of a (no,yes) Variable:
it can be or not be, happen or not happen. "The level of
support for the law" is something that can slide all the
way from low to high, and is called a (lo-hi) Variable. In
this example, we have shown which Variables are
(no,yes) and which are (lo-hi):
Clone
Try this instead
Enabling law
on same-sex
marriage is
passed at
Federal level
(no,yes)
District law
on same-sex
marriage is
passed (no,yes)
Support among
legislators
(lo-hi)
Support among
population
(lo-hi)
measure something like "A citizen's support for the new
law" in numerical form. We understand ideas like "the
further I fall, the more it hurts" - one (lo-hi) Variable
influencing another - even before we understand
numbers; and we can agree it is true, or not, without
even trying to put the Variables into numerical form.
(lo-hi) Variables are incredibly common in real-life
Theories of Change: quality of implementation, level of
tolerance, satisfaction, support …
5/30

NoneedtodivideaTheoryintoslices(outputs,outcomes…)
Clone
You don't have to stick to this format
Output:
improved
student
classwork
Outcome:
improved
student exam
resultsActivity:
student
workshop 1
held
Activity:
students
given
laptops
Output:
improved
student exam
skills
Activity:
student
workshop 2
held
Lots of Theories of Change, especially those based on
Logframes, are structured in slices, like this one. Here,
the slices go from left to right: Activities, Outputs etc. All
Variables which are one step away from the final
outcome belong to the "Outcome" slice. Behind them
are the "Outputs", etc. But in real life, as in the next
example, Variables do not fit neatly into slices. Claiming
otherwise is just "fake science".
Clone
Clone
Try this instead
students
receive
laptops
improved
student exam
results
improved
student
classwork
student
workshop 1
held
improved
student exam
skills
student
workshop 2
held
If you think the laptops might directly contribute to
improved exam results, apart from the indirect effect via
classwork, perhaps because the students can revise
better, feel free to add an arrow (I've made it thicker just
for this example).
Students receive laptops is both one step and two steps
away from the final outcome. So the "slices" structure
breaks down. Just like in the real world, no-one need
worry about whether something is, say an "output" or an
"intermediate result".
6/30

NoneedtodivideaTheoryinto"phases"
A Variable's position implies almost nothing about timing
Clone
Output: (Feb-May)
improved student
classwork
Outcome: (June)
improved student
exam resultsActivity (January):
student workshop 1
held
Activity (January):
students given
laptops
Output: (Feb-May)
improved student
exam skills
Activity (February):
student workshop 2
held
Often, when a Theory is forced into neat slices like this
(in this diagram there are three slices from "Activity" to
"Outcome", from left to right), we are supposed to
understand that all the activities in each slice take a
specific amount of time, which complete before the next
starts: the slices are phases.
We can design a project like this if we want to, it can be
very convenient, but believing projects have to have
discrete phases is just "fake science".
If the Prime Minister calls snap elections in two weeks,
discrete phases is just "fake science".
my party has to launch a campaign to influence a whole
nation's behaviour in just 14 days, perhaps like this:
Clone
This is possible too
Electorate:
more favourable
voting
intentions
(today until
election)
My party wins
election (14
days from today
- very brief
event)
Campaign
running: (today
until election)
Key speeches:
(in 3 & 7 days'
time)
Continuous Variables can stretch over time, and discrete
Variables can repeat, and their timings can overlap one
another in any number of different ways. A Variable's
position in a Theory doesn't tell you how it stretches
across time.
7/30

BeclearaboutwhatorwhoyourVariablesbelongto
Suppose an NGO gives you this Theory and asks you to
improve it.
Clone
Not very clear
Critical thinking abilities
Inter-ethnic tolerance
Opportunity for interaction
… somehow, tolerance depends on critical thinking
ability and opportunities for interaction. But where is
this tolerance? Who has these opportunities, for
interaction between …? You can make a Theory like this
clearer by saying, wherever possible, who or what do the
Variables belong to. You can't of course answer those
clearer by saying, wherever possible, who or what do the
Variables belong to. You can't of course answer those
questions just by looking at the Theory, because the
whole problem is that the relevant information isn't
there. You have to ask the NGO, to help them clarify
their initial thoughts. Perhaps, for example, they meant
this:
Clone
Try this instead
Critical thinking abilities
◼ who=student from ethnic group A
Tolerance towards students
from ethnic group B
◼ who=student from ethnic group AOpportunity to interact with
students from ethnic group B
8/30

ReasonstoavoidpassivesentencesinTheoriesofChange
It's good advice to usually avoid using passive sentences
in Theories of Change.
One reason is that active sentences are usually easier to
understand. But here's another reason.
Clone
We can improve this
City mayors' activities are
monitored
City mayors are more
transparent in policy
implementation
In Theories of Change, it often useful to make it clear
who or what a Variable "belongs to". The two Variables
above look like they belong to the City mayors. So if we
wanted to find out more about each of the two
Variables, we would go to the mayors, would we?
Perhaps not.
This next diagram probably expresses better what we
meant. And in this case we'd first go to the activists to
gather data on the Variable.
Clone
Try this instead
Citizen activists monitor city
mayors
City mayors are more
transparent in policy
implementation
Or maybe we don't know who is going to do the
monitoring, and that's why we used the passive.
Expressed actively: "Somebody is monitoring city
mayors". But that is a "hard-to-search" Variable. Where
would we go, what would we do, to gather data on that?
It might be useful to ask the mayors if they notice they
are being monitored. At least we can gather data on that
(though it doesn't solve our problem with the
"somebody" Variable).
Clone
Another possibility
City mayors are
aware of being
monitored
City mayors are
more transparent
in policy
implementation
Somebody is
monitoring city
mayors
9/30

EmergentVariablesinTheoriesofChange
Have a look at this Theory of Change fragment:
Clone
More
innovation at
Universities in
Croatia ֍
Universities in
Croatia are more
successful
Support given
to innovation at
Universities in
Croatia
It shouldn't be too hard to define in advance what would
count as evidence for "Support given to innovation at
Universities in Croatia". We might not have done it yet,
but if we sit down with our peers, we don't expect
rampant disagreement about what counts as evidence.
The same should be possible for "Universities in Croatia
are more successful" - evidence might be students
graduating, research published etc. But what about the
intervening Variable? In the Theorymaker language this
is called an emergent Variable, and we mark it with a
spiral symbol.
The key thing about an emergent Variable is that, given
the Variable label ("More innovation at Universities in
Croatia") we find it hard to describe in advance what
evidence for it would look like - but we can mostly
recognise evidence for it once we see it. The reason we
evidence for it would look like - but we can mostly
recognise evidence for it once we see it. The reason we
can't specify "innovation" very clearly in advance is
because if we could, it wouldn't be innovation.
The difference between emergent Variables and others
is not hard-and-fast. Often, we have some idea of what
evidence for a particular Variable would look like, but are
very open to being surprised or persuaded about
unexpected evidence too.
Emergent Variables are not a bad thing. They often play
key roles in our most treasured Theories. Most parents
hope their children might become wonderful people but
most would be reluctant to even imagine in any detail
how - while perhaps retaining some minimum standards
they hope their children will reach and some red lines
which they hope they won't cross.
Clone
My efforts to bring up my
children
The wonderfulness of my
children when they become
adults ֍
10/30

GroupingtogetherVariableswithsimilarfeatures
Have a look at this Theory. It's nice because each
Variable has an additional feature who=... which tells
you who or what the Variable belongs to - in this case,
generic students from ethnic groups A and B, and also
the interaction between them. It so happens that in this
Theory, interactions between two such students are
longer when student A is older - but the age of student B
doesn't matter.
Clone
We can simplify this
from ethnic group B
Length of interaction
◼ what=Interaction between students
from groups A and B
Age
from ethnic group A
◼ who=student from ethnic group B
In the next diagram, we've grouped together two
Variables with the same feature (belonging to the
student from group A).
So we've saved a bit of typing. Not much in this case, but
this trick can be very useful when we have lots of
Variables.
Clone
Try this instead
_
from ethnic group B
Length of interaction
◼ what=Interaction between students
from groups A and B
Age
from ethnic group A
◼ who=student from ethnic group B
We can use the same idea to group together any set of
Variables which share some feature.
11/30

WhattodowhenwehaveoneVariableforeachpersoninagroup
Here, the same teacher teaches three different students
- his ability contributes to their achievement, and their
achievement in turn influences his pride.
Clone
Achievement
◼ who=student 1
Pride
◼ who=Teacher
Ability
◼ who=Teacher
Achievement
◼ who=student 2
Achievement
◼ who=student 3
We very frequently encounter sets of Variables like this
which are almost the same but belong to different
people (or time points, or countries, firms …).
Sometimes these whole sets of Variables are just called
"Variables", which can be confusing. Statisticians in
particular only deal with Variables like this, which means
it is hard for them to deal with one-off Variables like "the
particular only deal with Variables like this, which means
it is hard for them to deal with one-off Variables like "the
passing of the new law on same-sex marriage, yes or no"
which we frequently encounter in our Theories of
Change.
In the Theorymaker language we call these sets of
Variables "for-each Variables". We can save a lot of ink
by putting just a single for-each Variable in place of
several, marked For each..., as in this example:
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Try this instead
Achievement,
For each... student
(students 1, 2, 3)
Pride
◼ who=Teacher
Ability
◼ who=Teacher
But we have to remember that these are actually sets of
Variables, one for each person, or country, or whatever.
12/30

Variableswhichstretchorrepeatacrosstime
In Theories of Change, unlike in statistics, we have to
assume that, by default, Variables exist just once in time,
like a voter's decision to vote a certain way on a
particular election day with a specific date. To be
consistent, we can treat phenomena which stretch or
repeat across time as sets of Variables, one for each time-
point, like this:
Clone
Voter hears today's speeches
◼ day=Monday
Voter decides to vote for Tory
party
◼ day=Election Thursday
◼ day=Tuesday
◼ day=Wednesday
In the Theorymaker language we call this set of purple
Variables a "for-each-time" Variable - we can pretend it is
just one Variable, though we know it is several. (We use
the same idea for "for-each" Variables for sets or groups
of people, or countries, or any other situation where we
have sets of repeated Variables.) We can mark a for-each
Variable with the words "For each …" like this:
Clone
Clone
Try this instead
Voter hears the day's
speeches
For each... day
party
◼ day=Election Thursday
These kinds of "for-each-time" Variables are so common
that we have special symbols for them. They are a bit
imprecise but very useful.
Below, the ^^^ symbol says that this is a discrete Variable
which is repeated across time; the ~~~ symbol says this
is a continuous event which happens all through the time
period; and the __^ symbol says this is a discrete event
which happens only at the end. So the _ says that the
Variable is not defined or considered.
Clone
Try this instead
Voter hears the
day's
speeches ^^^
party __^
Voter reads social media ~~~
13/30

ThreesymbolstohelpshowwhenaVariablestartsandstops
Many traditional Theories of Change are sliced into
"phases"; each phase defines when the Variables within
it start and end. But in real life, a Theory can contain
continuous Variables which stretch over time, and
discrete Variables which can happen once or repeat, and
their timings can overlap one another in any number of
different ways which don't fit into phases. In a traditional
Theory of Change we know a Variable's timing as soon
as we know what phase it is in. With more flexible
Theories, we need another way to show a Variable's
timing.
The Prime Minister has called a snap election in just two
weeks' time. Here is my party's plan to win:
Clone
Electorate:
more favourable
voting
intentions
(today until
election) ~~~
My party wins
election in 14
days from today
__^
Campaign
running: (today
until election)
~~~
Key speeches:
(in 3 & 7 days'
time) _^_^_
The Theorymaker lanaguage provides three simple
symbols, which you can type from any keyboard, to help
you sketch a rough overview of timings.
So, looking at the diagram:
Similarly,
If you need more precision, use a Gantt chart.
14/30

AVariable'spositiontellsyounothingaboutitsduration
Clone
Output: (in
six months)
improved student
classwork
Outcome: (next
year) improved
student exam
resultsActivity:
student
workshop
1 held
Activity:
students given
laptops
Output: (in six
months) improved
student exam
skills
Activity:
student
workshop
2 held
Often, when a Theory is forced into slices like this (in this
diagram there are three slices from "Activity" to
"Outcome", from left to right), we are supposed to
understand that the Variables in the later slices such as
the final Outcome(s) are necessarily "longer-term" and
will last or sustain longer.
But this is just "fake science".
nation's behaviour in just 14 days, starting tomorrow,
nation's behaviour in just 14 days, starting tomorrow,
perhaps like this:
Clone
Electorate:
more favourable
voting
intentions (from
tomorrow until
election) ~~~
My party wins
election (14
days from today
- very brief
event with
longer-term
consequences)
__^
Campaign
running: from
today until
election ~~~
Making a difference to a Variable can take minutes or
centuries, and then last for minutes or centuries. This
depends on many things but not at all on the Variable's
position in some Theory.
The only reason to attach specific timings to a Variable's
position is the same as the reason to put Variables into
"slices" in the first place: administrative convenience.
Sure, sustainable social change is hard but not because
it is so many links away in some Theory.
15/30

BecarefulwithVariableswhicharedefinedintermsofothers
Clone
NO!
Teachers have skills
in North Region
Teachers in whole
country have skills
Workshops delivered
in North Region
Teachers have skills
in South Region
Workshops delivered
in South Region
The arrows in a Theory of Change should show causal
influence. But in this Theory fragment, "Teachers in
whole country have skills" isn't influenced by Teachers
have skills in North (and South) Region - it is just the sum
of the two, by definition.
If some Variables define another, any data which is
evidence for those defining Variables is also evidence for
the Variable they define. (So it if you already have
"indicators" for the defining Variables, there is no need
to seek additional "indicators" for the defined Variable as
well.)
If you have to include a defined Variable in your Theory,
mark it clearly, e.g. with dotted borders and/or dotted
arrows:
Clone
Try this instead
Teachers have
skills in North
Region
Teachers in
whole country
have skillsWorkshops
delivered in
North Region
Teachers have
skills in South
Region
Workshops
delivered in
South Region
Or, better, omit the defined Variable completely and
organise your Theory visually using a grouping box:
Clone
Not clear!
Teachers in whole country
have skills
Workshops delivered in North
Region
Teachers have skills in
North Region
Workshops delivered in South
Region
Teachers have skills in
South Region
16/30

Don'tuseaVariabletoshowacausallink
Clone
NO!
B) Children improve
critical thinking
skills
C) Teachers
introduce critical
thinking materials,
leading to children
improving critical
thinking skills
A) Teachers
introduce critical
thinking material
This Theory fragment is a real mess, breaking at least
two rules:
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Try this instead
Teachers introduce
materials which help
children improve
skills
A) Teachers
introduce critical
thinking material
B) Children improve
critical thinking
skills
In this improved version, the causal story stands for
itself.
Here, we also used a grouping box to label the causal
story, but this is just optional decoration.
The arrows imply that A and B influence C, whereas in
fact A influences B and C just summarises that fact.
The Variables in a Theory of Change should be free of
causal words like "through" or "via" or "because".
·
·
17/30

Don'tuseaVariabletogroupthepartsofyourproject
Clone
No!
Activities for teachers
Student-teacher interaction
is more solution-oriented
Teachers attend training
Teachers attend workshops
Activities for students
Students attend workshops
Students take part in
discussion on social media
This Theory fragment tries to use "Activities for teachers"
and "Activities for students" as a way to organise the
diagram. But this is wrong, because all arrows in a
Theory of Change should show causal influence, and
here the red arrows do not. Here is one alternative:
Clone
Try this instead
Teachers have the necessary
skills
Student-teacher interaction
is more solution-oriented
Students have the necessary
skills
The red Variables are influenced by their parent
Variables and influence their child Variables, which is
how it should be.
In this second alternative, we just just eliminate the
original Variables and use two grouping boxes instead:
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Try this instead
Activities for teachers
Activities for students
Student-teacher interaction is
more solution-oriented
18/30

OneVariablecaninfluencemorethanoneotherVariable
Clone
A.1 Outcome 1
A. Goal
A.1.a Output
A.1.b Output
A.2 Outcome 2
A.2.a Output
A.2.b Output
In lots of Theories of Change, especially those based on
Logical Frameworks, every Variable only has one child
Variable immediately downstream of it. In other words,
it is only supposed to be able to contribute to exactly
one other Variable.
Why? The "only one child rule" is convenient for
administrators. And it allows us to number the Variables
in our project like in this example.
But in real life … Of course a Variable can influence
in our project like in this example.
But in real life … Of course a Variable can influence
more than one other Variable!
Clone
Try this instead
improved
student
classwork
improved
student
exam
results
improved
student
employment
prospects
student
workshop 1
held
students
receive
laptops
students
more able
to write
good CV
improved
student
exam
skills
student
workshop 2
held
improved
student
self-confidence
If your world looks more like this, make sure your
Theory of Change reflects it (unless you are forbidden to
do so, for example because of the planning or reporting
formats you have to use).
19/30

ATheoryofChangecanhavemorethanonevaluedVariable
Clone
improved student
classwork
improved student
exam results
student workshop 1
held
students receive
laptops
improved student
exam skills
student workshop 2
held
Lots of Theories of Change, like this one, have only one
valued Variable. You can tell which Variable is valued
because it is just the last one in the chain. Lots of names
have been invented for these valued Variables, like
"Outcome", "Goal" etc. But in the real world, you (or
maybe other stakeholders) often value more than one
Variable. If so, feel free to say so!
So if you think the laptops might have other valuable
consequences for the students, just mark them as
valuable (like in this example):
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Try this instead
students receive
laptops
students can pursue
own study interests
better
♥
students can
apply for jobs
better
♥
improved student
classwork
improved
student exam
results
♥
student workshop 1
improved student
exam skills
student workshop 2
Here, we also suggest that workshop 1 can help students
pursue their own interests.
The Theorymaker language uses a " ♥ " symbol to mark
valued Variables. You can even add more heart symbols
for Variables you value most.
20/30

IntermediateVariablescanbevaluabletoo
Clone
improved
student exam
results
improved
student
employment
prospects
♥
improved
student
self-confidence
students
attend
workshop
improved
teacher
skills
teachers
attend
workshop
Lots of Theories of Change, like this one, have only one
valued Variable, the last one in the chain. Lots of names
have been invented for these valued Variables, like
"Outcome", "Goal" etc. But in the real world, you (or
maybe other stakeholders) might value Variables which
are not at the end of a chain. If so, feel free to mark them
as valued too. The Theorymaker language uses a " ♥ "
symbol.
For example, maybe you believe student self-confidence
is valuable in its own right. Maybe you would still be
quite pleased with the project if it made a big difference
to student self-confidence regardless of its effect on
exam results, as in this example:
Clone
Try this instead
improved
student exam
results
improved
student
employment
prospects
♥
improved
student
self-confidence
♥
students
attend
workshop
improved
teacher
skills
teachers
attend
workshop
It's true, some intermediate Variables (like attending
workshops) are really only means to an end. But some
are valuable in their own right: don't undersell your
project if it really does produce additional value! You
might even want to add more hearts for Variables you
value more.
Sure, someone can always argue "you only value self-
confidence because it leads to something else, the
things you really value". But you can say that about any
valued Variable, for example employment prospects.
And then it is turtles all the way down.
21/30

DaretomentionVariablesyoucan'tcontrol!
Clone
Legislators
have more
favourable
views
Law on
same-sex
marriage
is passed
Public
opinion
improves
Our
campaign
is
launched
In many Theories of Change, the root Variables (the ones
with no parent Variables contributing to them) are
assumed to all be under the control of our project. But
of course the world isn't like that. Sometimes it can be
really useful to show the most important other factors
which contribute to the downstream Variables.
(In some Theories of Change, these additional Variables
plus the claim that they actually take the value or "Level"
we want them to, are referred to as "Assumptions".)
If you do add Variables, you need to make it clear which
Variables actually are under your control. In the
Theorymaker language, we do this by marking Variables
we can control with a green ► (or just by writing "!do").
Clone
Try this instead
Legislators
have more
favourable
views
Law on
same-sex
marriage
is passed
Other
campaign
lobbies
legislators
Public
opinion
improves
▶ Our
campaign
is
launched
Celebrities
speak out
Another way to include additional Variables is as
assumptions on the arrows:
Clone
Try this instead
Legislators
have
more
favourable
views
Law on
same-sex
marriage
is
passed
Assume:
Other
campaign
lobbies
legislators
Public
opinion
improves
Assume:
Celebrities
speak
out
▶ Our
campaign
is
launched
22/30

It'srareaVariableiscompletelydeterminedbyothers
Have a look at this Theory.
Clone
Enabling law
on same-sex
marriage is
passed at
Federal level
(no,yes)
District law
on same-sex
marriage
is passed
(no,yes) ◐Support among
legislators ◐
Support among
population
Does anyone believe that, say, "Support among
legislators" is entirely determined by "Support among
population"? Or that "District law on same-sex marriage"
is entirely determined by "Enabling law on same-sex
marriage at Federal level" and "Support among
legislators"? Surely not. We are all aware that there are
other important influences not shown here.
The Theorymaker language suggests using a half-filled
circle symbol to show this kind of incomplete
determination, as in the example. However, this is so
frequent that, if in doubt, we assume incomplete
determination, as in the example. However, this is so
frequent that, if in doubt, we assume incomplete
determination. So we often don't bother with the half-
filled circle.
We can also assume that if we know of any other
substantial individual influences, they should be shown
too. So the uncertainty left on our diagram can be
attributed to many small influences or just random
noise.
In the special case when we only want to show the mere
existence of a link, however weak, we use an empty
circle. In the next example, any influence at all would be
sensational news to most doctors.
Clone
Dose of homeopathic medicine Patient symptoms ◯
Finally, for the rare case of (almost) complete
determination we use a filled circle.
23/30

Assumeindividualinfluencesare"overallpositive",butnot
necessarilylinear
Clone
Support for new law
among population
Support for new law
among legislators
In this piece of a Theory, is the influence of population
support on legislator support likely to be linear, in the
sense that, for every 1% increase in population support,
support among legislators always goes up by, say, 1%??
Why would anyone think that? Why should the graph be
a straight line? There might be, for example, a tipping
point, so it is hard to get support up to, say, 50% and
easier thereafter. The line on the graph could have
almost any shape. How can we cope with all these
possibilities?
The Theorymaker language has a useful convention: if in
doubt, assume that a Variable's influence on its child is
not necessarily linear but at least "overall positive" in the
sense that making any positive difference on it leads to a
positive difference on the child Variable. Otherwise, you
need to describe the influence. So the influence shown
positive difference on the child Variable. Otherwise, you
need to describe the influence. So the influence shown
in this graph is not overall positive because of the blue
part of the graph, where if "support in population"
improves a little, legislator support actually goes down.
24/30

WhenaVariableisinfluencedbyseveralothers,assumeeach
influenceis"overallpositive"
Clone
Support among population
Support among legislators
Pressure from party leadership
There is another sense in which people talk about linear
influences in Theories of Change: the idea that, if a
Variable has more than one Variable influencing it, the
total influence on it is just the sum of the influences of
the individual parent Variables. In other words, to know
the influence of "Support among population" on
"Support among legislators", it is not necessary to know
anything about "Pressure from party leadership", and
vice versa.
"Linear" in this sense is short for "Linear superposition".
Quantitative scientists like it because it makes things
easier to calculate. But it would be "fake science" to
think that therefore, that's how the world is. Suppose for
example that, when interpreting support from the
population, individual legislators consider what the party
example that, when interpreting support from the
population, individual legislators consider what the party
leadership thinks. For example, if the leadership is totally
against, pressure from the population might have a
negative influence on legislators.
There are a lot of ways several Variables can combine.
How to keep things simple? We suggest adopting yet
another convention from the Theorymaker language: if
in doubt, assume that when several Variables
influence another, each influence is "overall positive",
in the sense that every positive difference on a parent
Variable leads to some positive difference on the child
Variable, regardless of the state of the other contributing
Variables.
(This isn't the same as assuming that the influences are
independent. Assuming overall positive influences is much
more useful and much more likely to be true than
assuming independence.)
25/30

Showing"overallpositive"and"overallnegative"influences
Clone
This is misleading
Strength of
sunshine
Subjective
temperature
Strength of
wind
How warm you feel depends partly on the strength of
the sun. We don't need to know exactly: following the
conventions of the Theorymaker language: if in doubt,
assume that a Variable's influence on its child is, while
not necessarily linear, at least "overall positive" in the
sense that any positive increase in the strength of the
sunshine means the subjective temperature goes up.
But the other arrow is misleading because strength of
wind makes us feel colder, not hotter. Theorymaker
suggests using a blue arrow and/or a minus-sign (-) to
wind makes us feel colder, not hotter. Theorymaker
suggests using a blue arrow and/or a minus-sign (-) to
mark overall negative influences: any increase in wind
means a decrease in subjective temperature.
Clone
Try this instead
Strength of
sunshine
Subjective
temperature
Strength of
wind
-
(Also, in Theorymaker, we assume that the influence of
this Variable is overall negative regardless of the other
Variable(s) - so a stronger wind means we feel colder
whether the sun is strong, weak or anything in between.)
26/30

"Memory"Variables
How does sea temperature depend on sunshine?
Clone
Misleading!
Incoming sunshine onto sea ~~~ Temperature of sea ~~~
… but this suggests that the sea temperature plummets
every time the sun goes behind a cloud. (The ~~~ sign
says this Variable repeats continuously across time.) In
fact the sea takes a long time to cool much: to know its
temperature at one moment, we need to know about
sunshine and the temperature in previous moments. A
Variable like this, whose Level depends partly on its
previous level is called a "memory" or "stock" Variable, as
in the next example:
Clone
Try this instead
Incoming sunshine onto sea ~~~ Temperature of sea ~~~
For "memory Variables", the Theorymaker language uses
an arrow from the Variable to itself. Marking it with a
minus-sign and/or colouring it blue says that it naturally
drops from instant to instant. So if this is the story with a
teacher's skill level, maybe we need some top-up
training from time to time to counter that tendency (the
^^^ sign says this Variable repeats discretely across time,
not continuously, for example once a week):
Clone
Teacher skill level ~~~
-
Top-up training ^^^
So this diagram says that the top-up training works in
the opposite direction to the natural decline.
If the Level of a Variable naturally increases from
moment to moment, we can use a + sign instead.
27/30

BreakinguporthodoxTheoriesofChange
Clone
Output: (in
six months)
improved student
classwork
Outcome: (next
year) improved
student exam
resultsActivity:
student
workshop
1 held
Activity:
students given
laptops
Output: (in six
months) improved
student exam
skills
Activity:
student
workshop
2 held
Some Theories of Change follow the kind of rigid layout
familiar from Logical Frameworks, in which the Variables
are divided up into slices: Inputs, Outputs, Outcomes
etc. This is a very powerful simplification: just from a
Variable's slice, you can tell many things about it, in
particular:
But we need our Theories of Change to model real life,
and if you believe real life is as simple as that, you
believe in fake science.
The Theorymaker language encourages you to break up
that orthodoxy and instead has a range of different
symbols to help you make various distinctions.
The first slice is just the Variables we can control
The final slice is just the Variable(s) we really value
·
·
The first slice is just the Variables we can control
The final slice is just the Variable(s) we really value
The slices "happen" one after another
·
·
·
28/30

MoreslidesonmoreTheoryofChangethemescoming
soon!

The diagrams shown in these slides see make use of a more precisely defined visual language for
Theories of Change (and Logframes) called Theorymaker.
I'm Steve Powell, a program evaluator. I made the Theorymaker language and the Theorymaker web
apps to help you with your Theories of Change.
Comments? Send me a tweet @stevepowell99.
AboutTheorymaker

Theories of change: sense and nonsense

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