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Course: PEBL 101: RESEARCH METHODOLOGY & DESIGN FOR BUSINESS > Section: UNIT 6: MEASUREMENT IN BUSINESS RESEARCH

Unit 6 Learning Outcomes

Indicative Content

Introduction, The process of operationalising variables, Scales of measurement

Intended Learning Outcomes for Unit 6:

By the end of Unit 6, you will be able to;

1. operationalise/ break down abstract concepts/variables into observable behaviour
2. incorporate appropriate scales of measurements while designing data collection instruments or tools

Unit 6 E-tivities

Learning Outcome(s) addressed: Unit 6 supports Module Learning Outcomes 2, 3, 4 & 5.

Purpose: Each of you will use your prior knowledge and/or experience on formulating questionnaires and collecting data. You will be able to reduce abstract variables into observable behavior ready to collect data. By the end of this unit, you will be able to define measurement, conceptualise and oprationalise variables and apply the various scales of measurement and the frequently used scales.

Over to you: Whenever you look at a questionnaire or interview guide, you find question framed differently. Some require ‘yes’ or ‘no’ answers, while others require you to tick the extent to which you agree or disagree with statements given. Other will take on different forms. This show that there are different scales of measurement used when getting or soliciting or collecting data. In this Unit, we are going to master the ideas that make measurement of abstract concepts/variables possible. As you embark on the preparatory research, first read the e-tivities and know what is required of you.

Preparatory research: As you learn about measurement in business research;

1. Read minilecture about measurement available here.
2. Read minilecture about scales of measurement here.
3. Read about the process of operationalizing abstract variables on pages 174 – 184 of Research Methods for Business by Sekaran (2003) Research Methods for Business; John Wiley & Sons Inc. available online here .
4. Read about scales of measurement on pages 185 – 192 of Research Methods for Business by Sekaran (2003) Research Methods for Business; John Wiley & Sons Inc. available online here.

E-tivity 6.1: This e-tivity challenges you to express your knowledge of the process of operationalizing abstract variables. Make a summary of 200 words about the process of operationalizing abstract variables and save it to your portfolio by 3^rd April 2019.

E-tivity 6.2: Using the sample questionnaire available here, identify the different measurement scale used. Write the question number and the type of scale use. For example you can say that Question 15 – Likert Scale. Put the summary in your portfolio by 5^th April 2019.

E-tivity 6.3: In the group discussion forum, create another new discussion topic in your group forum titled ‘E-tivity 6.3’. Under it, make adjustments to the questionnaire for the concept paper that you had designed under E-tivity 5.4. Group members should discuss and agree on the most suitable adjustments. Select a weaver for this section who should summarise the final solution for E-tivity 6.3 and post a new adjusted questionnaire for ‘Concept Paper’ that you created under E-tivity 2.1 by 7^th April 2019.

Link to other Units:  Unit 6 links to Units 1, 2, 5, 7 & 8.

Anticipated time required for Unit 6 activities: 4 hours

E-tivity 6.1: Summary about the process of operationalizing abstract variables: 3^rd April 2019.

E-tivity 6.2: Identifying different measurement scale used: 5^th April 2019.

Introduction to measurement

What is measurement?

It refers to the process of assigning values to objects of observation. It is the process of quantifying or scoring a subject’s performance or response. It is also a standardized process of assigning numbers or other symbols to certain characteristics of objects of interests according to pre-specified rules.

You must distinguish measuring objects or properties such as weight, revenues, expenditures, wages, etc or counting numbers of objectives and measuring abstract concepts such as preferences, feelings, opinions, perceptions, attitudes and intelligence levels.

Reasons for Measuring Attitudes

1. Attitudes lead to behavior
2. More feasible to ask questions on attitudes than to observe and interpret behavior
3. Large capacity for diagnosis and explanation
4. Learn which features of a new product concept are acceptable or unacceptable
5. Measure the perceived strengths and weaknesses of competitive alternatives

Why is measurement important in business research?

Certain human physiological phenomena such as blood pressure, pulse rates and body temperature as well as certain physical attributes such as height and weight, lend themselves to measurement through the use of appropriate measuring instruments. However, when we get into the realm of people’s subjective feelings, attitudes and perceptions, the measurement of these factors or variables becomes difficult.

There is lack of objective physical measuring devises to measure such subjective feelings, attitudes and perceptions. One technique is to reduce the abstract concepts to observable behaviour and characteristics. In other words, the abstract notions are broken down into observable characteristic behaviours. Eg, the concept of thirst is abstract; we cannot see thirst. However, we would expect a thirsty person to drink fluids. If several people say they are thirsty, then we can measure thirst levels of each of these individuals by measuring the quantity of fluids that they drink to quench their thirst.We will therefore be able to measure their levels of thirst, even though the concept of thirst itself is abstract.

Reduction of abstract concepts so that they can be measured in a tangible way is called operationalising the concepts. The concepts to be operationalised usually form the variables of a particular research.

Variable

It is any characteristic of the population that is capable of taking on different values. It is any characteristic of a person, phenomenon, subject that can take on different values

Variables can be independent, Dependent, intervening, moderating/control and Extraneous/background variables. These can be presented diagrammatically in a conceptual framework. (Already covered)

The Process Of Operationalising Abstract Concepts (Variables)

Operationalising or operationally defining a concept to render it measurable is done by looking at the behavioural dimensions, facets, indicators or properties denoted by the concept. These are then translated into observable and measurable elements so as to form an index of measurement of the concept. This involves breaking down the concept or variable into its constituent dimensions or indicators that are more or less observable and are present in the literature. Operationalising involves the following steps;

1.Identify the concept (variable) to be operationalised. Eg Achievement motivation

2.Identify the elements or behavioural dimensions or facets or characteristics that we would hope to find in people or elements with that attribute. Eg, a person with achievement motivation would follow five typical broad characteristics or dimensions like;

a)driven by work

b)find it hard to relax

c)prefer to work alone

d)engage in challenging but not too challenging jobs,

e)seek feedback

3.Examining each of the five dimensions and break it further into its elements. Although breaking the concept into these five dimensions has somewhat reduced its level of abstraction, we have still not operationalised the concept into measurable elements of behaviour. We therefore have to break it further. For example breaking down dimension one (driven by work) into its elements would be as follows.

Ideally a person driven by work will

a)be constantly working (count the number of hours he/she engages in work-related activities whether at work or at home)

b)be reluctant to take time off from work (count how often they take breaks and or socialise)

c)persevere even if there are some setbacks (keep track of how frequently people continue to persevere doing their job despite failures)

All the other dimensions ( the remaining four) would be broken down into their operational indicators.

4. Having operationalised the concept (eg achievement motivation above), by reducing its level of abstraction to observable behaviours, it is possible to develop a good measure to tap the concept. Its usefulness is that others could use the same measure thus ensuring replicability.

It should be noted that any operational definition is likely to

a)exclude some of the important dimensions and elements arising from failure to recognise or conceptualise them.

b)Include certain irrelevant features mistakenly thought to be relevant.

Operationally defining the concept, nevertheless, is the best way to measure it. However, physically observing and counting the number of times individuals behave in particular ways, even if practical, would be too laborious and time consuming. So instead of doing that, we could request them to report their own behaviour patterns by asking them appropriate questions which they can respond to on some scale of measurement that we provide.

Scales of Measurement

Before a researcher collects data on a concept, he/she has to design the questions or statements that the respondents will answer or respond to in order to know the behaviour of element of the variable being measured. Different behaviours need different questions/ scales to measure them.

Scaling is the process of creating a continuum on which objects are located according to the amount of the measured characteristic that the object possesses.

The term scaling refers to procedures for attempting to determine quantitative measures of subjective and sometimes abstract concepts.

It is defined as a procedure for the assignment of numbers to a property of objects in order to impart some of the characteristics of numbers to the properties in question.

A scale is a tool or mechanism by which individuals are distinguished on how they differ from one another on the variables of interest to our study.

 Data collected about variables may be classified by the scale of measurement. Measurement scales are used to categorize and/or quantify variables.

Properties of Measurement Scales

Each scale of measurement satisfies one or more of the following properties of measurement.

a)       Identity. Each value on the measurement scale has a unique meaning.

b)       Magnitude. Values on the measurement scale have an ordered relationship to one another. That is, some values are larger and some are smaller.

c)       Equal intervals. Scale units along the scale are equal to one another. This means, for example, that the difference between 1 and 2 would be equal to the difference between 19 and 20.

d)       A minimum value of zero. The scale has a true zero point, below which no values exist.

There are 4 scales of measurement namely; -

 1.Nominal scale

2.Ordinal scale

3.Interval scale

4.Ratio scale

1.Nominal scale

A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure.

This is one that allows the researcher to assign subjects to certain categories or groups. For example with respect to the variable of gender, respondents can be grouped into two categories ie male and female. These two groups can be assigned code numbers 1 and 2. These numbers serve as simple and convenient category labels with no intrinsic value other than to assign respondents to one of two non-overlapping or mutually exclusive categories. Nominal scales categorise individuals or objects into mutually exclusive and collectively exhaustive groups. The categorisation of gender into male and female is collectively exhaustive in that there is no third category into which respondents would normally fall.

The number of observations from each category may be added to form a frequency distribution.

For example, if we had interviewed 200 people and assigned code number 1 to all male respondents and number 2 to all female respondents, then computer analysis of the data at the end of the survey may show that 98 of the respondents are men and 102 are women. This frequency distribution tells us that 49% of the survey’s respondents are men and 51% women.

Other than this marginal information, such scaling tells us nothing more about the two groups. It only give some basic categorical information. The numbers assigned have no numericalmeaning; they are just merely for identification. They cannot be meaningfully subtracted, added or multiplied; they have no arithmetic origin, no order and no distance relationship.

2.Ordinal scale

An ordinal scale is next up the list in terms of power of measurement. It not only categorises the variables in such a way as to show differences among the various categories, it also rank-orders the categories in some meaningful way.

The ordinal scale has the property of both identity and magnitude. Each value on the ordinal scale has a unique meaning, and it has an ordered relationship to every other value on the scale.

With any variable for which the categories are to be ordered according to some preference, the ordinal scale would be used.

An example of an ordinal scale in action would be the results of a horse race, reported as "win", "place", and "show". We know the rank order in which horses finished the race. The horse that won finished ahead of the horse that placed, and the horse that placed finished ahead of the horse that showed. However, we cannot tell from this ordinal scale whether it was a close race or whether the winning horse won by a mile.

Data are arranged in preferential order e.g. shortest to tallest, first to last, most effective to least effective and numbered say from 1 to say 5. Any numerical assignment given to the responses must maintain the same preferential order although the assignment itself is random.

For example, respondents may be asked to indicate their preferences by ranking the importance they attach to five distinct characteristics in a job that the researcher might be interested in studying. (Not satisfactory – 1 to very satisfactory – 5)

Because of this random assignment the real numerical difference between categories is not meaningful and cannot be interpreted. Ordinal scale does not give the magnitude of differences between ranks. The difference between good and excellent may be more or less than the difference between Poor and very poor.

3.Interval Scale

The interval scale of measurement has the properties of identity, magnitude, and equal intervals.

An interval scale allows us to perform certain arithmetical operations on the data collected from the respondents. Whereas the nominal scale allows us to only to qualitatively distinguish groups by categorising them into mutually exclusive and collectively exhaustive sets, and the ordinal scale torank-order the preferences, the interval scale allows us to measure the distance between any two points on the scale. This helps us to compute the means and standard deviations of the responses on the variables.

For instance a Likert scale (3 point or 5 point) can be used where responses are categorised from strongly agree (5) to strongly disagree (1). The magnitude of difference represented by the space between 1 and 2 is the same as that between 4 and 5.

The clinical thermometer is a good example of an interval-scaled instrument; it has an arbitrary origin and the magnitude of the difference between 98.6 degrees (supposed to be the normal body temperature) and 99.6 degrees is the same as the magnitude of the difference between 104 and 105 degrees. Note however that one may not be seriously concerned if one’s temperature rises from 98.6 to 99.6 but is likely to be so when the temperature goes from 104 to 105 degrees.

However interval scale doesn’t have the capacity to measure absolute lack of the characteristics. They have no common origin and they are usually based on an arbitrary zero e.g. what is the starting salary of for or B.com graduate?

The interval scale taps the differences, the order, and the equality of the magnitude of the differences in the variable.

4.Ratio Scale

The ratio scale of measurement satisfies all four of the properties of measurement: identity, magnitude, equal intervals, and a minimum value of zero.

The weight of an object would be an example of a ratio scale. Each value on the weight scale has a unique meaning, weights can be rank ordered, units along the weight scale are equal to one another, and the scale has a minimum value of zero.

Weight scales have a minimum value of zero because objects at rest can be weightless, but they cannot have negative weight.

The ratio scale overcomes the deficiency of the arbitrary origin point of the interval scale in that it has an absolute zero point which is a meaningful measuring point.

Ratio scales are usually used in organisational research when exact numbers on objective factors are called for as in the following questions;

a)How many other organisations did you work for before joining this system?_____

b)How many retail outlets do you operate?_____

This is the highest level, has a uniform starting point. It has a true zero. It has all the characteristics of the interval data but in addition it has a uniform reference or starting point e.g. a true zero.

The best way to contrast interval and ratio scales is to look at temperature. The Centigrade scale has a zero point but it is an arbitrary one. The Farenheit scale has its equivalent point at -32^o. (Physicists would probably argue that Absolute Zero is the zero point for temperature but this is a theoretical concept.) So, even though temperture looks as if it would be a ratio scale it is an interval scale. Currently, we cannot talk about no temperature - and this would be needed if it were a ration scale.

Scaling Techniques Frequently Used

These are the methods of scaling aimed at getting the attitudinal responses of subjects, events or persons. They give the difference formats available to one when asking questions or seeking information. They can be applied on different scale types (nominal, ordinal, interval or ratio). There are two main categories of attitudinal scales;

1. Rating scales

2. Ranking scales

1. Rating scales

They are measurement scales that allow a respondent to register the degree (or amount) of a characteristic or attribute possessed byan object directly on the scale.

A rating scale is a set of categories designed to elicit information about a quantitative or a qualitative attribute. Rating scales are used widely online in an attempt to provide indications of consumer opinions of products. Rating scales are used quite frequently in survey research and there are many different kinds of rating scales. A typical rating scale asks subjects to choose one response category from several. Either each response category is labeled or else only the two endpoints of the scale are "anchored." Generally, rating scales should be balanced, with an equal number of favorable and unfavorable response choices.

A rating scale is a tool used for assessing the performance of tasks, skill levels, procedures, processes, qualities, quantities, or end products, such as reports, drawings, and computer programs. These are judged at a defined level within a stated range.  Rating scales list performance statements and the range of accomplishment in descriptive words.

These form “the scale” and can indicate a range of achievement, such as from poor to excellent, never to always, beginning to exemplary , or strongly disagree to strongly agree.

Some tasks, such as procedures and processes, need to be observed in order to be assessed.

Characteristics of rating scales

Rating scales should:

1. have criteria for success based on expected outcomes
2. have clearly defined, detailed statements. This gives more reliable results. For assessing end products, it can sometimes help to have a set of photographs  or real samples that show the different levels of achievement. Students can  visually compare their work to the standards provided.
3. have statements that are chunked into logical sections or flow sequentially
4. include clear wording with numbers when a number scale is used As an example, when the performance statement describes a behaviour or quality, 1 = poor through to 5 = excellent is better than 1 = lowest through to  5 = highest or simply 1 through 5. The range of numbers should be the same for all rows within a section (such as all being from 1 to 5). The range of numbers should always increase or always decrease. For example, if the last number is the highest achievement in one section, the last number should be the highest achievement in the other sections.
5. have specific, clearly distinguishable terms Using good then excellent is better than good
6. then very good because it is hard to distinguish between good and very good. Some terms, such as often or sometimes, are less clear than numbers, such as 80% of the time.
7. be short enough to be practical
8. highlight critical tasks or skills
9. indicate levels of success required before proceeding further, if applicable
10. sometimes have a column or space for providing additional feedback
11. have space for other information such as the student’s name, date, course, examiner, and overall result
12. be reviewed by other instructors

The following rating scales are some of the often used in organisational research;

a)Dichotomous scale

b)Category scale

c)Likert scale

d)Semantic differential scale

e) Numerical scales

The different scaling techniques are discussed below in detail;

a) Dichotomous scale:

A dichotomous scale is one with only two categories. It is generated for dichotomous questions which are questions that have two possible responses eg Yes or No, True or False, Agree or Disagree, dead or alive, pregnant or not pregnant, etc. Dichotomous data are data from outcomes that can be divided into two categories where each participant must be in one or other category, and cannot be in both.

Some examples

Gender -                 Male                    Female

Race -                      Black                 White

Agreement -            Yes                     No

T/F -                       True                     False

Value -                    High                   Low

A dichotomous variable is one that takes on one of only two possible values when observed or measured. The value is most often a representation for a measured variable or an attribute (e.g., gender: male/female).

- it is used to elicit a yes or no answer, as in the example below;

Note that a nominal scale is used to elicit the response.

b)Category scale:-

It is a rating scale which the response options provided for a closed-ended question are labeled with specific descriptions.

It uses multiple items to elicit a single response as per the following example;

Where in Kampala do you live?

Note that a nominal scale is used to elicit the response.

Characteristics

a)      Response options are still verbal descriptions.

b)      Response categories are usually ordered according to a particular descriptive or evaluative dimension.

c) Likert scale

A multiple item rating scale in which the degree of an attribute possessed by an object is determined by asking respondents to agree or disagree with a series of positive and/or negative statements describing the object.

It is one in which a statement is used and the participant decides how strongly they agree or disagree with the statements. For example the participant decides whether they strongly agree/ agree/ undecided/ disagree/ strongly disagree.

A Likert scale question is designed to help you understand how your respondents perceive a situation, person, item, job, activity, your brand, products or services by asking them to rank something on a gradual scale with specific points. For example, a Likert scale question will ask customers to rate their checkout experience as: very efficient, somewhat efficient, neither efficient nor inefficient, somewhat inefficient or very inefficient.

It is designed to examine how strongly subjects agree or disagree with statements on a 3, 4 or 5 point scale with the following anchors;

My property is properly valued before tax is levied.(to those not in formal employment)

Strongly Agree 1 | Agree 2 | Not Sure 3 | Disagree 4 | Strongly Disagree 5

The responses over a number of items tapping a particular concept or variable are then summed for every respondent. The interval scale is used here, the difference in the responses between any two points on the scale remaining the same.

Advantages of Likert scales

a)      A strength of Likert scales is that they can give us an idea about how strongly a participant feels about something.This therefore gives more detail than a simple yes no answer.

b)      A further strength is that we can treat the data as quantitative data which is easier to analyse statistically.

Disadvantages of Likert Scales

a)      A weakness in measuring attitudes is that we usually only measure how people think and not how they actually behave.

b)      There is a tendency for people to respond towards the middle of the scale perhaps to make them look less extreme.

c)      As with any questionnaire participants may provide the answers that they feel they should and importantly as the data is quantitative it does not provide in depth replies.

The following procedure is used to analyze data from Likert scales:

1.First, weights are assigned to the responses options, e.g. Totally agree=1,  Agree=2, etc

2. Then negatively-worded statements are reverse-coded (or reverse scored). E.g. a score of 2 for a negatively-worded statement with a 5-point response options is equivalent to a score of 4 on an equivalent positive statement.

d)Semantic differential scale

Several attributes are identified at the extremes of the scale and respondents are asked to indicate their attitudes on what may be called a semantic space towards a particular individual, object, or event.

Sometimes descriptive phrases are used instead of bipolar adjectives,especially when it is difficult to get adjectives that are exact opposites.

The bipolar (on two poles) adjectives used for instance would denotes such terms as Good -Bad, Strong – Weak, Hot -cold.

The semantic differential scale is used to assess respondents’ attitudes toward a particular brand, advertisement, object or individual. The responses can be plotted to obtain a good idea of the perceptions of the respondents. An example is as follows;

Responsive- - - - - - - - - - - - - Unresponsive

Beautiful - - - - - - - - - - - - - Ugly

Good - - - - - - - - - - - - - Bad

This is treated as an interval scale.

e)Numerical scale

A rating scale in which numbers rather than semantics pace or verbal descriptions are used as response options.

It is similar to the semantic differential scale, with the difference that numbers on a 5-point or 7-point scale are provided, with bipolar adjectives at both ends as shown below;

How pleased are you with your new estate agent?

Extremely Extremely

Pleased 7 6 5 4 3 2 1 Displeased

2.Ranking Scales

They make comparisons between or among objects, events, or persons  and elicit  the preferred choices and ranking among them. They are used to tap preferences between tow or more objects or items. However, they may not give definitive clues to some of the answers sought.

a)Paired Comparison scale

This is used when, among a small number of objects, respondents are asked to choose between two objects at a time. This helps to process preferences. If, during a paired comparison respondents consistently show preference for product one over products two, three and four, that would provide the manager a good indication about which product line is worth pursuing with vigour.

Paired-Comparison Scales

The brands to be rated are presented two at a time, so each brand in the category was compared once to every other brand

Advantage

Performs well on the criteria

 Limitations

a)      Cumbersome to administer

b)      Frame of reference is always the other brand being tested; these brands may change over time

b)Forced/Fixe choice

This offers respondents the opportunity to rank objects relative to one another, among the alternatives provided. This is easier for the respondents to do, particularly if the number of choices to be ranked is limited in number.

For example

Rank your preferences among the following magazines, which you would like to subscribe to, 1 being the most preferred choice and 5 being the least preferred.

Fortune-

Playboy-

Time-

People-

Prevention-

This type of scale is easy to measure and quantify. It also forces a participant to not choose a middle option.

However respondents may not feel that their desired response is available and of course the answers are not in-depth.

c)Comparative scale

This provides a benchmark or a point of reference to assess attitudes towards the current object, event, or situation under study. Eg

In a volatile financial environment, how wise or useful is it to invest in Treasury bills compared to stocks? Please Circle the appropriate response.

More Useful About the Same Less Useful

 1 2 3 4 5

THE CHALLENGE OF CHOOSING AN ATTITUDINAL SCALE

Choosing An Attitudinal Scale

There is difficulty or a problem in choosing a suitable scale. This could be that either because there are many different techniques, each with its own strengths and weaknesses or virtually any technique can be adapted to the measurement of any one of the attitude components.

However, a researcher’s choice shaped by:

1. Whether to use single or index measure.
2. Whether to use a ranking, sorting, choice, or rating scale.
3. Whether to use category labels or not.
4. If the decision is to use category labels, what labels to use.
5. Number of response options (scale categories) to use, i.e. whether to use 2, 3, 4, 5, etc responsecategories. In general, the larger the number of categories, the more sensitive the scale is; but also the more difficult it is for respondents to answer.
6. Whether to use balanced or unbalanced scale. A balanced scale has an equal number of points to the left and right of a mid-point while an nunbalanced scale has more response options on one side than the other.
7. Whether the scale should force choice among the response categories, i.e. whether the scale should contain a ‘neutral’ or ‘don’t know’ category.
8. The specific information required
9. Adabtability of the scale to the data collection method and budget constraints
10. Compatibility of the scale with the structure of the respondent’s attitude

Sample Questionnaire

Computer self efficacy.

Section B: Computer anxiety.

9. I do not fear using computers.

Strongly Agree | Agree | Not Sure | Disagree | Strongly Disagree
1 | 2 | 3 | 4 | 5

10. I don’t feel embarrassed when performing tasks using a computer.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

11. Use of computers to perform my tasks does not disappoint me.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section C: Computer playfulness

12. I can easily interact with and use computers to record financial transactions.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

13.  When I use computer systems, I do not feel intimidated and stressed.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section D: Effort expectancy

14. I use computer systems with ease.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

15. When I use computer systems to record financial transactions, I expect better results or outcomes.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section E: Users attitude

16. I have a positive attitude towards use of computerized systems.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section F: Conceptualization of computer knowledge

17.  Am computer literate and familiar with use of software packages to perform my daily tasks.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section G: Computerized accounting systems

18. What kind of software do you use in your department?          IFMS                 Quick books

      Pastel          None of the above

19. The software am using gives me the best results.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Adoption of Computerized accounting systems

Section I: Automating the accounting transactions

20. The financial records of the Electoral Commission can easily be accessed by management and other external users since they are all computerized.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section J: Perceived usefulness of computerized systems

21. I believe that use of computerized accounting systems improves my work performance.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

22. I find it easy to record and present financial data using computerized accounting systems.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section K: Methods used to record financial information

23. All cash books in the Electoral Commission are computer-based.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

24. All the ledgers in the Electoral Commission are computerized.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

28. The bin cards in the stores department are computerized.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

26. All invoices of the Electoral Commission are computer-based.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

Section L: The relationship between computer self efficiency and adoption of computerized accounting systems.

27. Computer self efficacy affects the level of adoption of computerized accounting systems in Government organizations.

Strongly Agree | Agree | Not sure | Disagree | Strongly Disagree

28. Which other factors do you think affect the level of adoption of computerized accounting systems in Government organizations apart from computer self efficacy?

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

19. Suggest possible measures that can be used by the Electoral Commission to improve the level of adoption of computerized accounting systems.

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

Thank you

God Bless You.

SOURCES OF ERRORS IN MEASUREMENT

1) Errors that originate from the instrument used in measurement. It may be defective or not sufficiently precise.

2) The person doing the measurement. The measurer may be inexperienced. He/she may misinterpret records.

3) Errors from the respondent misunderstanding the questions or deliberately giving false answers

The qualities of some measurements include

i. Validity

ii. Reliability

iii. Practicability

Steps in developing a reliable tool for measurement

i. Develop the concept to be measured

ii. Specify the dimension of measures

iii. Select the indicators of measurement

iv. Construct an index to the used in measurement