Statistical Analysis and Design of Questionnaires and Data

This assignment provides an overview of statistical methods and questionnaire design, detailing the process of data collection, summarization, and analysis. It covers various data types, including primary and secondary data, and explores statistical modeling techniques such as multiple regression and factor analysis. The assignment outlines the steps involved in conducting factor analysis and interpreting the results to identify strengths, weaknesses, opportunities, and threats. It also discusses deductive and inductive approaches in research and includes a practical example of regression analysis applied to coffee sales data. The analysis involves using Excel to derive a regression equation and predict future sales. Finally, it compares the suitability of different data types for analysis, emphasizing their application in forecasting and assessing relationships between variables. The assignment concludes with a list of relevant references.

Introduction to statistics and questionnaire design:
Statistical methods basically a process of collection, summarizing, analysis and the
interpretation of the analysis. (Bethlehem, 2009)
Making questionnaire on the basis of the importance of factors of the study of the
organization. The characteristics of the study will contain, a specific plan, design
structure to get the answers from the respondents. The questionnaire will contain the
questions related to the open ended, closed ended, and the nominal, ordinal and interval
level ratio variables. The analysis of the collected data from the questionnaire will
indicate the strength, weakness, opportunities and threats of the factors of the study. The
statistical data will indicate a summary statistics of the analysis, which will contain the
graphical representation of each factor, numerical summary of each factor and the final
principal components of the study. (Brace, 2008).
The procedure of the analysis can be derived as follows:
a. Topic selection: - Scale should not be wide are level of measurement should be
accurate.
b. Determination of hypothesis: - it includes the objective of the study.
c. Sampling method: - Selecting an appropriate sampling method related to the study.
d. Data collection: - Data should be collected through direct interview or by other similar
companies’ data.
e. Data handling: - Coding and putting the responses in to level of measurements.
f. Statistical Analysis: -It includes the appropriate statistical model for the analysis.
i. Gathering of results: - It includes the graphical and numerical representation of the
data.
j. Conclusions: - Determine the findings related to the hypothesis of the study. (Heeringa,
West and Berglund, 2010)
Data types:
There are two types of data which can be used for analysis, first is Primary data, which
can directly collected from the customers of the organization on the basis of
questionnaire. And other is Secondary data, which can collected from official website of
the organization and also from other official websites related to organization. (Bordens,
Abbott, 2013)
Statistical methods and modeling:
Statistical methods basically a process of collection, summarizing, analysis and the
interpretation of the analysis. Data collection is a process of collection of data related to
the information required in the questionnaire. In this process, the sampling process
involved and required to calculate the representative sample size of the population. Thus
a sample is a representative of the population will indicates the unbiased results of the
study, also the method of data collection will unbiased. Data summarization is a process
of calculating appropriate statistics of the data, and summarizing the data by using
graphs, tables and charts. Thus, a researcher can visualize the results on the basis of
graphical representation of the factors of the study and can summarize the results on the
basis of summary statistics. (Reid, 2013).
Analysis of data contains an appropriate method for the study, we can use different
statistical methods for a data on the basis of the purpose of the study. Consider the
multiple regression model for factor analysis and the future prediction:
1 1 2 2 3 3i i i i im m i iX A F A F A F A F VU     
Where
Xi = ith standardized variable
Aij = standardized multiple regression coefficient of variable I on common factor j
F= common factor
Vi = standardized regression coefficient of variable I on unique factor I
Ui = the unique factor for variable i
m = number of common factors
The model for the common factors which are uncorrelated can be defined as the linear
combination:
Fi = Wi1x1 + Wi2X2 + Wi3X3 + … + WikXk
Where,
Fi = estimate of i th factor
Wi = weight or factor score coefficient
k = number of variables.

The steps of directing factor analysis are as below:
Problem formulation.
↓
Obtain the correlation matrix.
↓
Find the appropriate model of factor analysis.
↓
Compute the number of factors.
↓
Rotate the factors.
↓
Interpret the factors Variables
↓
Calculate the Select the
Factor scores Surrogate
.
↓
Determine the model fit
Thus, by using factor analysis, a researcher can predict the factors which shows the
strength, weakness, opportunities and threats of the study. The strength will indicate the
effective factors, the weakness will indicate the weak factors of the study, the
opportunities will indicate that which factors have to improve and the direction of the
improvement, threats will indicates the causes which can trouble in the future. (Brown,
2015)
Interpretation of the analysis will indicate the areas where the organization have to
improve, and which intimidations can trouble the organization. By, using the analysis an
organization can improve market strategy and can make future plans related to the
factors. (Pahl and Richter, 2009)
Deductive and inductive approaches:
The deductive approach is basically related to making the hypothesis on the basis of
theoretical information and making the research strategy. The inductive approach is
basically based on the observational study based on the research process. Thus, a
researcher can use the deductive and inductive approaches on the basis of the research.
Thus, both approaches can be used in business intelligence which will depend on the
hypothetical/ Observational study. (Wilson, 2010)
Analysis of the samples:
Consider the data of sales of coffee from year 2005 to 2009, to predict the sales of coffee
in the quarter 20 of the year 2010, we can use regression analysis. The regression model
for the analysis of data is given below:
 Sales of coffee Quarter
  
Now use the Excel to run the regression analysis, the obtained regression analysis is
given as below:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.5602852
R Square 0.3139195
Adjusted R
Square
0.2758039
Standard Error 13.154284
Observations 20
ANOVA
df SS MS F Significance
F
Regression 1 1425.1161 1425.116 8.2359 0.0101870
Residual 18 3114.6338 173.0352
Total 19 4539.75
Coefficients Standard
Error
t Stat P-value Lower 95%

Intercept 21.878 6.1105 3.580 0.00213 9.04109695
Quarter 1.46390 0.5101 2.8698 0.01018 0.39222672
According to above results, the prediction equation for the sales of coffee is given as:
 
 
Sales of coffee Quarter
21.87 1.46 Q
  
 
Thus, the predicted sales for the year 2010 quarter 21 (In model we have considered all
the quarters in ascending ordered) will be:
 
 
Sales of coffee Quarter
21.87 1.46 21
52.62 in million
  
 

Hence, by using the regression analysis the sales of coffee in the year 2010 of quarter 1
will be about 52.62 in million £.
Compare the suitability of one type of data with other:
Consider the data of sales of coffee from year 2005 to 2009, in this data the sales of
coffee for the year 2005 to 2010 is given, so this data can be used for analysis of
forecasting of the sales of coffee in the next years.
Consider the data of the weight observed corresponding to the source compound and the
extraction method. This data can be used whether there is a difference between the mean
weight observed corresponding to the source compound and the difference between the
mean weight observed corresponding to the extraction method. Also we can check
whether there is an interaction effect in weight observed between the extraction method
source compounds.

References
Bethlehem,J. (2009) Applied Survey Methods: A Statistical Perspective. John Wiley &
Sons.
Bordens,K. and Abbott,B.B. (2013) |Research Design and Methods A Process Approach.
McGraw-Hill Higher Education.
Brace,I. (2008) Questionnaire Design: How to Plan, Structure and Write Survey Material
for Effective Market Research. Kogan Page Publishers.
Brown,T.A. (2015) Confirmatory Factor Analysis for Applied Research. Guilford
Publications.
Heeringa,S.G. West,B.T. and Berglund,P.A. (2010) |Applied Survey Data Analysis. CRC
Press.
Pahl,N. and Richter,A. (2009) |Swot Analysis:Idea, Methodology and a Practical
Approach. GRIN Verlag.
Reid,H.M. (2013) Introduction to Statistics: Fundamental Concepts and Procedures of
Data Analysis. Sage Publications.
Wilson,J. (2010) Essentials of Business Research: A Guide to Doing Your Research
Project. Sage Publications.