The aim of this practical is to learn how to construct a machine learning classifier in MATLAB, using Hexagon emotion recognition data from the FemNAT-CD project. Do not put my name on the project.
An essay on any of the following topics using one primary and one secondary source.What were the most significant intellectual and artistic achievements of the ancient Egyptians?
What was the cultural legacy of ancient Mesopotamia to successor civilizations?
How and why did Greek tragic drama evolve from its origins in Dionysian worship to the plays of Sophocles and Euripides?
What were the most important stages in the evolution of Greek sculpture from early classical style of the fifth century BCE to the late Hellenistic style of the third century CE?
What were the mystery religions of the Hellenistic era, how much can we find out about them, and what do they seem to have had in common with early Christianity?
Who were the leading Roman philosophers? Did they contribute anything original to the history of ideas, or were their doctrines totally derived from Greek antecedents?
In what ways did the literature of the Augustan era reflect the political and spiritual values of the Rome of Augustus Caesar?
How and why did early Christianity evolve away from Judaism in the first four centuries after the death of Jesus?
What underlying differences in religious outlook differentiate classical painting from early Christian art? Use specific examples of art works to illustrate your answer.
What were the most important achievements made by Byzantine artists, writers, and scholars from the reign of Justinian to the Great Schism of 1054?
What are the main characteristics of Romanesque architecture, when and where did the style originate, and how did it evolve?
How and where did the Gothic style originate, what stages did it develop through, and what are the characteristic qualities of High Gothic architecture?
Was there a twelfth-century Renaissance?
In what ways did painting in the late Middle Ages differ from medieval art before 1300?
How did church music develop between the ninth century and the fifteenth century?
Who were the leading Italian Renaissance humanists, and what were their most significant contributions to literature and philosophy?
What revolutionary developments took place in Italian art during the late fifteenth and early sixteenth centuries?
Who were the most creative and original composers in the fifteenth and sixteenth centuries, and how did Renaissance music develop during this period?
In what ways were the explorers of the fifteenth and sixteenth centuries influenced by the spirit of the Renaissance, and in what ways did their experiences in turn influence the development of Renaissance thought and literature?
Was there a Scientific Renaissance before 1600?
AGENCY: GEORGIA DEPARTMENT OF FAMILY AND CHILDREN SERVICESDescribe the DFCS. Including:
Services offered
Mission of the agency
Clientele or population servedSOCIAL WORKER: DEIDRA SIMITCH
Describe the social workers job activities and professional roles
Explain what brought the social worker to the field and their work history (MAKE IT UP)
Explain opportunities for advancement and what the social worker does for professional development (MAKE IT UP)
Describe the social workers overall job satisfaction and strategies they use for self-care.Analyze your experience. Specifically,
Discuss your thoughts and feelings related to the agency, clientele, and type of work.
Reflect on why you may have experienced these reactions to the agency, clientele, and type of work.
Explain which social work values and ethics were evident (or not evident) during the interview.
Explain how social and economic justice relates to the services provided or population served by this agency.
Explain how cultural competence was demonstrated (or not demonstrated) within the agency or by the social worker.
Explain how the t distribution is similar to a normal distribution, and how it difference from a normal distribution.
Explain the differences in terms of the null hypothesis between a one-sample t test, a two sample t test, and a matched pairs t test.
Suppose that you are testing the null hypothesis H0: = 100 against Ha: < 100 based on a random sample of nine observations from a normal population. The data from the sample give a mean of 98 and a standard deviation of 3. What is the value of the t statistic?
In Problem 3, either find the exact p-value or determine a lower and upper bound of the p-value for the test statistic.
Identify the correct type of t test to use in the following situations. Choose from a 1-sample, 2-sample, or matched pairs t test.
a) You interview 500 female students and ask each about the average number of minutes they spend using the internet each day.
b) You interview a sample of 250 unmarried male students and 250 unmarried female students and ask each about the average number of minutes they spend using the internet each day.
c) You interview 200 female students in their freshman year and again in their senior year and ask each about the average number of minutes they spend using the Internet each day.
Explain how the degrees of freedom are determined using:
a) a one sample t test
b) a two sample t test
A marketing company believes that younger adults use social media more than adults aged 35 or over. To investigate this, the company is planning to take a random sample of each group and determine the sample means.
a) Determine which type of statistical test is appropriate.
b) Create the appropriate null and alternate hypothesis for this test.
Part 2
A random sample of 25 U.S. adults were asked to estimate the average income of all U.S. households. The estimate mean was $47,000 with a standard deviation of $15,000. Construct a 95% confidence interval for this data.
People claim that women say more words per day than men. Estimates claim that a woman uses roughly 20,000 words per day, while a man uses approximately 7,000. To investigate this, a researcher recorded conversations of male college students over a 5-day period. The results are as follows:
7220 13932 4727 10419 9258 9717 10728 5265 12215 9944 7979 12252 9307 9086 10780 3357
The researcher believes that many use more than 7,000 words per day.
a) State the problem in your own words.
b) Create a plan for testing the researchers claim. Be sure to state the null and alternate hypotheses.
c) Carry out the appropriate hypothesis test. Begin by finding the sample mean and standard deviation. Give the value of the t statistic and give the p-value (or an estimate).
d) Formulate the statistical conclusion in terms of the null hypothesis and a practical conclusion. You may compare the p-value to 0.05 to determine if the null hypothesis should be rejected. When the p-value is less than 0.05, we reject the null hypothesis, otherwise we fail to reject the null.
3. A manufacturer states that the average lifetime of its light bulbs is 3 years. A random sample of 50 bulbs is taken and the average lifetime is found to be 34 months. The population is normal with a standard deviation of 8 months. Should the claim be rejected using the 0.05 as a benchmark for the p-value? Construct the hypothesis test for this using the four-step process. Write a detailed description of the steps: state, plan, solve, and conclude.
4. A marketing research firm wants to test whether the mean rating given by consumers to their favorite beer (denoted by X) differs from some other beer (denoted by Y). The ratings obtained from two different random groups are given below. Perform a t test to determine if there is a significant different in the mean ratings of these two beers. Use SPSS or Excel to perform the calculations. See the website below for instructions.
http://www.excel-easy.com/examples/t-test.html
When explaining the test and its results, write a detailed description of the steps: state, plan, solve, and conclude. Assume unequal variances, and use 0.05 as a benchmark for the p-value. Brand X Brand Y 57 63 61 60 62 60 60 62 60 63 62 59 58 57 57 64 62 62 Create mock scenario and mock data for a matched pairs t test. The data set should include at least 10 pairs of data. Perform a matched pairs t test using the four-step process described above. Use Excel to perform the calculations. See the website below for instructions:
http://www.real-statistics.com/students-t-distribution/paired-sample-t-test/Length: 5 - 7 pages References: Include a minimum of two scholarly peer-reviewed resources.Upload your document and click the Submit to Dropbox button.Due Date
Jan 6, 2019 11:59 PM
External Resource (S): Books and Resources for this Week
1. Statistics in Practice
Moore, D.S., Notz, W.I., & Fligner, M.A. (2015). Statistics in practice. New York, NY: W.H. Freeman.
Read Chapters 20 and 212. Excel-Easy. (2016). erforming 2-sample t tests with Excel.
https://www.excel-easy.com/examples/t-test.html3. Zaiontz, C. (2016). Paired Sample t Test. Real Statistics Using Excel.
http://www.real-statistics.com/students-t-distribution/paired-sample-t-test/4. BUS-7200_Grading_Rubrics
Supplemental (External) Resource
1.Scientific Methods in Business Studies
Take-home exam
In this take-home exam you are given three assignments. Your task is to provide answers to
each of them. Read the instructions for each assignment carefully. Write in a clear and logical
way. Do not exceed word limits. Use references to literature, and list references in relation to
each assignment (the word count of the references is not included in the maximum word
count for each assignment).
1. Exam assignment research design
Below you find the introduction to a scientific article that reports on a study of the use of
external scientists to solve R&D problems in firms. Your task is to analyze the way the
authors formulate their research problem, using Van de Ven (2007). In particular, you can
draw upon chapter 3 in your analysis. Also state an argument whether you approve to the way
the authors formulated their research problem or not. Your discussion should be no longer
than 500 words.2. Exam assignment qualitative methods
Based on the methods literature and your own experience with working with qualitative data
and analysis, discuss the benefits and drawbacks of qualitative data analysis and coding
according to the proposed Gioia method (as for instance discussed in the Gioia, Corley and
Hamilton, 2013, paper). Discuss the following (Maximum 750 words total):
a) What are the main opportunities provided by the Gioia method for coding and
analyzing qualitative data in the social sciences? What are the main benefits of this
approach compared to other means of analyzing and coding qualitative data? Draw on
the Gioia paper but also other methods literature to argue for this approach.
b) What are the major drawbacks of this approach to analyzing and coding qualitative
data? Reference methods literature to argue for your answer.3. Exam assignment quantitative methods
For the quantitative part of the exam, you are tasked with evaluating the methods section
found below. Please remember to take into account the different themes covered in the three
quantitative sessions when evaluating the data and methods, and answering the questions.
1. Comment on the values related to exploring the data, reliability and construct validity, and
the regression analysis. Are they OK and if so why, and are there some values that are more
questionable, and if so explain why? (25 credits) (maximum 500 words)
2. Have we carried out all the necessary tests for a rigorous analysis? Do you miss some tests
that you would add, and if so why and what would they add? (10 credits) (maximum 250
words)
3. Interpret the results vis–vis our hypotheses. (5 credits) (maximum 250 words)
HYPOTHESES
H1: Greater headquarters involvement in the development of an
innovation will positively affect transfer performance effectiveness.
H2: Greater use of formal hierarchical governance tools by
headquarters in the innovation transfer process will negatively affect
transfer performance effectiveness.
H3: Greater use of expatriates from the sending subsidiary to the
receiving subsidiary during the transfer will positively affect transfer
performance effectiveness.
H4: An established relationship between the sending and receiving
subsidiaries will positively affect transfer performance effectiveness.
H5: Relationship building between the sending and receiving subsidiary
will positively affect transfer performance effectiveness.
DATA AND METHODS
The data used in this research was collected between 2002 and 2005 and covers 169 intra-
MNE innovation transfer projects in great detail. Innovations in subsidiaries were identified
through snowball sampling, which is appropriate when the population is difficult to define
and no comprehensive listing exists (Hair et al., 2006). The data can be traced back to 72
6
innovation development projects hosted by 63 subsidiaries belonging to 23 different MNEs
headquartered in the US and Europe. The sending subsidiaries span 14 countries and the
receivers 31 countries.1 Different industries are represented in the sample, for example,
manufacturing, telecommunications, transportation, and the steel industry. The innovation
selection criterion was based on the novelty and specific value to the organization. This
follows the 2005 OECD definition of innovations, that is, the implementation of a new or
significantly improved product (good or service), or process, a new marketing method, or a
new organizational method in business practices, workplace organization or external
relations (p. 47). This selection was done by the innovating/developing subsidiary.
Moreover, the innovations had to have the potential of being transferred and they also had to
have been completed one to 10 years prior to the interview. Sampling innovations that have
transfer potential means that the dataset contains some innovations that have not been subject
to transfer. These innovations are excluded in the present analysis.
One potential sample bias is that it only contains successful innovations, in terms of
having been developed. However, given the question at hand, this bias is almost intrinsic
since the transfer of unsuccessful innovations is highly unlikely and would not add anything
to the MNEs competitive advantage. Successful in this sense does not imply subsequent
market success.
The data was collected through face-to-face interviews on site at the subsidiaries where
the respondent answered a structured questionnaire – an approach similar to surveys with the
advantage of being able to target the respondent in person and knowing exactly who answers
the questionnaire. The respondents had been involved in the innovation development and
were usually R&D managers, project managers, or subsidiary CEOs. In relation the transfer
projects, even if the data was collected at the sending subsidiary, the innovations had been
1 More specifically, the senders are located in: Sweden, Taiwan, Italy, France, UK, US, Germany, Belgium,
Finland, Austria, Czech Republic, Denmark, the Netherlands, and Switzerland.
7
transferred to more than one receiver (on average, the innovations in our sample were
transferred to 2.35 receivers). This allows respondents to compare, for instance, transfer
effectiveness across projects. The questionnaire had been pre-tested in two pilot interviews
and minor changes were made in order to eliminate ambiguous questions and phrasings as
well as to exclude erroneous indicators. By having access to managers with detailed
knowledge of the specific innovations, a deeper understanding could be gained (Denrell,
Arvidsson and Zander, 2004), as well as the possibility to discuss the questions with the
respondents. This approach allows targeting the appropriate respondent and detecting
inconsistencies in the answers during the interview, hence increasing reliability and face
validity of the data.
Measures
The advice of Boyd, Gove and Hitt (2005) was followed and single measure indicators were
avoided. Multiple indicators were used in both the dependent and independent variables. This
approach minimizes measurement error, is parsimonious, and offers a multifaceted
representation of the underlying construct (Hair et al., 2006). Additionally, as recommended
by Cox (1980), seven-point Likert-type scales were used to obtain the data on innovation
transfer in MNEs. Besides the subjective estimations by the respondents, distance measures
using secondary data, patenting, and size were included as control variables. The constructs
were identified in an iterative process where coefficient alphas as well as theoretical issues
were considered (Churchill, 1979; Nunnally, 1978). The constructs were theoretically valid
and empirically verified. Subsequently, factor analysis was used in order to confirm the
constructs discriminant validity.
8
Dependent variable
The dependent variable – knowledge transfer effectiveness – reflects the adoption and use of
the transferred knowledge within the receiving unit. The responses focused on circumstances
related to completeness, ease, and timeliness of the adoption and use and follows previous
recommendations and discussions in the literature (Kostova and Roth, 2002; Leonard-Barton
and Sinha, 1993; Repenning, 2002; Szulanski 1996). Compared to earlier studies,
concentrating on the extent of knowledge flows between firm subsidiaries our method of
depicting transfer performance is the degree of transfer effectiveness in terms of investigating
the actual adoption and use of the innovation at the receiving subsidiary (Ciabuschi et al.,
2011a). This reflects key ideas in the knowledge based view (Grant, 1996) and reflects
utilization of transferred knowledge.
Transfer performance effectiveness is measured as a four-item construct where the
respondents were asked to indicate on a scale from 1 (totally disagree) to 7 (totally agree)
whether: ,
counterpart adopted the innovation very quickly>, and
adopt by this counterpart>. One final item was included in this construct and was measured
on a similar scale from 1 (not at all) to 7 (very high):
has been completed>. The internal construct reliability (cronbachs alpha) was 0.817. These
four items were summed and averaged to form the dependent variable in the following
statistical analysis. The dependent variable is distinct from other variables in the analysis, as
Table 1 shows.
9
Independent variables
The first dimension of headquarters subsidiary-level influence is whether or not they have
been involved in the innovation development of the innovation subject to transfer and build
on and extend the attention-based view (Bouquet, Morrison and Birkinshaw, 2009; Ocasio,
1997). Headquarters involvement in innovation development is captured in a four-item
construct where the respondents were asked to indicate, on a scale from 1 (totally disagree) to
7 (totally agree) whether:
innovation>,
innovation>, , and
MNE HQ has taken important initiatives for developing the innovation>. The four indicators
were summed and averaged in order to form the construct used in the regression analysis.
Internal construct reliability was 0.908.
The use of formal hierarchical governance tools and sanctions by headquarters is
captured by four items and is similar to measures employed by Gates and Egelhoff (1986) and
Tsai (2002). The respondents were asked to indicate, on a scale from 1 (totally disagree) to 7
(totally agree), to what extent:
innovation with the counterpart>, and
any sanctions by HQ with the counterpart (Reversed)>. Moreover, the respondents were
asked to indicate on a scale from 1 (not at all) to 7 (very much) whether the transfer of the
innovation was driven by: and . These
four items were summed and averaged to form the construct. The cronbachs alpha of this
construct is 0.632. The use of subsidiary expatriates is reflected in a two-item construct and
builds on Gupta and Govindarajans (2000) measure, and Galbraiths (1973) integrative
mechanisms. The respondents were asked to indicate, on a scale ranging from 1 (not at all) to
7 (very high):
10
managers was used>. The respondents were also asked to indicate, on a scale from 1 (not at
all) to 7 (very much):
personnel between the developer and the receiver>. The indicators were added and averaged
to form the scale. The cronbachs alpha returned with a value of 0.743.
Established relationships, that is, dyadic transfer experience in the sender-receiver
relationship, is a two-item construct where the respondents indicated to what extent, (besides
the focal innovation discussed during the data collection) on a scale from 1 (not at all) to 7
(very much): and
shared knowledge>. The indicators were summed and averaged in order to form the
construct, which had a coefficient alpha of 0.738. This construct builds on literature
highlighting experiences role in knowledge transfer (Ingram and Baum, 1997).
Finally, relationship building between the sending and receiving subsidiaries during the
innovation transfer was captured using a three-item construct drawing on Ghoshal and
Bartletts (1988) framework concerning socialization mechanisms as well as the indicators
used by Persson (2006). The respondents were asked to indicate on a scale from 1 (not at all)
to 7 (very high) the level of use of: ,
teams, project groups etc.> and
regarding the innovation transfer>. The indicators were summed and averaged. Cronbach
alpha was 0.732.
Control variables
In order to more fully specify the model, a number of control variables were introduced. Age
was included since older subsidiaries are more established in their business networks and
have a tendency to be more autonomous (Forsgren, 1990); they can also exhibit a higher
innovative capability (Cohen and Levinthal, 1990; Foss and Pedersen, 2002). To control for
11
age, the logarithm of the number of years the subsidiary had been operating on the market
was included in the regression equation.
Size measured as the natural logarithm of the number of developing subsidiary
employees is used as a proxy for many subsidiary-related characteristics. Research has shown
that large subsidiaries have greater intra-firm bargaining power (Mudambi and Navarra, 2004)
and size can also affect knowledge transfer even if the knowledge has a low relevance (Yang
et al., 2008). Research has also used size as one indicator for valuable knowledge stock,
which can be of greater overall value for the MNE (Gupta and Govindarajan, 2000).
Basic research is captured with the help of a dummy variable. If the subsidiary
conducted research considered to be core, the variable was coded 1; if the subsidiary did not
conduct any basic research, the observation was coded 0. Knowledge developed by a
subsidiary performing core activities is likely to be more easily adopted, building on
absorptive capacity logic. In order to control whether knowledge sharing activities are
stimulated in the MNE, this was included as a single-item variable. The respondents were
asked to indicate, on a scale of 1 to 7, how important knowledge sharing was in the
performance evaluation made of them. This has been shown to have a positive impact on
knowledge transfer flows in previous studies (Bjrkman et al., 2004). To control for the target
subsidiarys knowledge-receiving ability, we employed a measure capturing unit similarity of
the innovation transfer partners. This is a two-item construct capturing how similar the sender
and receiver are regarding technological and organizational features. The respondents were
asked to indicate, with regard to the receiver, on a scale of 1 (totally disagree) to 4 (neither)
up to 7 (totally agree) whether: and
.2 The indicators were summed
and averaged, thus forming the construct. Internal reliability was 0.738. A dummy variable
2 These items were reverse coded in order to capture the similarities between the subsidiaries involved in the
knowledge transfer.
12
indicating whether the innovation subject to transfer was patented or not was included in the
model. Patenting proxy codification of knowledge and connects to the potential ease with
which the knowledge might be transferred (Tallman and Chacar, 2011). Additionally,
distances and differences between countries in which subsidiaries are located may influence
knowledge transfer. Therefore, we controlled for distances in a number of dimensions. The
geographic distance between the locations was calculated for each transfer project. The
number of kilometers was calculated using MapCrow. This measure was transformed into the
natural log of the distance measure and is consistent with the approach of other studies using
geographic distance (e.g., Hansen and Lovs, 2004). Cultural distance was controlled for by
using Kogut and Singhs 1988 index, expressed as:
4
CDj= {(Iij IiN)2/Vi}/4 ,
i=1
where CD is the cultural distance between the subsidiary host countries, Iij is the score
of the receiving subsidiarys country on the ith dimension, and IiN is the score of the sending
subsidiarys country in this dimension. Vi represents the score variance in the specific
dimension. Institutional distance was measured building on the approach of Gaur et al. (2007)
and Xu, Pan and Beamish (2004). The institutional dimensions found in the Executive
Opinion Survey of the Global Competitiveness Report (2005) were explored and a factor
analysis was conducted (principal component with varimax rotation and Kaiser
normalization). The institutional environment is captured by a seven-item construct that
loaded on a single factor having a coefficient alpha of 0.961. This data was matched to our
data calculating the institutional distance between the host countries of the sending and
receiving subsidiaries. The relative economic differences were captured by estimating
differences in GDP per capita between the host countries of the subsidiaries (Tsang and Yip,
13
2007). Data was obtained through the Total Economy Database (2006). Following Tsang and
Yip (2007), we created a measure for relatively more developed countries in relation to the
other part of the dyad. This measure can be expressed as:
ln(GDPsender)-ln(GDPreciever) if GDPsender GDPreciever and = 0 if GDPsender < GDPreceiver
Further assessment of the data
The use of perceptual measurements can be problematic because of social desirability and
self-assessment bias. This is mitigated by the face-to-face interviews.
All relevant indicators were included in a principal component factor analysis
(principal component with varimax rotation and Kaiser normalization, see Table 1). The
KMO-value returned at 0.638. The Bartletts test of sphericity was at a 0.001 significance
level. In the principal component analysis, six factors were extracted with an eigen value
above 1. The seventh factor returned with an eigen value of 0.837.
Some cross-loadings were observed: the first occurred for the item of headquarters
instruction to share the innovation with the counterpart on the construct of headquarters
participation during the development with a value of 0.436. The second cross-loading relates
to the respondents reporting whether the innovation transfer occurred without any sanctions
from headquarters (reversed) with a value of 0.354 on the construct of headquarters
participation during the development.
To investigate whether there is a correlation between two or more predictor variables
augmenting the estimated R2 of the model, the Variance Inflation Factor (VIF) was calculated,
see Table 2.
14
RESULTS
The paper examines how different organizational mechanisms affect knowledge transfer
effectiveness. The mean values, standard deviations, and correlation matrix for all the
variables are presented in Table 2. In order to estimate the models, Ordinary Least Squares
regressions were used. In Table 3, the results from the multiple regression analysis are
reported.
15
Table 1 Factor analysis with Varimax rotation
Variable
Factor
loading Communality
Factor 1: HEADQUARTERS INVOLVEMENT IN DEVELOPMENT
The MNE HQ has participated closely in developing this innovation 0.913 0.872
The MNE HQ has brought competence of use for the development of this
innovation 0.864 0.814
The MNE HQ has been important through specifying requests 0.899 0.832
The MNE HQ has taken important initiatives for developing the
innovation 0.785 0.678
Eigenvalue 3.790
% Variance 19.950
Factor 2: TRANSFER PERFORMANCE EFFECTIVENESS
The counterpart adopted the innovation very quickly 0.649 0.512
The innovation has been very easy to adopt by this counterpart 0.836 0.720
The performance of the innovation transfer was very satisfactory 0.811 0.692
To what extent the innovation transfer has been completed 0.490 0.240
Eigenvalue 3.154
% Variance 16.600
Factor 3: HEADQUARTERS HIERARCICHAL GOVERNANCE TOOLS
The MNE HQ has formally instructed you to share this innovation with
the counterpart 0.526 0.519
The transfer of the innovation has occurred without any sanctions by HQ
with the counterpart (Reversed) 0.561 0.610
Requirement from HQ 0.486 0.236
HQ evaluation system 0.686 0.575
Eigenvalue 2.350
% Variance 12.367
Factor 4: RELATIONSHIP BUILDING
Temporary training at partner sites 0.790 0.729
Cross-unit teams, project groups etc. 0.806 0.764
Face to face meetings were used in the communication regarding the
innovation transfer 0.761 0.679
Eigenvalue 1.847
% Variance 9.720
Factor 5: ESTABLISHED RELATIONSHIP
They previously cooperated with the receiver 0.867 0.811
They previously had shared knowledge 0.823 0.801
Eigenvalue 1.284
% Variance 6.757
Factor 6: SUBSIDIARY EXPATRIATES
To what extent, with regard to the transfer of the innovation exchange of
managers, was used 0.881 0.837
To what extent the transfer of the innovation was driven by moving
personnel between the developer and the receiver 0.817 0.802
Eigenvalue 1.225
% Variance 6.446
Total variance explained 71.840
16
Table 2 Correlation and descriptive statistics
MEAN S.D. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
1. Transfer performance effectiveness 5.211 1.312 1
2. Age 3.528 0.903 0.036 1
3. Size 5.414 1.590 0.041 0.112 1
4. Basic research 0.555 0.498 0.132 0.402** 0.224** 1
5. Knowledge sharing 4.148 1.774 0.147 0.081 0.272** 0.173* 1
6. Unit similarity 5.557 1.55271 0.465** -0.213** -0.158 -0.060 0.002 1
7. Patent 0.569 0.496 -0.111 -0.102 -0.212** 0.019 -0.240** 0.093 1
8. Physical distance 5.735 3.578 -0.001 -0.008 0.024 -0.035 -0.062 0.016 0.051 1
9. Cultural distance 0.618 0.809 0.015 0.049 -0.148 -0.012 -0.023 0.052 0.106 0.635** 1
10. Institutional distance 0.491 0.572 0.041 -0.114 0.059 -0.025 -0.147 -0.057 -0.070 0.464** 0.379** 1
11. Economic differences 0.052 0.124 0.067 -0.067 0.060 -0.008 0.209** 0.052 -0.007 0.391** 0.270** -0.042 1
12. Headquarters involvement in dev. 2.110 1.580 -0.147 -0.350** -0.149* -0.182* 0.005 0.099 0.024 0.073 0.080 -0.057 0.150 1
13. Headquarters hierarchical tools 2.601 1.569 -0.039 0.056 0.324** 0.228** 0.169 -0.054 -0.180* 0.048 0.123 -0.020 0.150 0.348** 1
14. Subsidiary expatriates 1.893 1.472 -0.390** -0.042 -0.064 -0.137 0.032 -0.264** 0.034 -0.049 -0.037 -0.026 -0.150 0.081 -0.098 1
15. Established relationship 4.777 1.660 0.289** 0.085 0.046 0.007 0.151 0.255** 0.074 -0.110 0.009 -0.202** 0.002 -0.039 0.108 -0.028 1
16. Relationship building 4.025 1.753 -0.004 0.000 -0.016 -0.020 -0.056 -0.037 0.012 -0.138 -0.167* -0.059 -0.185* 0.241** 0.093 0.175* 0.256** 1
VIF value 1.450 - - 1.597 1.629 1.433 1.277 1.310 1.271 1.963 1.585 1.409 1.478 5.528 1.528 1.185 1.336 1.246
Spearmans correlation
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
17
Table 3 Results from the Ordinary Least Squares regression analysis a
Regressor Model
s.e.
Age -0.006 0.145
Size 0.085 0.083
Basic research 0.125 0.250
Knowledge sharing 0.062 0.066
Patent -0.202* 0.236
Unit similarity 0.368*** 0.077
Physical distance -0.019 0.041
Cultural distance -0.010 0.162
Institutional distance 0.071 0.216
Economic differences 0.075 1.020
Headquarters involvement in development -0.002 0.081
Headquarters hierarchical governance tools -0.240** 0.082
Subsidiary expatriates -0.239** 0.077
Established relationship 0.211* 0.072
Relationship building 0.039 0.066
Diagnostics
N 169
R2 0.368
Adj.R2 0.274
F-statistics 3.915***
a Values are standardized parameter estimates
p<0.1, *p<0.05, **p<0.01, ***p<0.001
Using a minimum of 7 credible sources, with at least 4 sources coming from peer-reviewed journals taken from the APUS library, write a research paper about a topic related to your major or intended career, which follows the problem solution strategy.
Format your paper according to the guidelines that are given for your particular curricular division, that is, the curricular division in which the subject for your paper is located. You must choose one of the following documentation styles: APA, MLA, or Chicago.
Length: The research paper should be 9 to 10 pages in length. The page count does not include the Works Cited, Reference, or Bibliography page, or the title page or abstract if required by your documentation style. The page count refers only to the text of the paper itself. You will lose points on your final research project if you go over the 10 page limit by more than 250 words. Part of effective writing is being able to complete the assignment within the designated limits set.
Part 1)
Q. 1 – Match the correct term with the best example of that type of measurement by drawing a direct line, if the line is unclear or illegible no points earned =): (If you need help using MS Word to draw a line, check Youtube)
Nominal Scale Amount of Money in Savings account
Ratio Scale How many times you commute to work
Ordinal Scale Gender, Male, Female, Unidentified
Interval Scale Flavors of Ice Cream
Q2. Write a 7- point Likert Scale Question on how to measure satisfaction with Walmart. Remember to include the degrees.
Q3. Write a 5- point Likert Scale Question to ask students how likely they are to graduate with their degree plan. Remember to include the degrees.
Q4. What is the difference between a Likert Scale and an Interval Scale?
Part 2)
Q5. What is the difference between reliability of measurement versus the validity of construct measurements. (Quoting the text book will not earn any points=).
Six months ago, Acme, Inc. received a patent on a drug that will provide immortality to all. Acmes president has publicly stated he has no plans to market the drug. Beta, Inc. copies the drug and releases it on the market. Beta makes no profit on the sale of this drug and only charges enough to cover its costs in manufacturing.
Address the following:
Identify the intellectual property implications in this scenario.
Discuss how alternative dispute resolution applies.
Identify the ethical dilemma faced by Acme, and the dilemma faced by Beta. Use two theories of ethical thoughts to discuss the recommended course of action by both companies.
Discuss the concept of limited liability and how it may influence the type of business entity a person chooses when forming a business. Let’s evaluate Trump University. You can read more about it here: https://www.washingtonpost.com/politics/source-trump-nearing-settlement-in-trump-university-fraud-cases/2016/11/18/8dc047c0-ada0-11e6-a31b-4b6397e625d0_story.html?utm_term=.14219feea7b3
In this situation, the president chose to pay a settlement rather than litigate the case. What is the ethical dilemma here? Which ethical framework did the president use when arriving at his decision to settle?
In a 2 page essay in Times New Roman or similar font, double-spaced, with 1-inch margins.1.Compare and contrast the tasks of trial and appellate courts.
2.Analyze the different responsibilities and workloads of US magistrate judges, district judges, circuit judges, and Supreme Court justices.
3.Outline the jurisdiction and function of Article III courts.
4.Outline the jurisdiction and functions of Article I courts.